diff --git a/CHANGELOG.md b/CHANGELOG.md
index d6927f20bd..c98b9b1720 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,11 +1,11 @@
 # [Unreleased](https://github.com/pybamm-team/PyBaMM/)
 
 ## Features
-
 - The parameter "Ambient temperature [K]" can now be given as a function of position `(y,z)` and time `t`. The "edge" and "current collector" heat transfer coefficient parameters can also depend on `(y,z)` ([#3257](https://github.com/pybamm-team/PyBaMM/pull/3257))
 - Spherical and cylindrical shell domains can now be solved with any boundary conditions ([#3237](https://github.com/pybamm-team/PyBaMM/pull/3237))
 - Processed variables now get the spatial variables automatically, allowing plotting of more generic models ([#3234](https://github.com/pybamm-team/PyBaMM/pull/3234))
 - Numpy functions now work with PyBaMM symbols (e.g. `np.exp(pybamm.Symbol("a"))` returns `pybamm.Exp(pybamm.Symbol("a"))`). This means that parameter functions can be specified using numpy functions instead of pybamm functions. Additionally, combining numpy arrays with pybamm objects now works (the numpy array is converted to a pybamm array) ([#3205](https://github.com/pybamm-team/PyBaMM/pull/3205))
+- Implement the MSMR model ([#3116](https://github.com/pybamm-team/PyBaMM/pull/3116))
 
 ## Bug fixes
 
diff --git a/docs/source/api/models/lithium_ion/electrode_soh.rst b/docs/source/api/models/lithium_ion/electrode_soh.rst
index 8942b2394e..4bf7d57dbe 100644
--- a/docs/source/api/models/lithium_ion/electrode_soh.rst
+++ b/docs/source/api/models/lithium_ion/electrode_soh.rst
@@ -8,4 +8,8 @@ Electrode SOH models
 
 .. autofunction:: pybamm.lithium_ion.get_min_max_stoichiometries
 
+.. autofunction:: pybamm.lithium_ion.get_initial_ocps
+
+.. autofunction:: pybamm.lithium_ion.get_min_max_ocps
+
 .. footbibliography::
diff --git a/docs/source/api/models/lithium_ion/index.rst b/docs/source/api/models/lithium_ion/index.rst
index f925d2c3d4..1a72c3c662 100644
--- a/docs/source/api/models/lithium_ion/index.rst
+++ b/docs/source/api/models/lithium_ion/index.rst
@@ -9,5 +9,6 @@ Lithium-ion Models
   mpm
   dfn
   newman_tobias
+  msmr
   yang2017
   electrode_soh
diff --git a/docs/source/api/models/lithium_ion/msmr.rst b/docs/source/api/models/lithium_ion/msmr.rst
new file mode 100644
index 0000000000..89ac143e2e
--- /dev/null
+++ b/docs/source/api/models/lithium_ion/msmr.rst
@@ -0,0 +1,7 @@
+Multi-Species Multi-Reaction (MSMR) Model
+=========================================
+
+.. autoclass:: pybamm.lithium_ion.MSMR
+    :members:
+
+.. footbibliography::
diff --git a/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst b/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst
index abf878e57b..522418a42f 100644
--- a/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst
+++ b/docs/source/api/models/submodels/interface/kinetics/butler_volmer.rst
@@ -1,5 +1,5 @@
-Butler Volumer
-==============
+Butler Volmer
+=============
 
 .. autoclass:: pybamm.kinetics.SymmetricButlerVolmer
     :members:
diff --git a/docs/source/api/models/submodels/interface/kinetics/index.rst b/docs/source/api/models/submodels/interface/kinetics/index.rst
index 8def3d7fc8..efb8be4d30 100644
--- a/docs/source/api/models/submodels/interface/kinetics/index.rst
+++ b/docs/source/api/models/submodels/interface/kinetics/index.rst
@@ -10,5 +10,6 @@ Kinetics
   marcus
   no_reaction
   tafel
+  msmr_butler_volmer
   total_main_kinetics
   inverse_kinetics/index
diff --git a/docs/source/api/models/submodels/interface/kinetics/msmr_butler_volmer.rst b/docs/source/api/models/submodels/interface/kinetics/msmr_butler_volmer.rst
new file mode 100644
index 0000000000..18bea7ee7a
--- /dev/null
+++ b/docs/source/api/models/submodels/interface/kinetics/msmr_butler_volmer.rst
@@ -0,0 +1,5 @@
+MSMR Butler Volmer
+==================
+
+.. autoclass:: pybamm.kinetics.MSMRButlerVolmer
+    :members:
diff --git a/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst b/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst
index 132e5b88a9..fc664adf2b 100644
--- a/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst
+++ b/docs/source/api/models/submodels/interface/open_circuit_potential/index.rst
@@ -6,3 +6,4 @@ Open-circuit potential models
   base_ocp
   current_sigmoid_ocp
   single_ocp
+  msmr_ocp
diff --git a/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst b/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst
new file mode 100644
index 0000000000..f2106367d2
--- /dev/null
+++ b/docs/source/api/models/submodels/interface/open_circuit_potential/msmr_ocp.rst
@@ -0,0 +1,8 @@
+MSMR Open Circuit Potential
+===========================
+
+
+.. autoclass:: pybamm.open_circuit_potential.MSMROpenCircuitPotential
+    :members:
+
+.. footbibliography::
diff --git a/docs/source/api/models/submodels/particle/index.rst b/docs/source/api/models/submodels/particle/index.rst
index ae020ac3fa..b17a7502e4 100644
--- a/docs/source/api/models/submodels/particle/index.rst
+++ b/docs/source/api/models/submodels/particle/index.rst
@@ -8,3 +8,4 @@ Particle
   fickian_diffusion
   polynomial_profile
   x_averaged_polynomial_profile
+  msmr_diffusion
diff --git a/docs/source/api/models/submodels/particle/msmr_diffusion.rst b/docs/source/api/models/submodels/particle/msmr_diffusion.rst
new file mode 100644
index 0000000000..af7dfe2582
--- /dev/null
+++ b/docs/source/api/models/submodels/particle/msmr_diffusion.rst
@@ -0,0 +1,7 @@
+MSMR Diffusion
+==============
+
+.. autoclass:: pybamm.particle.MSMRDiffusion
+    :members:
+
+.. footbibliography::
diff --git a/docs/source/examples/index.rst b/docs/source/examples/index.rst
index 4287e28927..4bab430032 100644
--- a/docs/source/examples/index.rst
+++ b/docs/source/examples/index.rst
@@ -59,6 +59,7 @@ The notebooks are organised into subfolders, and can be viewed in the galleries
     notebooks/models/lead-acid.ipynb
     notebooks/models/lithium-plating.ipynb
     notebooks/models/MPM.ipynb
+    notebooks/models/MSMR.ipynb
     notebooks/models/pouch-cell-model.ipynb
     notebooks/models/rate-capability.ipynb
     notebooks/models/SEI-on-cracks.ipynb
diff --git a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb
index 54101815af..ac34142fab 100644
--- a/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb
+++ b/docs/source/examples/notebooks/creating_models/6-a-simple-SEI-model.ipynb
@@ -199,6 +199,7 @@
     "V_hat = pybamm.Parameter(\"Partial molar volume [m3.mol-1]\")\n",
     "c_inf = pybamm.Parameter(\"Bulk electrolyte solvent concentration [mol.m-3]\")\n",
     "\n",
+    "\n",
     "def D(cc):\n",
     "    return pybamm.FunctionParameter(\"Diffusivity [m2.s-1]\", {\"Solvent concentration [mol.m-3]\": cc})"
    ]
@@ -485,9 +486,11 @@
     "    {\"SEI layer\": {xi: {\"min\": pybamm.Scalar(0), \"max\": pybamm.Scalar(1)}}}\n",
     ")\n",
     "\n",
+    "\n",
     "def Diffusivity(cc):\n",
     "    return cc * 10**(-12)\n",
     "\n",
+    "\n",
     "# parameter values (not physically based, for example only!)\n",
     "param = pybamm.ParameterValues(\n",
     "    {\n",
@@ -565,6 +568,7 @@
     "L_0_eval = param.evaluate(L_0)\n",
     "xi = np.linspace(0, 1, 100) # dimensionless space\n",
     "\n",
+    "\n",
     "def plot(t):\n",
     "    _, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,5))\n",
     "    ax1.plot(solution.t, L_out(solution.t) * 1e6)\n",
@@ -581,6 +585,7 @@
     "    plt.tight_layout()\n",
     "    plt.show()\n",
     "    \n",
+    "\n",
     "import ipywidgets as widgets\n",
     "widgets.interact(plot, t=widgets.FloatSlider(min=0,max=solution.t[-1],step=0.1,value=0));"
    ]
diff --git a/docs/source/examples/notebooks/models/DFN.ipynb b/docs/source/examples/notebooks/models/DFN.ipynb
index a4ecfdb427..25b79ec260 100644
--- a/docs/source/examples/notebooks/models/DFN.ipynb
+++ b/docs/source/examples/notebooks/models/DFN.ipynb
@@ -62,22 +62,22 @@
     "\n",
     "#### Current:\n",
     "$$\n",
-    "i_{\\text{e,n}}\\big|_{x=0} = 0, \\quad i_{\\text{e,p}}\\big|_{x=1}=0,  \\\\\n",
+    "i_{\\text{e,n}}\\big|_{x=0} = 0, \\quad i_{\\text{e,p}}\\big|_{x=L}=0,  \\\\\n",
     "\\phi_{\\text{e,n}}\\big|_{x=L_{\\text{n}}} = \\phi_{\\text{e,s}}\\big|_{x=L_{\\text{n}}}, \\quad i_{\\text{e,n}}\\big|_{x=L_{\\text{n}}} = i_{\\text{e,s}}\\big\\vert_{x=L_{\\text{n}}} = I, \\\\ \n",
-    "\\phi_{\\text{e,s}}\\big|_{x=1-L_{\\text{p}}} = \\phi_{\\text{e,p}}\\big|_{x=1-L_{\\text{p}}}, \\quad \n",
-    "    i_{\\text{e,s}}\\big|_{x=1-L_{\\text{p}}} = i_{\\text{e,p}}\\big|_{x=1-L_{\\text{p}}} = I.\n",
+    "\\phi_{\\text{e,s}}\\big|_{x=L-L_{\\text{p}}} = \\phi_{\\text{e,p}}\\big|_{x=L-L_{\\text{p}}}, \\quad \n",
+    "    i_{\\text{e,s}}\\big|_{x=L-L_{\\text{p}}} = i_{\\text{e,p}}\\big|_{x=L-L_{\\text{p}}} = I.\n",
     "$$\n",
     "\n",
     "#### Concentration in the electrolyte:\n",
     "$$\n",
-    "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=1}=0,\\\\ \n",
+    "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=L}=0,\\\\ \n",
     "c_{\\text{e,n}}\\big|_{x=L_{\\text{n}}} = c_{\\text{e,s}}|_{x=L_{\\text{n}}}, \\quad N_{\\text{e,n}}\\big|_{x=L_{\\text{n}}}=N_{\\text{e,s}}\\big|_{x=L_{\\text{n}}}, \\\\\n",
-    "c_{\\text{e,s}}|_{x=1-L_{\\text{p}}}=c_{\\text{e,p}}|_{x=1-L_{\\text{p}}}, \\quad N_{\\text{e,s}}\\big|_{x=1-L_{\\text{p}}}=N_{\\text{e,p}}\\big|_{x=1-L_{\\text{p}}}.\n",
+    "c_{\\text{e,s}}|_{x=L-L_{\\text{p}}}=c_{\\text{e,p}}|_{x=L-L_{\\text{p}}}, \\quad N_{\\text{e,s}}\\big|_{x=L-L_{\\text{p}}}=N_{\\text{e,p}}\\big|_{x=L-L_{\\text{p}}}.\n",
     "$$\n",
     "\n",
     "####  Concentration in the electrode active material:\n",
     "$$\n",
-    "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \\frac{j_{\\text{k}}}{F}, \\quad \\text{k} \\in \\text{n, p}.\n",
+    "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ N_{\\text{s,k}}\\big|_{r_{\\text{k}}=R_{\\text{k}}} = \\frac{j_{\\text{k}}}{F}, \\quad \\text{k} \\in \\text{n, p}.\n",
     "$$\n",
     "\n",
     "#### Reference potential:\n",
@@ -87,8 +87,8 @@
     "#### And the initial conditions:\n",
     "\n",
     "$$\n",
-    "c_{\\text{s,k}}(x,r,0) = c_{\\text{s,k,0}}, \\quad \\phi_{\\text{s,n}}(x,0) = 0, \\quad \\phi_{\\text{s,p}}(x,0) = \\phi_{\\text{s,p,0}}, \\\\ \\text{k} \\in \\text{n, p},\\\\\n",
-    "\\phi_{\\text{e,k}}(x,0) = \\phi_{\\text{e,0}}, \\quad c_{\\text{e,k}}(x,0) = 1, \\\\ \\text{k} \\in \\text{n, s, p}. \n",
+    "c_{\\text{s,k}}(x,r,0) = c_{\\text{s,k,0}}, \\quad \\text{k} \\in \\text{n, p},\\\\\n",
+    "c_{\\text{e,k}}(x,0) = c_{\\text{e,0}}, \\quad \\text{k} \\in \\text{n, s, p}. \n",
     "$$\n"
    ]
   },
@@ -269,7 +269,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pybamm",
+   "display_name": "dev",
    "language": "python",
    "name": "python3"
   },
@@ -287,7 +287,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c"
+    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
    }
   }
  },
diff --git a/docs/source/examples/notebooks/models/MSMR.ipynb b/docs/source/examples/notebooks/models/MSMR.ipynb
new file mode 100644
index 0000000000..7413339f5b
--- /dev/null
+++ b/docs/source/examples/notebooks/models/MSMR.ipynb
@@ -0,0 +1,566 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Multi-Species Multi-Reaction model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zsh:1: no matches found: pybamm[plot,cite]\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "import pybamm\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Equations\n",
+    "\n",
+    "Here we briefly outline the models used for the open-circuit potential, kinetics, and solid phase transport used in the MSMR model, as described in Baker and Verbrugge (2018). The remaining physics is modelled differently depending on which options are selected. By default, the rest of the battery model is as described in Maquis et al. (2019). In the following we give equations for a single electrode."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Thermodynamics\n",
+    "The MSMR model is developed by assuming that all electrochemical reactions at the electrode/electrolyte interface in a lithium insertion cell can be expressed in the form \n",
+    "$$ \\text{Li}^{+} + \\text{e}^{-} + \\text{H}_{j} \\rightleftharpoons  (\\text{Li--H})_{j}.$$\n",
+    "For each species $j$, a vacant host site $\\text{H}_{j}$ can accommodate one lithium leading to a filled host site $(\\text{Li--H})_{j}$. The OCV for this reaction is written as\n",
+    "$$ U_j = U_j^0 + \\frac{\\omega_j}{f}\\log\\left(\\frac{X_j - x_j}{x_j}\\right),$$\n",
+    "where $f = (RT)/F$, and $R$, $T$, and $F$ are the universal gas constant, temperature in Kelvin, and Faraday’s constant, respectively. Here $X_j$ represents the total fraction of available host sites which can be occupied by species $j$, $x_j$ is the fraction of filled sites occupied by species $j$, $U_j^0$ is a concentration independent standard electrode potential, and the $\\omega_j$ is an unitless parameter that describes the level of disorder of the reaction represented by gallery $j$. \n",
+    "\n",
+    "The equation for each reaction can be inverted to give \n",
+    "$$x_j = \\frac{X_j}{1+\\exp[f(U-U_j^0)/\\omega_j]}.$$\n",
+    "The overall electrode state of charge is given by summing the fractional occupancies \n",
+    "$$x = \\sum_j x_j = \\sum_j \\frac{X_j}{1+\\exp[f(U-U_j^0)/\\omega_j]},$$\n",
+    "which is an explicit closed form expression for the inverse of the OCV. This is opposite to many battery models where one typically gives the OCV as an explicit function of the state of charge (or stoichiometry).\n",
+    "\n",
+    "At a particle interface with the electrolyte, local equilibrium requires that \n",
+    "$$U_j = U(x) \\quad \\forall j.$$"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kinetics\n",
+    "The kinetics of the insertion reaction are given as\n",
+    "$$i_j = i_{0,j}[e^{(1-\\alpha_j)f\\eta} - e^{-\\alpha_jf\\eta}], \\qquad i = \\sum_j i_j,$$\n",
+    "where $i_j$ is the interfacial current associated with reaction $j$, $\\alpha_j$ is the symmetry factor, $\\eta$ is the overpotential, given by \n",
+    "$$ \\eta = \\phi_s - \\phi_e - U(x),$$\n",
+    "where $\\phi_s$ and $\\phi_e$ are the solid phase and electrolyte potentials, respectively, and $i_{0,j}$ is the exchange current density of reaction $j$, given by\n",
+    "$$i_{0,j} = i_{0,j}^{ref}(x_j)^{\\omega_j\\alpha_j}(X_j-x_j)^{\\omega_j(1-\\alpha_j)}(c_e/c_e^{ref})^{1-\\alpha_j},$$\n",
+    "where $c_e$ is the electrolyte concentration."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Solid phase transport\n",
+    "Within the MSMR framework, the flux within the particles is expressed in terms of gradient of the chemical potential\n",
+    "$$N = -c_{\\text{T}}x\\frac{D}{RT}\\nabla \\mu + x(N+N_{\\text{H}}),$$\n",
+    "where $N$ is the flux of lithiated sites, $N_{\\text{H}}$ is the flux of unlithiated sites, $c_{\\text{T}}$ is the total concentration of lithiated and delithiated sites, and $D$ is a diffusion coefficient. Ignoring volumetric expansion during lithiation, the total flux of sites vanishes\n",
+    "$$N+N_{\\text{H}}.$$ \n",
+    "It can then be shown that \n",
+    "$$N = c_{\\text{T}}fDx(1-x)\\frac{\\text{d}U}{\\text{d}x}\\nabla x.$$\n",
+    "\n",
+    "A mass balance in the solid phase then gives\n",
+    "$$\\frac{\\partial x}{\\partial t} = -\\nabla\\cdot\\left(x(1-x)fD\\frac{\\text{d}U}{\\text{d}x}\\nabla x\\right),$$\n",
+    "which, for a radially symmetric spherical particle, must be solved subject to the boundary conditions\n",
+    "$$N\\big\\vert_{r=0} = 0, \\quad N\\big\\vert_{r=R} = \\frac{i}{F},$$\n",
+    "where $R$ is the particle radius. This must be supplemented with a suitable initial condition for the electrode state of charge.\n",
+    "\n",
+    "Solution of this problem requires evaluate of the function $U(x)$ and the derivative $\\text{d}U/\\text{d}x$, but these functions cannot be explicitly integrated. This problem can be avoided by replacing the dependent variable $x$ with a new dependent variable $U$ subject to the transformation \n",
+    "$$x = \\sum_j \\frac{X_j}{1+\\exp[f(U-U_j^0)/\\omega_j]}.$$\n",
+    "This gives the following equation for mass balance within the particles\n",
+    "$$\\frac{\\text{d}U}{\\text{d}x}\\frac{\\partial U}{\\partial t} = -\\nabla\\cdot\\left(x(1-x)fD\\nabla x\\right),$$\n",
+    "\n",
+    "which must be solved along with the transformed boundary and initial conditions."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Parameterization of the MSMR model\n",
+    "The behaviour of MSMR model is characterised by the parameters $X_j$, $U^0_j$, $\\omega_j$, $\\alpha_j$, and $i_{0,j}^{ref}$. Let's take a look at their values in the example parameter set provided in PyBaMM. The thermodynamic parameter values are taken from Verbrugge et al. (2017) and correspond to a graphite negative electrode and NMC positive electrode. The remaining value are based on a parameterization of the LG M50 cell, from Chen et al. (2020).\n",
+    "\n",
+    "We first load in the MSMR model and specify the number of reactions in each electrode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pybamm.lithium_ion.MSMR({\"number of MSMR reactions\": (\"6\", \"4\")})"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we can inspect the parameter values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "negative electrode:\n",
+      "X_n_0 = 0.43336, U0_n_0 = 0.08843, w_n_0 = 0.08611, a_n_0 = 0.5 j0_ref_n_0 = 2.7\n",
+      "X_n_1 = 0.23963, U0_n_1 = 0.12799, w_n_1 = 0.08009, a_n_1 = 0.5 j0_ref_n_1 = 2.7\n",
+      "X_n_2 = 0.15018, U0_n_2 = 0.14331, w_n_2 = 0.72469, a_n_2 = 0.5 j0_ref_n_2 = 2.7\n",
+      "X_n_3 = 0.05462, U0_n_3 = 0.16984, w_n_3 = 2.53277, a_n_3 = 0.5 j0_ref_n_3 = 2.7\n",
+      "X_n_4 = 0.06744, U0_n_4 = 0.21446, w_n_4 = 0.0947, a_n_4 = 0.5 j0_ref_n_4 = 2.7\n",
+      "X_n_5 = 0.05476, U0_n_5 = 0.36325, w_n_5 = 5.97354, a_n_5 = 0.5 j0_ref_n_5 = 2.7\n",
+      "positive electrode:\n",
+      "X_p_0 = 0.13442, U0_p_0 = 3.62274, w_p_0 = 0.9671, a_p_0 = 0.5 j0_ref_p_0 = 5\n",
+      "X_p_1 = 0.3246, U0_p_1 = 3.72645, w_p_1 = 1.39712, a_p_1 = 0.5 j0_ref_p_1 = 5\n",
+      "X_p_2 = 0.21118, U0_p_2 = 3.90575, w_p_2 = 3.505, a_p_2 = 0.5 j0_ref_p_2 = 5\n",
+      "X_p_3 = 0.3298, U0_p_3 = 4.22955, w_p_3 = 5.52757, a_p_3 = 1 j0_ref_p_3 = 1000000.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "parameter_values = model.default_parameter_values\n",
+    "\n",
+    "# Loop over domains\n",
+    "for domain in [\"negative\", \"positive\"]:\n",
+    "    print(f\"{domain} electrode:\")\n",
+    "    d = domain[0]\n",
+    "    # Loop over reactions\n",
+    "    N = int(parameter_values[\"Number of reactions in \" + domain + \" electrode\"])\n",
+    "    for i in range(N):\n",
+    "        print(\n",
+    "            f\"X_{d}_{i} = {parameter_values[f'X_{d}_{i}']}, \"\n",
+    "            f\"U0_{d}_{i} = {parameter_values[f'U0_{d}_{i}']}, \"\n",
+    "            f\"w_{d}_{i} = {parameter_values[f'w_{d}_{i}']}, \"\n",
+    "            f\"a_{d}_{i} = {parameter_values[f'a_{d}_{i}']} \"\n",
+    "            f\"j0_ref_{d}_{i} = {parameter_values[f'j0_ref_{d}_{i}']}\"\n",
+    "        )"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the functional form of the open-circuit potential $U$, fractional occupancies $x_j$, and exchange current densities $i_{0,j}$ as a function of stoichiometry $x$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# get symbolic parameters\n",
+    "param = model.param\n",
+    "param_n = param.n.prim\n",
+    "param_p = param.p.prim\n",
+    "\n",
+    "# set up ranges for plotting\n",
+    "U_n = pybamm.linspace(0.05, 1.1, 1000)\n",
+    "U_p = pybamm.linspace(2.8, 4.4, 1000)\n",
+    "\n",
+    "# get reference electrolyte concentration and temperature\n",
+    "c_e = param.c_e_init\n",
+    "T = param.T_init\n",
+    "\n",
+    "# set up figure\n",
+    "fig, ax = plt.subplots(3, 2, figsize=(10, 10))\n",
+    "colors = [\"r\", \"g\", \"b\", \"c\", \"m\", \"y\"]\n",
+    "\n",
+    "# sto vs potential\n",
+    "x_n = param_n.x(U_n)\n",
+    "x_p = param_p.x(U_p)\n",
+    "ax[0, 0].plot(parameter_values.evaluate(x_n), parameter_values.evaluate(U_n), \"k-\")\n",
+    "ax[0, 1].plot(parameter_values.evaluate(x_p), parameter_values.evaluate(U_p), \"k-\")\n",
+    "ax[0, 0].set_xlabel(\"x_n\")\n",
+    "ax[0, 0].set_ylabel(\"U_n [V]\")\n",
+    "ax[0, 1].set_xlabel(\"x_p\")\n",
+    "ax[0, 1].set_ylabel(\"U_p [V]\")\n",
+    "\n",
+    "# fractional occupancy vs potential\n",
+    "for i in range(6):\n",
+    "    xj = param_n.x_j(U_n, i)\n",
+    "    ax[1, 0].plot(\n",
+    "        parameter_values.evaluate(x_n),\n",
+    "        parameter_values.evaluate(xj),\n",
+    "        color=colors[i],\n",
+    "        label=f\"x_n_{i}\",\n",
+    "    )\n",
+    "ax[1, 0].set_xlabel(\"x_n\")\n",
+    "ax[1, 0].set_ylabel(\"x_n_j\")\n",
+    "ax[1, 0].legend()\n",
+    "for i in range(4):\n",
+    "    xj = param_p.x_j(U_p, i)\n",
+    "    ax[1, 1].plot(\n",
+    "        parameter_values.evaluate(x_p),\n",
+    "        parameter_values.evaluate(xj),\n",
+    "        color=colors[i],\n",
+    "        label=f\"x_p_{i}\",\n",
+    "    )\n",
+    "ax[1, 1].set_xlabel(\"x_p\")\n",
+    "ax[1, 1].set_ylabel(\"x_p_j\")\n",
+    "ax[1, 1].legend()\n",
+    "\n",
+    "# exchange current density vs potential\n",
+    "for i in range(6):\n",
+    "    xj = param_n.x_j(U_n, i)\n",
+    "    j0 = param_n.j0_j(c_e, U_n, T, i)\n",
+    "    ax[2, 0].plot(\n",
+    "        parameter_values.evaluate(x_n),\n",
+    "        parameter_values.evaluate(j0),\n",
+    "        color=colors[i],\n",
+    "        label=f\"j0_n_{i}\",\n",
+    "    )\n",
+    "ax[2, 0].set_xlabel(\"x_n\")\n",
+    "ax[2, 0].set_ylabel(\"j0_n_j [A.m-2]\")\n",
+    "ax[2, 0].legend()\n",
+    "for i in range(4):\n",
+    "    xj = param_p.x_j(U_p, i)\n",
+    "    j0 = param_p.j0_j(c_e, U_p, T, i)\n",
+    "    ax[2, 1].plot(\n",
+    "        parameter_values.evaluate(x_p),\n",
+    "        parameter_values.evaluate(j0),\n",
+    "        color=colors[i],\n",
+    "        label=f\"j0_p_{i}\",\n",
+    "    )\n",
+    "ax[2, 1].set_ylim([0, 0.5])\n",
+    "ax[2, 1].set_xlabel(\"x_p\")\n",
+    "ax[2, 1].set_ylabel(\"j0_p_j [A.m-2]\")\n",
+    "ax[2, 1].legend()\n",
+    "\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Example solving MSMR using PyBaMM\n",
+    "Below we show how to set up and solve a CCCV experiment using the MSMR model in PyBaMM. We already created the model in the previous section, so we can go ahead and define our experiment, before creating and solving a simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "At t = 275.026 and h = 2.68649e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n",
+      "At t = 275.028 and h = 4.19765e-11, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.solvers.solution.Solution at 0x2853c7d00>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "experiment = pybamm.Experiment(\n",
+    "    [\n",
+    "        (\n",
+    "            \"Discharge at 1C for 1 hour or until 3 V\",\n",
+    "            \"Rest for 1 hour\",\n",
+    "            \"Charge at C/3 until 4.2 V\",\n",
+    "            \"Hold at 4.2 V until 10 mA\",\n",
+    "            \"Rest for 1 hour\",\n",
+    "        ),\n",
+    "    ],\n",
+    "    period=\"10 seconds\",\n",
+    ")\n",
+    "sim = pybamm.Simulation(model, experiment=experiment)\n",
+    "sim.solve()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we can plot the results. In the MSMR model we can look at both the potential and stoichiometry as a function of position through the electrode and within the particle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "67da37f9dcb64ac696aca8772d5ffce7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106530343899824, step=0.06106530343899824)…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x285cd5640>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim.plot(\n",
+    "    [\n",
+    "        \"Negative particle stoichiometry\",\n",
+    "        \"Positive particle stoichiometry\",\n",
+    "        \"X-averaged negative electrode open-circuit potential [V]\",\n",
+    "        \"X-averaged positive electrode open-circuit potential [V]\",        \n",
+    "        \"Negative particle potential [V]\",\n",
+    "        \"Positive particle potential [V]\",\n",
+    "        \"Current [A]\",\n",
+    "        \"Voltage [V]\",\n",
+    "    ],\n",
+    "    variable_limits=\"tight\",  # make axes tight to plot at each timestep\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can also look at the individual reactions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0b056c49819644d6848340ae609978f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=6.106530343899824, step=0.06106530343899824)…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x288076940>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xns = [f\"Average x_n_{i}\" for i in range(6)]  # negative electrode reactions: x_n_0, x_n_1, ..., x_n_5\n",
+    "xps = [f\"Average x_p_{i}\" for i in range(4)]  # positive electrode reactions: x_p_0, x_p_1, ..., x_p_3\n",
+    "sim.plot(\n",
+    "    [\n",
+    "        xns,\n",
+    "        xps,\n",
+    "        \"Current [A]\",\n",
+    "        \"Negative electrode stoichiometry\",\n",
+    "        \"Positive electrode stoichiometry\",\n",
+    "        \"Voltage [V]\",\n",
+    "    ]\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and plot how they sum to give the electrode stoichiometry "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x28c7d24f0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sol = sim.solution\n",
+    "time = sol[\"Time [h]\"].data\n",
+    "fig, ax = plt.subplots(1, 2, figsize=(8, 4))\n",
+    "\n",
+    "ax[0].plot(time, sol[\"Average negative particle stoichiometry\"].data, \"k-\", label=\"x_n\")\n",
+    "bottom = 0\n",
+    "for xn in xns:\n",
+    "    top = bottom + sol[xn].data\n",
+    "    ax[0].fill_between(time, bottom, top, label=xn[-4:])\n",
+    "    bottom = top\n",
+    "ax[0].set_xlabel(\"Time [h]\")\n",
+    "ax[0].set_ylabel(\"x_n [-]\")\n",
+    "ax[0].legend(\n",
+    "    loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3\n",
+    ")\n",
+    "ax[1].plot(time, sol[\"Average positive particle stoichiometry\"].data, \"k-\", label=\"x_p\")\n",
+    "bottom = 0\n",
+    "for xp in xps:\n",
+    "    top = bottom + sol[xp].data\n",
+    "    ax[1].fill_between(time, bottom, top, label=xp[-4:])\n",
+    "    bottom = top\n",
+    "ax[1].set_xlabel(\"Time [h]\")\n",
+    "ax[1].set_ylabel(\"x_p [-]\")\n",
+    "ax[1].legend(\n",
+    "    loc=\"upper center\", bbox_to_anchor=(0.5, -0.15), ncol=3\n",
+    ")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Daniel R Baker and Mark W Verbrugge. Multi-species, multi-reaction model for porous intercalation electrodes: part i. model formulation and a perturbation solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium manganese oxide electrode. Journal of The Electrochemical Society, 165(16):A3952, 2018.\n",
+      "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n",
+      "[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n",
+      "[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[6] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n",
+      "[7] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "[8] Mark Verbrugge, Daniel Baker, Brian Koch, Xingcheng Xiao, and Wentian Gu. Thermodynamic model for substitutional materials: application to lithiated graphite, spinel manganese oxide, iron phosphate, and layered nickel-manganese-cobalt oxide. Journal of The Electrochemical Society, 164(11):E3243, 2017.\n",
+      "[9] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
+      "[10] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/examples/notebooks/models/SPMe.ipynb b/docs/source/examples/notebooks/models/SPMe.ipynb
index 978951220b..1548caa623 100644
--- a/docs/source/examples/notebooks/models/SPMe.ipynb
+++ b/docs/source/examples/notebooks/models/SPMe.ipynb
@@ -25,7 +25,7 @@
     "\n",
     "ii) At the centre of each particle the standard no-flux condition is imposed, and the flux on the surface of the particle is simply the current $I$ divided by the thickness of the electrode $L_{\\text{k}}$, as in the SPM. Since lithium is transferred between the electrolyte and particles, the flux through the particle surface also enters the electrolyte diffusion equation as a source/sink term. There is no transfer of lithium between the electrolyte and current collectors, which leads to no flux boundary conditions on the lithium concentration in the electrolyte $c_{\\text{e,k}}$ at either end of the cell. \n",
     "\n",
-    "iii) We must also impose initial conditions which correspond to setting an initial concentration in each particle $c_{\\text{s,k}}(t=0) = c_{\\text{s,k,0}}$, and to having no deviation from the initial (uniform) lithium concentration in the electrolyte $c_{\\text{e,k}}(t=0) = 0$.  \n",
+    "iii) We must also impose initial conditions which correspond to setting an initial concentration in each particle $c_{\\text{s,k}}(t=0) = c_{\\text{s,k,0}}$, and to having no deviation from the initial (uniform) lithium concentration in the electrolyte $c_{\\text{e,k}}(t=0) =  c_{\\text{e,0}}$.  \n",
     "\n",
     "\n",
     "The model equations for the SPMe read: \n",
@@ -33,20 +33,18 @@
     "\n",
     "#### Particles: \n",
     "$$\n",
-    "\\mathcal{C}_{\\text{k}} \\frac{\\partial c_{\\text{s,k}}}{\\partial t} = -\\frac{1}{r_{\\text{k}}^2} \\frac{\\partial}{\\partial r_{\\text{k}}} \\left(r_{\\text{k}}^2 N_{\\text{s,k}}\\right), \\\\\n",
+    "\\frac{\\partial c_{\\text{s,k}}}{\\partial t} = -\\frac{1}{r_{\\text{k}}^2} \\frac{\\partial}{\\partial r_{\\text{k}}} \\left(r_{\\text{k}}^2 N_{\\text{s,k}}\\right), \\\\\n",
     "N_{\\text{s,k}} = -D_{\\text{s,k}}(c_{\\text{s,k}}) \\frac{\\partial c_{\\text{s,k}}}{\\partial r_{\\text{k}}}, \\quad \\text{k} \\in \\text{n, p},\n",
     "$$\n",
     "\n",
     "$$\n",
-    "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - \\frac{a_{R, \\text{k}}\\gamma_{\\text{k}}}{\\mathcal{C}_{\\text{k}}} N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \n",
+    "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ N_{\\text{s,k}}\\big|_{r_{\\text{k}}=R_{\\text{k}}} = \n",
     "\\begin{cases}\n",
-    "\t\t  \\frac{I}{L_{\\text{n}}}, \\quad &\\text{k}=\\text{n}, \\\\ \n",
-    "\t\t  -\\frac{I}{L_{\\text{p}}}, \\quad &\\text{k}=\\text{p}, \n",
+    "\t\t  \\frac{I}{Fa_{\\text{n}}L_{\\text{n}}}, \\quad &\\text{k}=\\text{n}, \\\\ \n",
+    "\t\t  -\\frac{I}{Fa_{\\text{p}}L_{\\text{p}}}, \\quad &\\text{k}=\\text{p}, \n",
     "\\end{cases} \\\\\n",
-    "c_{\\text{s,k}}(r_{\\text{k}},0) = c_{\\text{s,k,0}}, \\quad \\text{k} \\in \\text{n, p},\n",
     "$$\n",
-    "\n",
-    "where $D_{\\text{s,k}}$ is the diffusion coefficient in the solid, $N_{\\text{s,k}}$ denotes the flux of lithium ions in the solid particle within the region $\\text{k}$, and $r_{\\text{k}} \\in[0,1]$ is the radial coordinate of the particle in electrode $\\text{k}$. All other relevant parameters are given in the table at the end of this notebook.\n",
+    "where $D_{\\text{s,k}}$ is the diffusion coefficient in the solid, $N_{\\text{s,k}}$ denotes the flux of lithium ions in the solid particle within the region $\\text{k}$, and $r_{\\text{k}} \\in[0,R_{\\text{k}}]$ is the radial coordinate of the particle in electrode $\\text{k}$. All other relevant parameters are given in the table at the end of this notebook.\n",
     "\n",
     "\n",
     "#### Electrolyte: \n",
@@ -66,10 +64,10 @@
     "$$\n",
     "\n",
     "$$\n",
-    "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=1}=0, \\\\\n",
+    "N_{\\text{e,n}}\\big|_{x=0} = 0, \\quad N_{\\text{e,p}}\\big|_{x=L}=0, \\\\\n",
     "c_{\\text{e,k}}(x,0) = 0, \\quad \\text{k} \\in \\text{n, s, p},\n",
     "$$\n",
-    "where $D_{\\text{e}}$ is the diffusion coefficient in the solid, $N_{\\text{e,k}}$ denotes the flux of lithium ions in the electrolyte within the region $\\text{k}$, and $x\\in[0,1]$ is the macroscopic through-cell distance. This equation is also solved subject to continuity of concentration and flux at the electrode/separator interfaces.\n",
+    "where $D_{\\text{e}}$ is the diffusion coefficient in the solid, $N_{\\text{e,k}}$ denotes the flux of lithium ions in the electrolyte within the region $\\text{k}$, and $x\\in[0,L]$ is the macroscopic through-cell distance. This equation is also solved subject to continuity of concentration and flux at the electrode/separator interfaces.\n",
     "\n",
     "### Voltage Expression\n",
     "The voltage is obtained from the expression: \n",
@@ -90,7 +88,7 @@
     "where\n",
     "$$\n",
     "\\bar{c}_{\\text{e,n}} = \\frac{1}{L_{\\text{n}}}\\int_0^{L_{\\text{n}}} c_{\\text{e,n}} \\, \\text{d}x, \\quad\n",
-    "\\bar{c}_{\\text{e,p}} = \\frac{1}{L_{\\text{p}}}\\int_{1-L_{\\text{p}}}^{1} c_{\\text{e,p}} \\, \\text{d}x.\n",
+    "\\bar{c}_{\\text{e,p}} = \\frac{1}{L_{\\text{p}}}\\int_{L-L_{\\text{p}}}^{L} c_{\\text{e,p}} \\, \\text{d}x.\n",
     "$$\n",
     "\n",
     "More details can be found in [[3]](#References)."
@@ -248,7 +246,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pybamm",
+   "display_name": "dev",
    "language": "python",
    "name": "python3"
   },
@@ -266,7 +264,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c"
+    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
    }
   }
  },
diff --git a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb
index 5d78554b9a..4d32f6a40e 100644
--- a/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb
+++ b/docs/source/examples/notebooks/models/electrode-state-of-health.ipynb
@@ -20,20 +20,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "ERROR: Invalid requirement: '#'\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "zsh:1: no matches found: pybamm[plot,cite]\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -55,18 +49,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3b55c57e62d4444fb157f8a1bbdde58c",
+       "model_id": "17f51c91ccd74aeb9afa13693702858e",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=2.32485835391946, step=0.0232485835391946), …"
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=2.3248351274860397, step=0.0232483512748604)…"
       ]
      },
      "metadata": {},
@@ -75,10 +69,10 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.plotting.quick_plot.QuickPlot at 0x13d79856520>"
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x17f99c580>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -146,7 +140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -165,18 +159,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "x_100 : 0.83337428922595\n",
-      "y_100 : 0.03354553395256055\n",
-      "Q : 4.968932758817601\n",
-      "x_0 : 0.0015118453536460735\n",
-      "y_0 : 0.8908948803914055\n"
+      "x_100 : 0.8333742766485323\n",
+      "y_100 : 0.03354554691532985\n",
+      "Q : 4.968932683689383\n",
+      "x_0 : 0.0015118453536460618\n",
+      "y_0 : 0.8908948803914054\n"
      ]
     }
    ],
@@ -244,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -252,10 +246,10 @@
      "output_type": "stream",
      "text": [
       "x_100 : 0.833374276202919\n",
-      "y_100 : 0.03354554737459606\n",
+      "y_100 : 0.0335455473745959\n",
       "Q : 4.968932679279884\n",
-      "x_0 : 0.0015118456462390728\n",
-      "y_0 : 0.8908948800898482\n"
+      "x_0 : 0.0015118456462390713\n",
+      "y_0 : 0.890894880089848\n"
      ]
     }
    ],
@@ -288,12 +282,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -350,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -406,12 +400,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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OxkeAxkiimb5/5yttHj0C7O0BPz+aTCSEaBfGgI4d+XkkAJQvDyxbBri5KXd/fX3A0pJfVq8GqlYFjhwBTp4E2rTJu7wJIYToDj0hHzw6Ohr379/H/fv3AQCBgYG4f/8+3qUUQpo8eTL69Okjix86dCjevHmDCRMm4NmzZ1i7di327t2LsWPHCpE+IT8UEQEMHswnE11dgcePAR8fmkwkP0bjIyFZi4sDfv8duHkTsLYGzp0DSpcWOitCCMldu3bxyURjY2D5cuDBA+UnEzOqUAEYPZpfHzWKangTQgjJHYJOKN6+fRvVq1dH9erVAQCenp6oXr06pk+fDgAIDg6WvXkGgBIlSuDEiRM4d+4cqlatiiVLluB///sf3HL615WQPDZ1KhASAri4AMePA2XLCp0R0RQ0PhKSWWIi0KkT4O/Pu5eeOQNUqiR0VoQQkrsiI3nzFQCYNo1PBv5sCVpvb6BIEeD1a2Dx4p/PkRBCCBExxpjQSahTVFQUrKysEBkZCUtLS6HTIZooJka2xDAmNBTmKZ11o6OjYWZmJgu7fZs3XWGMb8dr1kyQbIkSaFxIQ88F+WlKjpGqkkiAHj2AffsAU1M+mdioUa5kTH6AxgWOngfy05QcH0eN4g1YypYFHj7kqxRzw65dQM+egIkJEBAAODvnznF1GY0LhBBdJugKRUK0lUQCDB3KJxN79aLJREII+RlSKS8fsW8fYGTEmwrQZCIhRBvdvQusWcOvr1mTe5OJANC9O9CkCd/yTBVRCCGE/CyNaspCSL5gbMz3LwMwtrTE8dTr6c741q0D7tzhXTGXLBEkS0IIEYYSY6QqGOPNrLZsAfT0+Aqbli1zLVtCCFGfH4yPUikwbBj/t3t3Xn87N4lEvEFLtWr8g5lTp3iDK0IIISQnaMszIbksOBgoVw6IigLWruUnhiR/o3EhDT0XJL+ZMQOYOZNf37oVcHcXMhvdROMCR88DyWsbNwJDhvAasc+e8ZqHecHLi3/gXbo0bxiYm6sgdQ2NC4QQXUZbngnJZZ6efDKxdm2+RY8QQkjOLFuWNpm4ahVNJhJCtNfnz8CkSfz6nDl5N5kI8AYtDg7Aq1fUoIUQQkjO0YQiIapKSuLLZLZuRVJsLLZu3YqtW7ciKSkJ584Bu3fzbXnr1wP6+kInSwghaqZgjFTF5s1pXU7nzAFGjMj9VAkhRK0UjI8TJwLfvvHtyB4eeZuGhUXaROLcucDbt3n7eIQQQrQTbXkmRFXZdOgLD49GvXpmePmSd+dbsULIJIkqaFxIQ88F+Wm50OV5715eP4wxYMIEYP58XvuLCIPGBY6eB/LTshkfz56NRsuWfHy8dg345Ze8T4UxoGlT4OJF4I8/gAMH8v4xtRGNC4QQXUYrFAnJJUuXAi9f8i0ks2cLnQ0hhGimkyeBXr34m90hQ2gykRCi/VI7Lg8apJ7JRCCtQYu+PnDwIHD2rHoelxBCiPagCUVCcknq1pHlywH6gJIQQlR38SLQqROQnAz07MkbW9FkIiFE2z15AhQqBPj4qPdxK1UCRo7k10eOBBIT1fv4hBBCNJuB0AkIJSYmBvpZFLjT19eHiYmJXFx29PT0YGpqmqPY2NhYZLfbXCQSQSwW5yg2Li4OUqk02zzSbzdTJTY+Ph4SiSRXYsViMUQp7xATEhKQnJycK7GmpqbQ0+Nz5ImJiQrrdakSa2JiIvtdSUxMRFJMDFJ/2vSveWJiDFq0MEGXLjw2KSkJiQrOzIyNjWFgYKBybHJyMhISErKNNTIygqGhocqxEokE8fHx2cYaGhrCyMhI5VipVIq4uLhciTUwMIBxSitCxhhiY2NzJVbRz0IIUY/bt4F27YD4eP7v1q28Hi0h+Q2dQ9I5ZG6fQwIx8PExQaFC6j+HHD8e+Ocf4MULYPFiI0yZQueQdA5JCCFKYjomMjKSAcj20qZNG7l4sVicbWzjxo3lYm1sbLKNrVWrllysk5NTtrEVKlSQi61QoUK2sU5OTnKxtWrVyjbWxsZGLrZx48bZxorFYrnYNm3aKHze0uvcubPC2OjoaFmsu7u7wtiwsDBZrIeHh8LYwMBAWayXl5fC2MePH8tivb29FcbevHlTFrtw4UIm5jvxGAOYOEPs339fkMWuXr1a4XGPHz8ui/X19VUYu3fvXlns3r17Fcb6+vrKYo8fP64wdvXq1bLYCxcuKIxduHChLPbmzZsKY729vWWxjx8/Vhjr5eUliw0MDFQY6+HhIYsNCwtTGOvu7i6LjY6OVhjbvn17BoBFRkYyXZc6RtJzQXIsOlo2RkaHhmY59mf0+DFj1tb8bk2bMhYXp8Z8yQ/RuMDROWQaOofkcvMc0s/vgixWqHNIIyNf9v49j6VzSI7OIQkhJHv02T8huahoUaEzIIQQzfLmDdCiBfD1K1CnDnDkCJBukRchhOiE/LAiOzERGDdO6CwIIYRoCp3t8vzp06csO3HRdpWsY2m7SrrtKhERMEvpyufR5w3WbS8JAHj3LhRFihSSxdKWZ83ZrhITEwM7Ozvq0AfqVkhygQpdnj9+BBo1AgIDeS2vixcBa2u1Z0x+QFvGhRkzZmDmzJlyt7m4uODZs2dK3Z/OIekcUtVYReeQFZ3e4Olbfg4ZGhqKQoWEPYd8+BBo0MAIjBni/HmgcWM6h1Qmls4hCSG6TGdrKJqZmWV6Y5NdnCrHVFb6E7jcjE1/wpmbsSYqLBdRJdbY2Fj2Bzs3Y42MjGQnGLkea20N7N2L12+ATZMcAOzFtGmAg4O1XE0lQ0ND2YnWj6gSa2BgIDsxzM1YfX19pX+HVYnV09PLk1iRSJRrsYrevBBCVGRsDOzdy69aWmJv6vUM43d4ONCyJZ9MLFWKdxilyUSS1ypWrIjz58/Lvlb2b2R6dA5J55A5jk05hzxwAHi+xwEFCuzF8uWAtbXw55D16gEeHsCaNbxBy4MHBjAzo3NIOockhJDs6eyEImJigCwKakNfX36vlYJPjKGnB6Q/oVIlNjaWV1DJikgEpD8BVCU2Lg5Q8Ikx0v9BVCU2Ph5Q9AdTlVixOK1tZ0ICb+eZG7Gmpmn7RRITAQWfGKsUa2KS9ruSEitxa4O+TQAjSPBnlzaYNTElP5EoLTYpSXG7PGNjIPWkTpXY5GT+WNkxMgJSTyxViZVI+GuXHUNDHq9qrFTKf9dyI9bAgD8XAP8/oeATY5ViqaA2IbnHwADo0oVfBdAl5Xp6UVFAq1bA06e8VMT584CDg5rzJDrJwMAA9vb2P3cQOoekc8ifOIcMLN8Ggw8DxpBg47I26NIZ+eYccvZ0I+zda4iAAGDVsmR4DqdzSDqHJIQQBYQq3igUWWHxdEWR5S4ZCmozsTjrOICxDAW1mY1N9rEZCmozJ6fsYzMU1GYVKmQfm6GgNqtVK/vYDAW1WePG2cdmKKjN2rTJPjbjr1Hnzopj0xfmd3dXHJuuoDbz8FAcm66gNvPyUhybrqA28/ZWHJuuoDZbuFBx7IULabGrVyuOTVdQm/n6Ko5NV1Cb7d2rODZdUxZ2/Lji2HQFtdmFC4pj0xXUZjdvKo5NV1CbPX6sODZdQW0WGKg4Nl1BbRYWpjg2XUHt9E0isrpEUkFtGWq+QPJabCxjv/6a9mcpIEDojMiPaMu44O3tzcRiMXNwcGAlSpRgPXv2ZG/fvs02Pj4+nkVGRsou79+/Z3QOmYLOITktPIfcvJlf7WRC55CMMTqHJIQQBfJB+V9CNFcygH0pFwWfexNCiO5ITgb27QP27UNyfDz27duHffv2ITk5GYmJQOfOwKVLgKUlcOYMUK6c0AkTXVG3bl1s3boVp0+fxrp16xAYGIhGjRrh+/fvWcb7+PjAyspKdnF0dFRzxkSb5ddzyL59gbp1gThaeEcIIeQHdLYpS2Q2BbVpu0o2sbRdRRY7tEcE1h/kBbWjXr2BVWleUDs6NBRmhQoJvl2FtjyrHhsVEwMrKqgNQHuaLxABZdOUJTIyGoMHm2HPHj4Enz0LNGwoZKJEWdo6LkRERMDJyQlLly7FgAEDMn0/ISFBrilFVFQUHB0d6RxS1Vg6hwQAxHxLRIOKEbgfzMfEmDdvYF4yf55D3rkD/FIrGUZIwOlTvHlWdrEA6BySziEJITpKd2sompnJn8AoilPlmMpSoUi2SrEqFMlWKVaFItkqxRobp/3Bzs1YI6O0E4xcjPW7bIQdB82wPuVrfYt0r7mZmXxNJUPDtBOtH1El1sAg7cQwN2P19ZX/HVYlVk8vb2JFotyLpYLahOS50aOBPXv4UHfwIE0mapIzZ4TOIG8UKFAAZcuWxatXr7L8fraNPOgcks4hcxA7Z6ERXgZnOG9Mfz0fnUPWrAkMGGKADRsM4DEeuHv3Bw9B55CEEKKTaMszIUpKSODd7wghhKhu61b+fu+ff3hDFqIZfH2Brl2FziJvREdH4/Xr13CgjkAkjz19CixeLHQWqpk7F7C2Bh4/BtauFTobQggh+ZHurlAkREULFwIvXgAlCgMIEzobQvJWTEwM9LPoYqqvrw+TdCtIYhRs09PT04NpulUsqsTGxsYiu4ocIpEI4nSrblSJjYuLg1TBNj2zdKsQVImNj4+HRMEqBVVixWIxRCnb9BISEpCsYJueKrGmpqbQS9mml5iYiCQF2/RUiTUxMZH9riQmJiIpJgapP638ax6DDRtM0Lkzj01KSkKigm16xsbGMEhZSaNKbHJystw21YyMjIxgmLLURpVYiUSCeAXb9AwNDWGUslJJlVipVIo4Bdv0VIk1MDCQrahjjCFWwTY9ZWKPHdPHwIFKrurSAF5eXmjXrh2cnJzw6dMneHt7Q19fHz169BA6NaLFGAOGD+e7jTu0BnBK6IyUU6gQ4OMDDBkCTJ8OdOsG/GyDdEIIIVpGyI4wQtCWToVEvV6+ZMzYOKVZnm9at7fo0FAGgAFg0ek7DxKNQuNCmtTnIrtLmwxdTMVicbaxjTN0MbWxsck2tlaGLqZOTk7ZxlbI0MW0QoUK2cY6ZehiWqtWrWxjbTJ0MW3cuHG2seIMXUzbtGmj8HlLr3Pnzgpj048l7u7uCmPD0nUx9fDwUBgbmK6LqZeXl8LYx+m6mHp7eyuMvZmui+nChQuZOF33S3GG2AvpupiuXr1a4XGPp+ti6uvrqzB2b7oupnv37lUY6+vrK4s9fvy4wtjV6bqYXrhwQWHswnRdTG/evKkw1jtdF9PHjx8rjPVK18U0MDBQYaxHui6mYWFhCmPd03UxjY6OziKmMQPiGMCYo+MxBmj+GNmtWzfm4ODAjIyMWNGiRVm3bt3Yq1evlL4//a0gOfH333xINDVlLPCxZp1DJienNf/u00fobPInGhcIIbqMVigS8gOMASNG8C3Prq68Qyn6CZ0VIYQQkleqATgKwATAYVSrth3v3wubUW7YvXu30CkQHRMZCYwbx6//9Rfg7CxoOirT1wdWrwZ++QXYvh0YPBho0EDorAghhOQXutvlmTpxESXt28frRxkZAY8eAWWLZt3BNDo6Wm5bI9EcNC6kSX0uPr16BUsLi0zf1zcygkmBArKvY8Ky3/+vZ2AAU2vrHMXGhoeDZbPdWKSnB7GNTY5i475+hVTBtmCzwoVzFBsfEQGJgi25qsSKbWwgStlunBAVhWQFW2dViTW1toZeyrbgxOhoJCnYDqtKrEmBAtBP2ZKbGB2NpM+fYZbSudQGj/EFlQAAoW/eoFDRorLYpNhYJEZHZ3tcY0tLGKRsr1clNjk+HglRUdnGGpmbwzBlG7wqsZLERMRHRGQbaygWwyjlb4MqsdLkZMR9/ZorsQYmJjBOGcOYVIrY8HCVY1++1keLdgUR/kUPDesn4vCuCEgQDzsnJ50fI+lvBVHVqFHAqlVA2bLAw4eAcbJmnkMOHAhs3gxUrQrcvq18vz9dQOMCIUSnCb1EUt1oWTpRRWQkYw4OfKuHbIdaYiJjvr6M+fqyxJgY5uvry3x9fVliYqKAmZKfQeNCGtlzkW7bqtwlw5ZnJhZnHQcwlmHLM7OxyT42w5Zn5uSUfWyGLc+sQoXsYzNseZbt3crqkmHLM2vcOPvYDFueWZs22cdm/FPbubPi2PRb39zdFcem2/LMPDwUx6bb8sy8vBTHptvyzLy9Fcem2/LMFi6U+14iwHxTLokAY+m2PLPVqxUfN92WZ+brqzg23ZZntnev4th0W57Z8eOKY9NteWYXLiiOTbflmd28qTg23ZZn9vix4th0W55ZYKDi2HRbnllYmOLYdFueWTTfhvkBRZgTAhnAWA3cZpGwYAxgke3bMxoj6W8FUc2dO4zp6fH/bufOpdyooeeQYWGMFSyYeVgkNC4QQnQbfb5EiALTpwPBwUDp0sCkSSk3GhoCffvyqwD6plwnhBBd9/Yt4JTua0MAfQXKhajmKwrCDWfwFs4ogxc4hdawxHeh0yJEI0mlgIcH/7d7d14yB4DGnkPa2gJz5vDmMlOn8p07trZCZ0UIIURotOWZkGzcuwfUqsVPBs+cAVq2FDojkldoXEgjey4+fcr6udDXB9J1eYaCzs3Q0wPSdW5WKTY2lq+jyopIBKTr3KxSbFwc/0+dnfRbzlSJjY8HFHRuVilWLOZ5A7x4q4Jt1yrFmpry5xkAEhMBBZ2bVYo1MQH09fHgAdCicSJiIpPQvBkvF2GcsUFwSiwAfkwFW79hbJy2r06V2ORk/lxkx8iIv6lXNVYi4a9ddgwNebyqsVIp/13LjVgDg7QnnTH+f0OJ2JhoBtfmUly/qY8iDlJcOR8PZ6e0/1NRMTGwsrNT2xhpna78gTJEIhHu3r0LJyenHwf/BPpbQZS1aROvN2hhATx7BhQpInRGP08iAWrX5ufH/fvzLdCExgVCiG4TfIXimjVrsGjRIoSEhKBq1apYtWoV6tSpk2388uXLsW7dOrx79w42Njbo3LkzfHx8YJL+DS4hP0kiAYYO5e/dunbNMJmYnMxnGAEkN2+OM35+AAA3NzcYUFEZkssEGyPNzOQnwRTFqXJMZaWfBMzN2PSTlrkZq8rzq0qssXEWs3K5EGtklDZJlQuxL17wcfJzpBF+ra+HvR5nYHwtizEydTIR4BNlqZN1P6JKrIGB8gW+VInV11f+d1iVWD29vIkViZSKTUwEOnUW4fpNfRQsCJw9pwfnChn+TymaAM8DERERWL58OaysrH4YyxiDh4cHJGrOkZDshIen7WqZOTPDZKIGn0OmNmhp0ADYsgUYNIg3ayGEEKLDhNxvvXv3bmZkZMS2bNnCnjx5wgYNGsQKFCjAQkNDs4zfuXMnMzY2Zjt37mSBgYHszJkzzMHBgY0dO1bpx6Q6F0QZ69bxOjEWFox9/Jjhmym1phjAokNDGQAGgEWnr3tGNEp+HRdojCSa4O1bxhwd+bBYvTpjER9pjNQUycmMdeuWVhb02rWs49Q9LohEomzHuayYm5uz169f52FGHI2PRBkDBvD/U1WqMJaUlOGbWnAOmVrat0YNPoboOhoXCCG6TE+YaUxu6dKlGDRoEPr164cKFSpg/fr1EIvF2LJlS5bxV69eRYMGDdCzZ084OzujZcuW6NGjB27evKnmzIk2Cw1N+2R5zhzt2KZCNBONkSS/CwsDWrQA3r8HXFyA06cBJRaVkXyAMd6Bds8evvjz4MH8s9pIKpWicLrO6D/y/ft3lEzpLE6IkK5eTdsKvG6ddnZDXrCAj/N37wL/+5/Q2RBCCBGSYBOKiYmJuHPnDlxlVYoBPT09uLq64tq1a1nep379+rhz547szfGbN29w8uRJtGnTRi05E93g5QVERgI1avCC2oQIgcZIkt9FRABubny7c/HiwLlzgApzQERgM2YAa9fyndE7dvDXkhCSc8nJwLBh/Hr//kD9+sLmk1fs7IBZs/j1KVOAL1+EzYcQQohwBPvcLDw8HBKJBHZ2dnK329nZ4dmzZ1nep2fPnggPD0fDhg3BGENycjKGDh2KKVOmZPs4CQkJSEhXcD0qKip3fgCilS5cAP7+m7/BWr9eOz9ZJplduCB0BpnRGEnys5gYoG1b4P59/uby/HnA0VHorIiyVq1KmxBYswbo1k3YfH7k5cuXuHDhAsLCwiDN0Chp+vTpAmVFiLw1a4CHD4GCBYH584XOJm95ePCVmA8f8knFDRuEzogQQogQBN3yrCp/f3/MmzcPa9euxd27d3Hw4EGcOHECs2fPzvY+Pj4+sLKykl0c6R0PyUZCQtony0OH8k52RPvFxwOenkJnkTtojCTqkJAA/PEHcOUKUKAAcPYsUKaM0FkRZe3cybc6A3xSMfXvXn61adMmlC9fHtOnT8f+/ftx6NAh2eXw4cNCp0cIAODTJ2DaNH59/nzA1lbYfPKagQFv0ALwjta3bwubDyGEEGEItv7KxsYG+vr6CA0Nlbs9NDQU9vb2Wd5n2rRp6N27NwYOHAgAqFy5MmJiYjB48GD89ddf0NPLPD86efJkeKabLYiKiqI3zCRLixcDz5/zLXvz5gmdDVGXBQuAN2+EziIzGiNJfpScDPTqxScRzcyAU6eAKlWEzooo6+RJoG9ffn3kSGDqVEHTUcqcOXMwd+5cTJw4UehUCMmWlxfw/TtQpw6Q8idY6zVqBPz5J9/ZM3w4cO0ab0RPCCFEdwg27BsZGaFmzZrw8/OT3SaVSuHn54d69epleZ/Y2NhMb4j19fUBAIyxLO9jbGwMS0tLuQshGb15wxuwAMDSpXzVDdF+L18CPj5CZ5E1GiNJfiOVAoMGAQcOAEZGwOHD+aeJB/mxK1eAzp3TJoWXL+flPfK7b9++oUuXLkKnQUi2/PyAXbv4ZNq6dbo1qbZwIWBhAdy8Cfj6Cp0NIYQQdRO0Qpynpyfc3d1Rq1Yt1KlTB8uXL0dMTAz69esHAOjTpw+KFi0Kn5R3/O3atcPSpUtRvXp11K1bF69evcK0adPQrl072ZtmQlTFGDBiBN/62qwZ0LPnD+5gZCTb52Fkbo7VqdeNjPI4U5KbGOOfqCck8Nf933+FzigzGiNJfsEYLw2wdSugrw/s3g2k6xckj8bIfOfhQ17zMi4O+O03/sZfUyY9unTpgrNnz2Lo0KFCp0JIJomJ/FwC4HUFa9T4wR20bHx0cOANnsaNAyZNAjp2BKythc6KEEKIugg6oditWzd8/vwZ06dPR0hICKpVq4bTp0/LmhC8e/dObrXN1KlTIRKJMHXqVHz8+BG2trZo164d5s6dK9SPQLTAwYN8256hIS+o/cMVG4aGsrNHQwDDU88kiUbZu5d3pTU25tvdf/gmQAA0RpL8YtYsYMUKfn3LFv6mMVs0RuYrb97wDs4REUCDBnzsMzQUOivFVq5cKbteunRpTJs2DdevX0flypVhmCH5UakFIQkRwJIlaeVyFJQrTqOF4+PIkfzvwpMnvI7kmjVCZ0QIIURdRCy7fXBaKioqClZWVoiMjKStfQTfvwPlywMfP/JaUkqdDBKNFxnJX/fgYP7J+tixNC6kojGSZLRiBTBmDL++ciV/80g0Q0gI0LAh8Po1ULkycPEi70CrKnWPCyVKlFAqTiQS4Y0ai+DS+EjSCwoCKlTgK3937OD1BHWVvz/QtClf+Xz7NlC9utAZqQ+NC4QQXSboCkVChObtzScTS5YEpkxR8k4SCXD5Mr9avz4uX70KAGjUqBFtK9UQ06bxycQyZYCJE/mWJUJIZr6+aZOJs2YpOZlIY2S+EBEBtGrFJxNLlADOnMnZZKIQAgMDhU6BkB8aM4ZPJjZuzOuSKkVLx8cmTYDu3Xk5jOHDgf/+05yyCoQQQnKOVigSnXX/PlCrFj+3O3WKv/FSSkwMYG7Or4aGwjxl+2l0dDTMzMzyJlmSa+7eBWrX5g0mzp3jdeBoXEhDzwVJdeAA0LUr/7/i6clLAyjVxIPGSMHFxvJtzv/9B9jZ8YYspUrl/Hg0LnD0PJBUx48D7doBBgbAgwd8paJStHh8/PgRKFcOiI7mH0aldpTXdjQuEEJ0GX12RHSSVAoMG8YnEzt3VmEykWg0iQQYOpS//t27K2gqQYiOO3sW6NGD/18ZMECFyUQiuKQkoFs3PploZcVXJv7MZGJ+deTIEWzfvl3oNIgOio1NW63t6anCZKKWK1oUmD6dX58wga+SJoQQot1oQpHopP/9D7h+nX9IvHy50NkQddmwAbh1C7C0BJYuFTobQvKnq1d505WkJKBLF/7/hiYTNUPqBPDx44CJCXDsGFC1qtBZ5Y2JEyfKOt4Tok4+Prx+YrFivIQKSTN6NF+l+PkzLytECCFEu9GEItE5YWHApEn8+uzZ/BNVov1CQtLqZM6dCzg4CJsPIfnRgwdAmzZ8BU6rVsDffwMaXNZLpzAGjBvHm0Po6wP79gGNGgmdVd559uwZJBKJ0GkQHfPiBbBwIb++YoVs9zJJYWQErFrFr69eDTx8KGw+hBBC8hZNKBKdM2EC8O0bUK0aMGKE0NkQdfHy4t2da9bk290JIfJevABatuT/Txo25DUUjYyEzoooy8cnbcW9ry/Qtq2g6eS5iIgIrF69Wug0iA5hjJ83JibyD1w6dhQ6o/zJ1ZWXE5JKeYMW3arWTwghuoUmFIlOuXgR2LaNb99bv54X0ybaz88P2Lkz7XWnFVeEyHv3jr8JDAsDqlfnW2bFYqGzIsrauBH46y9+fdkyoHdvYfPJS35+fujZsyccHBzgTXsqiRrt38+buRkb81V4VAoie0uX8r8h//3Hz78IIYRoJ5pQJDojMTFtZdrgwUDdusLmQ9QjIQHw8ODXPTx4Z29CSJqwMKBFC+D9e8DFBTh9mjfzIJph/37ebArgZR3GjBE0nTzx/v17zJo1CyVKlEDLli0hEolw6NAhhISE5PiY8+fPh0gkwhhtfMJIrvv+HRg7ll+fNAkoXVrYfPI7R0dg6lR+ffx4ICpK2HwIIYTkDVqfRXTG4sVAQABQuDDfGpZjhoayAjqGYjEWpl43NMyFLEluW7SIb+W0t+e1EwkhaSIiADc3/n+keHG++qZw4Z88KI2RanPuHNCzJ99SOGQIMGeO0BnlnqSkJBw+fBj/+9//cPnyZbRq1QqLFi1Cjx498Ndff6HCT7TWvXXrFjZs2IAqVarkYsZEm82YAXz8CJQsCUyc+BMH0qHx0dOTl194+ZI/f9QMjxBCtI+IMd2qbBEVFQUrKytERkbC0tJS6HSImrx5A1SsCMTH8yYDvXoJnRFRh9ev+euekAD88w/Qo0fWcTQupKHnQnfExvKaiVeuAHZ2wOXLQJkyQmdFlHXzJtCsGRATw+uV7d6dd+UchBgXChcujHLlyuHPP/9Ely5dULBgQQB84uXBgwc5nlCMjo5GjRo1sHbtWsyZMwfVqlXD8tTikz9A46NuevgQqFEDkEiAU6d4/USinDNn+POlrw/cvw9UqiR0RrmPxgVCiC6jLc9E6zHGi0LHxwPNm/PVHET7pRZPT0jgr3v37kJnREj+kZgI/PEHn0wsUAA4e5YmEzVJQADQujWfTHR11c5u3MnJyRCJRBCJRNDPxR9u+PDh+O233+Dq6vrD2ISEBERFRcldiG6RSnm5HImET9zTZKJq3NyADh348zdiBDVoIYQQbUMTikTr7d/Pa4IZGQFr1+ZCEW2JBLh1C7h1C5LERNy6dQu3bt2CRCLJlXxJ7jhwIJdfd0K0hEQC/PknXzkiFgMnTwK5uvOTxsg89fYtr3n59StQpw5w6BBvEqFtPn36hMGDB2PXrl2wt7dHp06dcOjQIYh+YjDfvXs37t69Cx8l6574+PjAyspKdnF0dMzxYxPN5OsLXL0KmJvzhkc/TQfHx2XLABMT3hhxzx6hsyGEEJKbaEKRaLXISGD0aH598mSgbNlcOGh8PH8XV6cO4iMiUKdOHdSpUwfx8fG5cHCSG75/T3vdJ03KpdedEC2QWmtv3z4+2X74MFCvXi4/CI2ReebzZ75N/eNHoFw54MQJPtGhjUxMTNCrVy/8+++/ePToEcqXL49Ro0YhOTkZc+fOxblz51SahHn//j1Gjx6NnTt3wsTERKn7TJ48GZGRkbLL+/fvc/rjEA0UHg5MmMCvz5wJFCuWCwfVwfHR2Zk3jAKAceP4ORohhBDtQBOKRKtNmwYEB/OtfJMmCZ0NUZfp04FPn4BSpfhEMiGETyZ6eQGbNwN6esCuXXylG9EM37/zbc4vXvAOqmfPAjY2QmelHqVKlcKcOXPw9u1bnDhxAgkJCWjbti3s7OyUPsadO3cQFhaGGjVqwMDAAAYGBrh48SJWrlwJAwODLCcnjY2NYWlpKXchumPyZL4SuHJlYORIobPRbOPH83OyT5+A2bOFzoYQQkhuoS7PRGvdvg2sXs2vr13Lt1sQ7XfvHrByJb++Zg297oSkmjs3rcvm5s28hiLRDPHxvA7ZnTt8EvHsWT6pqGv09PTQunVrtG7dGp8/f8aOHTuUvm/z5s3x6NEjudv69euHcuXKYeLEiblap5FovqtXgf/9j19ft443ZyY5Z2ICrFgBtG3Lt0D36weULy90VoQQQn4WTSgSrSSR8G19jPGOzkrUXidaILV4ulQKdO3Ki4ETQoBVq/iKbQBYvhzo21fIbIgqkpN5M7F//+Xbm0+d4tuddZ2trS08PT2VjrewsEClDC1mzczMUKhQoUy3E92WnMzPJQCgf3+gQQNh89EWv/3GJxSPH+crPs+do/rWhBCi6WjLM9FKK1cCd+/y7qVLlgidDVGXTZuAGzcAC4tcKp5OiBbYvh0YNYpfnzEjrb4oyf8YA4YO5Y1XjIyAI0eAWrWEzirvWVtbIzw8XOn44sWL4+3bt3mYEdElq1YBDx8C1tbAggVCZ6NdVqzgTaT8/HjTREIIIZqNVigSrfPiRVrx54ULARVKLBENFhaWVidzzhygSBFh8yEkPzh8mK+wAYAxY3h9UaI5pkyRr3nZrJnQGalHREQETp06BSsrK6Xiv3z5kqMuuf7+/irfh2i3N2+AqVP59fnzdadOqbqULAlMnAjMmgV4evK6sNraWIoQQnQBTSgSrSKR8K188fG82cDAgUJnRNTFywuIiACqVwc8PITOhhDh+fkB3brxcbFfP75am7aXaY4lS/iEBgBs3Kh7NS/d3d2FToHoGKkUGDAAiI0FmjTh10numzSJr5wPCgLmzeMXQgghmokmFIlWWbYMuHaNb3n93//y6M2zoSHg7c2visXwTr1OFbsFc+ECsGMHf73XrwcMaGQjOu76daB9eyAxkU9EbdzIV7mpBY2RP23rVv4hCcC3XOraxIZUKhU6BaKD1q8H/P0BsThtZXCuo/ERpqa8lm+HDsDixXwhQNmyAidFCCEkR0SMMSZ0EuoUFRUFKysrREZGwtLSUuh0SC4KCOCr0xIS+GSirr0B01WJiUDVqsCzZ7yI+tq1qh+DxoU09FxovocPgcaN+Yrdli2Bo0d5zSqiGY4cATp14itLvbyARYuEzojGhVT0PGivwECgcmUgJobXUBwxQuiMtBtjvEnLqVO8gd6pU5q7gp7GBUKILqOmLEQrJCfzTzgTEoBWrdJqhhHtt3gxn0wsXJi2zRDy6hWfRIyIAOrXBw4epMlETXLxovw29YULhc6IEO2XutU5Jgb49Vcqm6IOIhFv0GJkBJw5w+v9EkII0Tw0oUi0wpIlwM2bgJUV7/Sbp59ySqXAkyfAkyeQJifjyZMnePLkCW3REkBgIDB7Nr++dCnv6k2IrvrwAXB1BUJDgWrVgBMnADMzARKhMTJH7t0D2rXjH4y1b8+3qWvqih1CNMnGjbx0iqkpsGVLHpeHoPFRpkyZtNIOY8bw2pWEEEI0C1UaIxrvyZO0zqXLlwPFiuXxA8bFAZUq8auhoaiUcj06Ohpmgrx7102M8S1J8fFA06ZAz55CZ0SIcD5/5o2o3r7lb9JOnxZwgp3GSJW9eMG3/X3/zrer795NtWAJUYegIGD8eH59/nygVKk8fkAaH+VMmcJrYL97B/j4pH1ITAghRDPQCkWi0ZKTAXd3Xkfvt9/4daIbDh0CTp7k9c3XrqWVPER3RUbyyahnzwBHR+D8ecDOTuisiLI+fuTb1D9/BmrU4DUvTUyEzooQ7ccYMHAgEB0NNGpEdROFYGbGGyoCvMTDq1fC5kMIIUQ1NKFINNqUKcCdO3wlDm0P0x3fvwOjR/PrEycC5coJmw8hQomNBdq25dtlbW2Bc+eA4sWFzooo6+tXPhmcurL01CmAavrLa9y4MbZv3464uDihUyFaZv16wM+Pb3XOs67O5If++IOvsE9M5FufCSGEaA7BN9SsWbMGixYtQkhICKpWrYpVq1ahTp062cZHRETgr7/+wsGDB/H161c4OTlh+fLlaNOmjRqzJvnBoUNp3S83bQKKFBE2H6I+M2bwenElS/JJZW1GYyTJTmIi7wb833+8fuzZs4CLi9BZEWXFxPCV9U+e8L9fZ8/y5lJEXvXq1eHl5YWRI0eia9euGDBgAH755Reh0yIa7u5dYOxYfn3ePD6hT4QhEvHO2pUr89q/x47xerJENYwxJCcnQyKRCJ0KIUTD6evrw8DAACIlVmsJOqG4Z88eeHp6Yv369ahbty6WL18ONzc3PH/+HIWzOKtOTExEixYtULhwYezfvx9FixbF27dvUYA6Meicly95V2cA8PQEOncWNB2iRg8f8s6AALBmDV9ZoK1ojCTZkUiAXr14rUSxmG//r1ZN6KyIslIng69fBwoW5JOJzs5CZ5U/LV++HIsXL8bRo0exbds2/PrrryhdujT69++P3r17w4729xMVffvGzxsTEoDffwdGjRI6I+Liws/nFyzgO1BcXbX7/C63JSYmIjg4GLHU2YYQkkvEYjEcHBxgZGSkME7EGGNqyimTunXronbt2li9ejUAQCqVwtHRESNHjsSkSZMyxa9fvx6LFi3Cs2fPYGhomKPHjIqKgpWVFSIjI2FJ+4o0Umws8MsvwKNHQMOGwL//8jp6ahMTA5ib86uhoTBPeTOjqwW11Ukq5a/5tWv8zcC+fblz3Pw6LtAYSbIilQKDBvFupEZGwPHjfLtYvkFjpEKpk8F79vDJYD8//jctP8tP40JYWBg2btyIuXPnQiKRoE2bNhg1ahSaNWuW54+dn54HkjNSKdChA18FV6IEL5tTsKAaE6DxMVvR0byEzcePwMyZaQ0X8zuhxwWpVIqXL19CX18ftra2MDIyUmpVESGEZIUxhsTERHz+/BkSiQRlypSBnoKaIIKtUExMTMSdO3cwefJk2W16enpwdXXFtWvXsrzP0aNHUa9ePQwfPhxHjhyBra0tevbsiYkTJ0JfX19dqRMBMQYMHconEwsX5m/I1DqZSAS1eTOfTDQ35x29tRmNkSQrjAHjxvHJRD09YNeufDaZSBRijK+GSv3bdfBg/p9MzE9u3rwJX19f7N69G4ULF0bfvn3x8eNHtG3bFh4eHli8eLHQKZJ8btEiPplobAzs36/myUSikLk5sGQJ0L077/jcuzef9CWKJSYmyj5wFovFQqdDCNECpqamMDQ0xNu3b5GYmAgTBd0CBZtQDA8Ph0QiybRVxc7ODs+ePcvyPm/evMG///6LXr164eTJk3j16hU8PDyQlJQEb2/vLO+TkJCAhIQE2ddRUVG590MQtdu4Edixg7+R3rNHoLqJhoaAlxe/KhbDK/U6zWzmqc+feQMWAJg9GyhaVNh88hqNkSQrs2alTaZv2cKL2ec7NEZma8aMtK70O3bwhixEsbCwMOzYsQO+vr54+fIl2rVrh127dsHNzU22Cqdv375o1aoVTSgShfz90+our1zJu6qrHY2PCnXtys/1//2X17g8fFjojDSHohVEhBCiKmXHFMGbsqhCKpWicOHC2LhxI/T19VGzZk18/PgRixYtyvbNso+PD2bOnKnmTEleuHUrrc6Njw/QpIlAiRgZybrBGAFYlNoZhuSpCRN43aNq1YARI4TOJn+iMVK7LV3KJ6QAXkfU3V3QdLJHY2SWVq3iE8IAr//arZuw+WiKYsWKoVSpUujfvz/69u0LW1vbTDFVqlRB7dq1BciOaIrgYL7yTSoF+vThZSMEQeOjQqkNWqpWBY4c4Z3vW7cWOitCCCHZEeyjDBsbG+jr6yM0NFTu9tDQUNjb22d5HwcHB5QtW1Zu61758uUREhKCxMTELO8zefJkREZGyi7v37/PvR+CqE1kJK+Zl5jIa9+MHy90RkSdLl0Ctm7lJ5rr1gEGGvVRSM7QGEnS27SJb3UGgDlzqImAptm5M+01mz0bGDZM2Hw0iZ+fHwICAjB+/PgsJxMBwNLSEhcuXFBzZkRTJCfzycTQUKBSpbRVwiR/qlAhbbwcNYo3zyFEFSKRCIeVXN46Y8YMVPtBV7smTZpgzJgxP52XOgUFBUEkEuH+/ftCp/JT/P39IRKJEBERIXQqJBuCTSgaGRmhZs2a8PPzk90mlUrh5+eHevXqZXmfBg0a4NWrV5BKpbLbXrx4obD7jLGxMSwtLeUuRPOMGwe8eweULJk2sSQYqRQICgKCgiBNTkZQUBCCgoLkfi9J7klMTHvzPXiw7tQbozGSpNq9GxgyhF+fMCFty16+RWOknJMngb59+fVRo4C//hI0HY3j7e2d5RuJqKgotTRiIZpv3jz+waSFBa+bKGjvExofleLtDdjbA69e8bqKRPt8/vwZw4YNQ/HixWFsbAx7e3u4ubnhypUrshhVJgbTCw4ORutcXNp68OBBzJ49O9eOl1Nbt25FgQIFlIp1dHREcHAwKlWqlLdJEZ0naLEFT09PbNq0Cdu2bUNAQACGDRuGmJgY9OvXDwDQp08fuYYEw4YNw9evXzF69Gi8ePECJ06cwLx58zB8+HChfgSiBmfO8GYcIhGfTLSyEjihuDheJbpECcR9/YoSJUqgRIkSiIuLEzgx7bR0KfD0KWBry7e66xIaI8nx47wwfWpDqvnzNWBlDY2RMleu8NX1ycm8s/OyZRrw+uUzFy9ezHKFdXx8PC5fvixARkSTPHzIVwUDfIeDi4uw+dD4qBxLSyC1JOqcOXxRAdEunTp1wr1797Bt2za8ePECR48eRZMmTfDly5efPra9vT2MjY1zIUvO2toaFhYWuXa8vJaYmAh9fX3Y29vDQBe2dRFBCTqh2K1bNyxevBjTp09HtWrVcP/+fZw+fVrWhODdu3cIDg6WxTs6OuLMmTO4desWqlSpglGjRmH06NGYNGmSUD8CyWNRUWl1bkaOBBo1EjYfol5BQWk1x5Ys0b1ujDRG6rYLF9Imo/78k9fdo8kozfHoEdC2LZ8/aNMG8PXlDcWIch4+fIiHDx+CMYanT5/Kvn748CHu3buHzZs3o6i2d+ciPyU5Gejfn//bvj3Qs6fQGRFV9OwJ/PorH0NTS34Q7RAREYHLly9jwYIFaNq0KZycnFCnTh1MnjwZv//+OwDA2dkZANCxY0eIRCLZ1wCwbt06lCpVCkZGRnBxccGOHTvkjp9xZeOHDx/Qo0cPWFtbw8zMDLVq1cKNGzfk7rNjxw44OzvDysoK3bt3x/fv32Xfy7jl+du3b+jTpw8KFiwIsViM1q1b4+XLl7Lvp64kPH78OFxcXCAWi9G5c2fExsZi27ZtcHZ2RsGCBTFq1ChIJBLZ/RISEuDl5YWiRYvCzMwMdevWhb+/PwC+9bdfv36IjIyESCSCSCTCjJTC2s7Ozpg9ezb69OkDS0tLDB48OMstz0+ePEHbtm1haWkJCwsLNGrUCK9fv872dXr8+DFat24Nc3Nz2NnZoXfv3ggPD5d7XkaNGoUJEybA2toa9vb2spwAoGfPnuiWoWB0UlISbGxssH37dgB895WPjw9KlCgBU1NTVK1aFfv37882JwA4cOAAKlasCGNjYzg7O2NJhmXMqc9Hjx49YGZmhqJFi2LNmjVyMRERERg4cCBsbW1haWmJZs2a4cGDBwofl2SD6ZjIyEgGgEVGRgqdClHC4MGMAYyVLMlYdLTQ2aSIjuZJASw6NJQBYABYdL5JUDtIpYy1bcuf6iZN+Nd5hcaFNPRc5A/XrzNmZsZ//9u3ZywpSeiMVEBjJHv9mjEHB/40NGjAWEyM0Bn9HCHGBZFIxPT09Jienh4TiUSZLmKxmG3evFlt+TBG46Om8fHh/wcLFGDs0yehs0lB46NKHjxgTF+fP2XnzgmdTdaEHhfi4uLY06dPWVxcnOw2qZT/qqn7ouy5elJSEjM3N2djxoxh8fHxWcaEhYUxAMzX15cFBwezsLAwxhhjBw8eZIaGhmzNmjXs+fPnbMmSJUxfX5/9+++/svsCYIcOHWKMMfb9+3dWsmRJ1qhRI3b58mX28uVLtmfPHnb16lXGGGPe3t7M3Nyc/fHHH+zRo0fs0qVLzN7enk2ZMkV2vMaNG7PRo0fLvv79999Z+fLl2aVLl9j9+/eZm5sbK126NEtMTGSMMebr68sMDQ1ZixYt2N27d9nFixdZoUKFWMuWLVnXrl3ZkydP2LFjx5iRkRHbvXu37LgDBw5k9evXZ5cuXWKvXr1iixYtYsbGxuzFixcsISGBLV++nFlaWrLg4GAWHBzMvn//zhhjzMnJiVlaWrLFixezV69esVevXrHAwEAGgN27d48xxtiHDx+YtbU1++OPP9itW7fY8+fP2ZYtW9izZ8+yfP6/ffvGbG1t2eTJk1lAQAC7e/cua9GiBWvatKnc82JpaclmzJjBXrx4wbZt28ZEIhE7e/YsY4yx48ePM1NTU1mejDF27NgxZmpqyqKiohhjjM2ZM4eVK1eOnT59mr1+/Zr5+voyY2Nj5u/vzxhj7MKFCwwA+/btG2OMsdu3bzM9PT02a9Ys9vz5c+br68tMTU2Zr6+v7DGcnJyYhYUF8/HxYc+fP2crV65k+vr6srwYY8zV1ZW1a9eO3bp1i7148YKNGzeOFSpUiH358iXL50MXZTW2ZIUmFEm+de6c7JyLXbggdDbp0MmgWhw6xJ9mQ0PGnj7N28eicSENPRfCu3+fvwEGGHN1ZewHf8fzHx0fI4ODGStVij8FlSsz9vWr0Bn9PCHGhaCgIBYYGMhEIhG7desWCwoKkl0+ffrEkpOT1ZZLKhofNcfTp4wZGfH/h1u3Cp1NOjo+PubEqFH8KXNxYSwhQehsMhN6XMjqTX+6XzO1XlT5Nd6/fz8rWLAgMzExYfXr12eTJ09mDx48kItJPzGYqn79+mzQoEFyt3Xp0oW1adMmy/tt2LCBWVhYZDtR5O3tzcRisWyCizHGxo8fz+rWrSv7Ov2E4osXLxgAduXKFdn3w8PDmampKdu7dy9jjE8oAmCvXr2SxQwZMoSJxWK5yTU3Nzc2ZMgQxhhjb9++Zfr6+uzjx49y+TVv3pxNnjxZdlwrK6tMP4OTkxPr0KGD3G0ZJxQnT57MSpQoIZv0/JHZs2ezli1byt32/v17BoA9f/5c9rw0bNhQLqZ27dps4sSJjDE+cWxjY8O2b98u+36PHj1Yt27dGGOMxcfHM7FYLJvcTTVgwADWo0cPxljmCcWePXuyFi1ayMWPHz+eVahQQe75aNWqlVxMt27dWOvWrRljjF2+fJlZWlpmmswuVaoU27Bhww+eGd2h7IQibb4h+dL378DAgfz68OFAkyaCpkPULDo6rcPf+PFA+fLC5kOIujx/DrRsCUREAPXrA4cPAyYmQmdFlBURAbRqBbx+zcuknTmje6UacouTkxOcnZ0hlUpRq1YtODk5yS4ODg5y3ewJSU8i4VudExOB1q2BPn2Ezoj8jJkzgcKF+d/H5cuFzobklk6dOuHTp084evQoWrVqBX9/f9SoUQNbt25VeL+AgAA0aNBA7rYGDRogICAgy/j79++jevXqsLa2zvaYzs7OcjUSHRwcEBYWlu3jGxgYoG7durLbChUqBBcXF7kcxGIxSpUqJfvazs4Ozs7OMDc3l7st9XEePXoEiUSCsmXLwtzcXHa5ePGiwm3JqWrVqqXw+/fv30ejRo1gaGj4w2MBwIMHD3DhwgW5XMqVKwcAcvlUqVJF7n7pnzsDAwN07doVO3fuBADExMTgyJEj6NWrFwDg1atXiI2NRYsWLeQeZ/v27dn+zNm9/i9fvpTbPp6xgWW9evVkr8+DBw8QHR2NQoUKyT1uYGCgUs81kUdVOkm+NHEi8PYt4OzMmxAQ3TJzJvD+PX/9qSMq0RVv3wKurkBYGFC9OnDihMDdSIlKYmOBdu2ABw8AOzvg7FnAwUHorDTT0aNH0bp1axgaGuLo0aMKY1PrbSlj3bp1WLduHYKCggAAFStWxPTp03O1GygR3ooVwPXrvLHHxo1Ue1bTFSgALFwI9O3L62r37AkUKyZ0VvmbWMw/nBficVVhYmKCFi1aoEWLFpg2bRoGDhwIb29v9O3bN9dyMjU1/WFMxkk2kUj0053XszqmoseJjo6Gvr4+7ty5k+kDs/STkNkx+8EJozLPQ3rR0dFo164dFixYkOl7DulObn703PXq1QuNGzdGWFgYzp07B1NTU7Rq1Ur2GABw4sSJTDWRc7OpTkbR0dFwcHCQ1adMT9ku2iQNTSiSfOfff3knPoB3d1ZiDCVa5NEj3gkV4E0oVD05IUQTBQcDzZsDHz4A5crxlW10TqM5kpKAbt2A//4DrKz461e6tNBZaa4OHTogJCQEhQsXRocOHbKNE4lEcisSfqRYsWKYP38+ypQpA8YYtm3bhvbt2+PevXuoWLFiLmROhPbyZdoHkYsX08STtujdm08OX70KeHkBu3cLnVH+JhJp5geSFSpUkGumYmhomGmML1++PK5cuQJ3d3fZbVeuXEGFChWyPGaVKlXwv//9D1+/flW4SlFZ5cuXR3JyMm7cuIH69esDAL58+YLnz59nm4MyqlevDolEgrCwMDTKpgupkZGRSn/z0qtSpQq2bduGpKQkpVYp1qhRAwcOHICzs/NPdYquX78+HB0dsWfPHpw6dQpdunSRPX6FChVgbGyMd+/eoXHjxkodL/X1T+/KlSsoW7as3ETs9evX5WKuX7+O8ilb3mrUqIGQkBAYGBjINfshOUMTiiRfiY1N2+o8dCjQrJmw+WTJwADw8OBXTUzgkXr9JwZbwkmlwLBhfLvSH3/wzqiEaLsvX4AWLdK2yZ4/D9jaCp3VT9CxMVIqBQYMAI4f59vTjx0DqlYVOivNln51w8+uEkmvXbt2cl/PnTsX69atw/Xr12lCUQtIpfwcMj6er/ZOPZ/MV3RsfMwtenr8Q+aaNYE9e4AhQ4CmTYXOiuTUly9f0KVLF/Tv3x9VqlSBhYUFbt++jYULF6J9+/ayOGdnZ/j5+aFBgwYwNjZGwYIFMX78eHTt2hXVq1eHq6srjh07hoMHD+L8+fNZPlaPHj0wb948dOjQAT4+PnBwcMC9e/dQpEiRTNtilVGmTBm0b98egwYNwoYNG2BhYYFJkyahaNGicrmrqmzZsujVqxf69OmDJUuWoHr16vj8+TP8/PxQpUoV/Pbbb3B2dkZ0dDT8/PxQtWpViMViiJVceTFixAisWrUK3bt3x+TJk2FlZYXr16+jTp06cHFxyRQ/fPhwbNq0CT169JB1cX716hV2796N//3vfyqVHenZsyfWr1+PFy9e4MKFC7LbLSws4OXlhbFjx0IqlaJhw4aIjIzElStXYGlpKTdpnGrcuHGoXbs2Zs+ejW7duuHatWtYvXo11q5dKxd35coVLFy4EB06dMC5c+ewb98+nDhxAgDg6uqKevXqoUOHDli4cCHKli2LT58+4cSJE+jYseMPt4+TDNRT0jH/ELpwLlFs8mRe1LdYMcbS1cYlOuJ//+Ovv7k5Y+/eqe9xaVxIQ8+FekVGMlarFv+9d3Dg3YGJ5pBKGRszhr9++vqMHT0qdEZ5QxvHheTkZLZr1y5mZGTEnjx5otR9tPF50CYbNvD/i2ZmjAUGCp0NyQvDh/PXuEIFxpTsLZHnhB4XlG2ckJ/Ex8ezSZMmsRo1ajArKysmFouZi4sLmzp1KouNjZXFHT16lJUuXZoZGBgwJycn2e1r165lJUuWZIaGhqxs2bJyTT8Yy9zMJSgoiHXq1IlZWloysVjMatWqxW7cuMEY401ZqlatKnf/ZcuWyT1exi7PX79+Zb1792ZWVlbM1NSUubm5sRcvXsi+n1XzlKwex93dnbVv3172dWJiIps+fTpzdnZmhoaGzMHBgXXs2JE9fPhQFjN06FBWqFAhBoB5e3szxngTkmXLlskdO2NTFsYYe/DgAWvZsiUTi8XMwsKCNWrUiL1WcOL54sUL1rFjR1agQAFmamrKypUrx8aMGcOkKe28Mz4vjDHWvn175u7uLnfb06dPGQDm5OQku28qqVTKli9fzlxcXJihoSGztbVlbm5u7OLFi4yxzE1ZGOMNfSpUqMAMDQ1Z8eLF2aJFi+SO6eTkxGbOnMm6dOnCxGIxs7e3ZytWrJCLiYqKYiNHjmRFihRhhoaGzNHRkfXq1Yu9U+cb0HxO2bFFxBhjAs1lCiIqKgpWVlaIjIyEpaWl0OmQdJ48AapVA5KTgUOHAAW7nIgWCg8HXFyAr1+BJUsAT0/1PTaNC2nouVCf2FjeMODSJaBQIf7vT+yWIQKYNy9te+W2bdrb/EHIcWHUqFEoXbo0RqV26kqxevVqvHr1CstV7NLw6NEj1KtXD/Hx8TA3N8c///yDNtksh09ISEBCQoLs66ioKDg6OtL4mA+FhfFziIgI3rhj9GihMyJ54ds3oGxZfs6o7nPF7Ah93hQfH4/AwECUKFECJtTFjRA4OztjzJgxGDNmjNCpaDRlxxbq8kzyBcb4VtfkZOD33/P5ZCJjwOfPwOfPYFIpPn/+jM+fP0PH5uZz3cSJfDKxSpW0Ds+EaKvERKBzZz6JaGnJa+5pzWSijoyRGzakTSYuW6a9k4lCO3DgQKaOjgCvy7R//36Vj+fi4oL79+/jxo0bGDZsGNzd3fH06dMsY318fGBlZSW7ODo6qvx4RD28vPhkYvXqwPDhQmejgI6Mj3mlYMG0Zo0zZvD6w4QQQoRDE4okX9i6Fbh8mTfgWLlS6Gx+IDYWKFwYKFwYseHhKFy4MAoXLozY2FihM9NY//0HbNnCr69bx0sMEaKtJBLgzz+BU6cAU1PezblmTaGzykU6MEbu28c/BAOAKVMA+hA873z58gVWVlaZbre0tER4eLjKxzMyMkLp0qVRs2ZN+Pj4oGrVqlixYkWWsZMnT0ZkZKTs8v79e5Ufj+S9CxeAHTt4I4r16/P5OYQOjI95rV8/oG5d4Pt3YPx4obMhhBDdRhOKRHDh4WknBDNmAE5OgqZD1CwpKe2N+cCBQErDNEK0EmO8mPy+fYChIS/v0LCh0FkRVZw7B/TqxV/LwYOBOXOEzki7lS5dGqdPn850+6lTp1CyZMmfPr5UKpXb1pyesbExLC0t5S4kf0lISDuHGDYMqFNH2HxI3tPTA1av5hPIO3fylf6EEJIqKCiItjurkcoTiuk782S0YcOGn0qG6KaJE3mX08qVaZWHLlq+HHj8GLCxSdvGosnc3d1xic5uSRYYA8aNAzZv5m+Idu0C3NyEzoqo4uZNoGNH/kFI587A2rX8TS3JO56enpgwYQK8vb1x8eJFXLx4EdOnT8ekSZMwduxYlY41efJkXLp0CUFBQXj06BEmT54Mf39/9OrVK4+yJ3lt8WLg+XPAzg6YO1fobIi61KrFP9ABgBEjeMkkQggh6qfyhGKrVq0wfvx4JCUlyW4LDw9Hu3btMGnSpFxNjmi/y5fTtrquX89X7BDd8fYtX5UKAIsW8cYUmi4yMhKurq4oU6YM5s2bh48fPwqdEsknZs3itfYAPqnYqZOw+RDVBATwJjoxMYCrK/D334C+vtBZab/+/ftjyZIl2Lx5M5o2bYqmTZvi77//xrp16zBo0CCVjhUWFoY+ffrAxcUFzZs3x61bt3DmzBm0aNEij7Ineen167QVwkuXAgUKCJoOUbO5cwFra+DRI2DNGqGzIYQQ3ZSjFYqHDh1C7dq18fTpU5w4cQKVKlVCVFQU7t+/nwcpEm2VmAgMHcqvDxpEW1110ejRvJxQo0aAu7vQ2eSOw4cP4+PHjxg2bBj27NkDZ2dntG7dGvv375f7IIboluXL0ybPV6wA+vYVMBmisnfvgJYteeOoOnX4VnVjY6Gz0h3Dhg3Dhw8fEBoaiqioKLx58wZ9ctAFZ/PmzQgKCkJCQgLCwsJw/vx5mkzUUIzxlWnx8UDz5kCPHkJnRNStUCFg3jx+ffp0ICRE2HwIIUQXqTyhWL9+fdy/fx+VKlVCjRo10LFjR4wdOxb+/v5wouJ3RAVLlwJPnwK2ttqx1ZWo5tgx4MgRXjx93Trt2jZoa2sLT09PPHjwADdu3EDp0qXRu3dvFClSBGPHjsXLly+FTpGoka8vkLozc9Ys6mKuaT5/5pOJHz4A5cvzJjrm5kJnpZtsbW1hTk8+AXDgAHD6NGBkRKUHdNnAgbypWVQUQBvlCCFE/XLUlOXFixe4ffs2ihUrBgMDAzx//py6kxGVBAbyN9YAr39jbS1sPkS9YmL4ygIA8PQEKlYUNp+8EhwcjHPnzuHcuXPQ19dHmzZt8OjRI1SoUAHLUve+Eq22fz9/wwPw+olTpwqbD1HN9+98m/Pz50Dx4sDZs7zeK1Gv/fv3o2vXrvjll19Qo0YNuQvRPVFRfIcDAEyeDJQtK2w+RDj6+mnbnbdtA65eFTYfQgjRNSpPKM6fPx/16tVDixYt8PjxY9y8eRP37t1DlSpVcO3atbzIkWiZ1G0qcXFA06ZA795CZ6QiAwO+P9fdHQYmJnB3d4e7uzsMDAyEzkxjzJ7NtxA6OfFtKtokKSkJBw4cQNu2beHk5IR9+/ZhzJgx+PTpE7Zt24bz589j7969mJU6o0601pkzQM+egFQKDBjA64TqxCoaLRkj4+OBDh2AO3f4JOLZs0CxYkJnpXtWrlyJfv36wc7ODvfu3UOdOnVQqFAhvHnzBq1btxY6PSKA6dOBT5+A0qU1cFWaloyP+UnduvxvLAAMHw5IJMLmQwghOoWpyN7enp08eVLutsTERObl5cWMjIxUPZzaRUZGMgAsMjJS6FR01v79jAGMGRkx9uyZ0NkQdXv8mDEDA/47cOSI0NlwuTkuFCpUiBUsWJB5eHiwe/fuZRnz7ds35uzs/NOPlRdojMwdly8zZmrKf8+7dmUsOVnojIgqkpMZ++MP/vqZmzN2+7bQGQlLyHHBxcWF/fPPP4wxxszNzdnr168ZY4xNmzaNDR8+XK250PgovDt3GNPT4/83z54VOhuSX4SFMVagAP+9WL1avY8t9LgQFxfHnj59yuLi4gR5fKH5+voyKyurXDteYGAgA5DtOby6j6MMb29vVrhwYQaAHTp0KM8fT0gXLlxgANi3b9+Uvk/jxo3Z6NGjFcY4OTmxZcuW5TivjK+3snn+6HHV+XuUkbJji8orFB89epTpE2FDQ0MsWrQIZ8+e/Zm5TaIDvn9Pqx82cSLg4iJsPkS9GAOGDQOSk4H27YHffxc6o9y3bNkyfPr0CWvWrEG1atWyjClQoAACAwPVmxhRm7t3gd9+46uwW7cGduygbsCahDHeMOzgQV6f7cgRXqOLCOPdu3eon9K1zdTUFN+/fwcA9O7dG7t27RIyNaJmEgn/vymVAt27A9RPh6SytU3r+D11KhAWJmw+RDkhISEYOXIkSpYsCWNjYzg6OqJdu3bw8/MTOjWV9O3bFx06dJC7zdHREcHBwahUqVKePnZAQABmzpyJDRs2IDg4mFbu5xP169dHcHAwrKysAABbt25FgQIFVD6Oun6PfobKE4o2CooHNW7c+KeSIdovdZtKqVK87o1GYowXAYyJAZNKERMTg5iYGDDGhM4s39u2Dbh8GRCLgZUrhc4mb/Tu3RsmJiZCp0EE8uwZ0KoVr/HVqBGvoWhkJHRWaqbhY+SUKcD//gfo6QG7dgHNmgmdkW6zt7fH169fAQDFixfH9evXAQCBgYEa8ztFcsfGjcCtW4ClJW/sp5E0fHzMz4YOBapVAyIiNPg9hg4JCgpCzZo18e+//2LRokV49OgRTp8+jaZNm2L48OFCp/fT9PX1YW9vn+flDF6/fg0AaN++Pezt7WFsbJwpJjExMU9zIJkZGRnB3t4eop+sdaSu36OfkaOmLITkxL17aZNIa9YApqbC5pNjsbG8xae5OWLDw2Fubg5zc3NqTPQDX74A48fz6zNm8AYHhGiTt2/5ipnPn4EaNXgnc7FY6KwEoMFj5OLFwPz5/PrGjcAffwibDwGaNWuGo0ePAgD69euHsWPHokWLFujWrRs6duwocHZEXUJC0iaJ5s0DHByEzSfHNHh8zO/SN2jZsgW4cUPYfIhiHh4eEIlEuHnzJjp16oSyZcuiYsWK8PT0lH1wBABLly5F5cqVYWZmBkdHR3h4eCA6OlrhsY8dO4batWvDxMQENjY2cn8rRCIRDh8+LBdfoEABbN26NctjSSQSDBgwACVKlICpqSlcXFywYsUK2fdnzJiBbdu24ciRIxCJRBCJRPD390dQUBBEIhHu378vi7148SLq1KkDY2NjODg4YNKkSUhOTpZ9v0mTJhg1ahQmTJgAa2tr2NvbY8aMGdn+nDNmzEC7du0AAHp6erLJq9QVk3PnzkWRIkXgkrIl8NGjR2jWrBlMTU1RqFAhDB48WO65TL3fvHnzYGdnhwIFCmDWrFlITk7G+PHjYW1tjWLFisHX11fh8y+VSrFw4UKULl0axsbGKF68OObOnQuA/00fkdqZM8Xnz59hZGQkW5makJCAiRMnwtHREcbGxihdujQ2b96c5WN9+fIFPXr0QNGiRSEWi1G5cuUsdy8kJydjxIgRsLKygo2NDaZNm6bwg5yIiAgMHDgQtra2sLS0RLNmzfDgwQOFP3d6/v7+EIlEiIiIgL+/P/r164fIyEjZ70j61zU2Nhb9+/eHhYUFihcvjo0bN8q+l/H3KKuVjocPH5abuJwxYwaqVauGLVu2oHjx4jA3N4eHhwckEgkWLlwIe3t7FC5cWPaa/Kz8O9VJtIpEAgwZwrepdOsGuLkJnRFRt0mTgPBwoFIlYMwYobMhJHeFhgKursCHD0D58rwhS8ouB6IhfH3TPvSYPz+tyD8R1saNGyGVSgEAw4cPR6FChXD16lX8/vvvGDJkiMDZEXUZNw6IjARq1eIr0QjJSv36vOfNtm28QcuNG7pdciQmJibb7+nr68vtqFEUq6enB9N0K0GyijUzM1M6r69fv+L06dOYO3dulvdLP2Gip6eHlStXokSJEnjz5g08PDwwYcIErF27NstjnzhxAh07dsRff/2F7du3IzExESdPnlQ6t4ykUimKFSuGffv2yf7+DB48GA4ODujatSu8vLwQEBCAqKgo2USbtbU1Pn36JHecjx8/ok2bNujbty+2b9+OZ8+eYdCgQTAxMZGbXNq2bRs8PT1x48YNXLt2DX379kWDBg3QIosaD15eXnB2dka/fv0QHBws9z0/Pz9YWlri3LlzAPhr5ubmhnr16uHWrVsICwvDwIEDMWLECLnJ1H///RfFihXDpUuXcOXKFQwYMABXr17Fr7/+ihs3bmDPnj0YMmQIWrRogWLZdKqbPHkyNm3ahGXLlqFhw4YIDg7Gs2fPAED2mEuWLJGtpvz7779RtGhRNEvZEtKnTx9cu3YNK1euRNWqVREYGIjw8PAsHys+Ph41a9bExIkTYWlpiRMnTqB3794oVaoU6tSpI/e8DhgwADdv3sTt27cxePBgFC9eHIMGDcryuF26dIGpqSlOnToFKysrbNiwAc2bN8eLFy9gbW2d5X2yU79+fSxfvhzTp0/H8+fPAQDm5uay7y9ZsgSzZ8/GlClTsH//fgwbNgyNGzeWTQTnxOvXr3Hq1CmcPn0ar1+/RufOnfHmzRuULVsWFy9exNWrV9G/f3+4urqibt26OX4cAKo3ZdF0QhfO1VVr1/JCyZaWjH36JHQ2Pyk6mv8wAIsODWUAGAAWHR0tdGb51pUrsqeMXb4sdDaZ0biQhp4L1X39yliVKvz329mZsQ8fhM5IYBo4Rh46lNboYdw4xqRSoTPKX2hc4Oh5EMa5c/z/pp6eFjRI0sDxUdOEhDBmZcWf5vXr8/7xhB4XFDVOSP39yurSpk0buVixWJxtbOPGjeVibWxsMsWo4saNGwwAO3jwoMo/7759+1ihQoVkX2dsylKvXj3Wq1evbO+PLBqXWFlZMV9fX8aYck0whg8fzjp16iT72t3dnbVv314uJuNxpkyZwlxcXJg03QnGmjVrmLm5OZNIJIwx3jykYcOGcsepXbs2mzhxYra5HDp0KNPz7+7uzuzs7FhCQoLsto0bN7KCBQvKjTUnTpxgenp6LCQkRHY/JycnWT6M8cZojRo1kn2dnJzMzMzM2K5du7LMJyoqihkbG7NNmzZl+f24uDhWsGBBtmfPHtltVapUYTNmzGCMMfb8+XMGgJ07dy7L+yvT7OS3335j48aNk33duHFjVr58ebnnfuLEiax8+fKyr9M3R7l8+TKztLRk8fHxcsctVaoU27BhQ5aP+aOmLNk1D3JycmJ//vmn7GupVMoKFy7M1q1bl+VxszpOxt8Bb29vJhaLWVRUlOw2Nzc35uzsnOm19fHxyfLnYSwPm7IQoiqt2aZCciQ5mTdiAYD+/YGGDYXNh5DcFB3NG7A8fAjY2wPnzgFFiwqdFVGFvz9v8CCVAv36AYsWAT9Z8obksm/fvmHx4sUYMGAABgwYgCVLlsjqKhLtFh8PeHjw68OHU4Mk8mN2dsCsWfz6lCm85A7JX5gKNUPPnz+P5s2bo2jRorCwsEDv3r3x5cuXbMsE3L9/H82bN8+tVAEAa9asQc2aNWFrawtzc3Ns3LgR7969U+kYAQEBqFevntzW1AYNGiA6OhofPnyQ3ValShW5+zk4OCAsB12GKleuDKN0RbwDAgJQtWpVuRWhDRo0gFQqla2aA4CKFStCTy9tisjOzg6VK1eWfa2vr49ChQplm1NAQAASEhKyfQ1MTEzQu3dvbNmyBQBw9+5dPH78GH379gXAXz99fX2le3NIJBLMnj0blStXhrW1NczNzXHmzJlMr88vv/wi99zXq1cPL1++hEQiyXTMBw8eIDo6GoUKFZKVpTA3N0dgYKCsZmVuSv+ai0Qi2Nvb5+g1T8/Z2RkWFhayr+3s7FChQoVMr+3PPg5AW56JGowdS9tUdNnKlXyyxdoaWLBA6GwIyT0JCUDHjsC1a0DBgsDZs0Dp0kJnRVRx7x7vNp+QwDvPb9xIk4n5zaVLl/D777/D0tIStWrVAgCsXLkSs2bNwrFjx/Drr78KnCHJSwsXAi9f8g+jZ88WOhuiKTw8eHOtR4/4pOKGDUJnJAxFtQb1M+wFVzSxkH4SAuB13X5GmTJlIBKJZNtgsxMUFIS2bdti2LBhmDt3LqytrfHff/9hwIABSExMhDiLQtWmPyjSLxKJMk1oJiUlZRu/e/dueHl5YcmSJahXrx4sLCywaNEi3MijIp2GhoaZ8k0t+6EKVbag/+jxVcnpR88/wLc9V6tWDR8+fICvry+aNWsGJycnpe+f3qJFi7BixQosX75cVmtzzJgxP9WIJjo6Gg4ODvD398/0vZx0av4RVZ5fPT09pX5/f/Z1VAWtUCR56vRpYPdu3i1zwwbdrmOii96/5529Af6mQEGTeEI0SnIy0KMHcP48r69/+jSQ7gNcogFevOD1fL9/Bxo35n+r8nETPZ01fPhwdO3aFYGBgTh48CAOHjyIN2/eoHv37lrRCZRk78ULvrMFAJYvp7q0RHkGBsDq1fz6pk3A7dvC5iMUMzOzbC/p6yf+KDbjJE9WMaqwtraGm5sb1qxZk2U9xoiICADAnTt3IJVKsWTJEvzyyy8oW7ZsptqEGVWpUkXW3CMrtra2cvUGX758qbAp0pUrV1C/fn14eHigevXqKF26dKZVakZGRlmudEuvfPnyuHbtmtxk0JUrV2BhYZFtLcLcVL58eTx48EDu+b5y5Qr09PR+qlZfRmXKlIGpqanC16By5cqoVasWNm3ahH/++Qf9+/eX+55UKsXFixeVerwrV66gffv2+PPPP1G1alWULFkSL168yBSXcQL4+vXrKFOmTKaJdQCoUaMGQkJCYGBggNKlS8tdbHL4ZlaZ3xFl2Nra4vv373KvY/rGP0KgCUWSZ2Jj07a6jh7Nu54S3TJmDBATAzRowLcSEqINpFK+ff/QIcDYGDhyBEhX95logI8fgZYteUfu6tX5a5jhvRXJJ169eoVx48bJnfTr6+vD09MTr169EjAzkpcY4+eQCQl84r9LF6EzIprm11+BXr3479KIEfxvN8k/1qxZA4lEgjp16uDAgQN4+fIlAgICsHLlStSrVw8AULp0aSQlJWHVqlV48+YNduzYgfXr1ys8rre3N3bt2gVvb28EBATg0aNHWJBui1SzZs2wevVq3Lt3D7dv38bQoUMzrdxKr0yZMrh9+zbOnDmDFy9eYNq0abh165ZcjLOzMx4+fIjnz58jPDw8yxVjHh4eeP/+PUaOHIlnz57hyJEj8Pb2hqenZ6YVoHmhV69eMDExgbu7Ox4/fowLFy5g5MiR6N27N+zs7HLtcUxMTDBx4kRMmDAB27dvx+vXr3H9+vVMXZoHDhyI+fPngzEm14Xb2dkZ7u7u6N+/Pw4fPozAwED4+/tj7969WT5emTJlcO7cOVy9ehUBAQEYMmQIQkNDM8W9e/cOnp6eeP78OXbt2oVVq1Zh9OjRWR7T1dUV9erVQ4cOHXD27FkEBQXh6tWr+Ouvv3A7h59OODs7Izo6Gn5+fggPD1c4ia1I3bp1IRaLMWXKFLx+/Rr//PNPth3K1YUmFEmemTkTCAoCihdPq2WiFfT1gc6dgc6doW9khM6dO6Nz585ZfsKhy06cAA4e5E/XunV8lSohmo4xYNQoYMcO/ru9dy+Q0pSOpMrnY+TXr3wy8e1boEwZvrqUVj7lXzVq1EBAQECm21PrQRHt9PffwL//8on+tWu1qBRBPh8ftc2iRYCFBe/2nNKAl+QTJUuWxN27d9G0aVOMGzcOlSpVQosWLeDn54d169YBAKpWrYqlS5diwYIFqFSpEnbu3AkfHx+Fx23SpAn27duHo0ePolq1amjWrBlu3rwp+/6SJUvg6OiIRo0aoWfPnvDy8spy63SqIUOG4I8//kC3bt1Qt25dfPnyBR6phV1TDBo0CC4uLqhVqxZsbW1x5cqVTMcpWrQoTp48iZs3b6Jq1aoYOnQoBgwYgKlTp6rytOWYWCzGmTNn8PXrV9SuXRudO3dG8+bNsTp1KW8umjZtGsaNG4fp06ejfPny6NatW6Yt9T169ICBgQF69OiRabXsunXr0LlzZ3h4eKBcuXIYNGhQtl3Ip06diho1asDNzQ1NmjSBvb09OnTokCmuT58+iIuLQ506dTB8+HCMHj0agwcPzvKYIpEIJ0+exK+//op+/fqhbNmy6N69O96+fZvjydf69etj6NCh6NatG2xtbbFw4cIcHcfa2hp///03Tp48icqVK2PXrl1yXcIFobBli5qsXr2aOTk5MWNjY1anTh1248YNpe63a9cuBiBTVyVFhO7EpSvu32dMX593WDt2TOhsiLrFxPButwBj48cLnc2P5edxQZ3jI2P5+7nID6ZM4b/XIhFjO3cKnQ1RVXQ0Y7/8wl/DIkUYCwwUOiPNIOS4sHv3bla8eHG2aNEidvnyZXb58mW2aNEi5uzszHbv3s0ePHggu+Q1Gh/VIzycMRsb/v9UQQNKQpSyZAn/XbKxYezLl9w/vtDjgrKdWAnJTwIDA5menh67c+eO0KmQbCg7tgheLWjPnj3w9PTE+vXrUbduXSxfvhxubm54/vw5ChcunO39goKC4OXlhUaNGqkxW6IMiQQYPJj/27kz0Lat0BkRdZs7l69OdXRMq6FIVEfjY/6ycGFaPa9164CePYXNh6gmMRHo1Am4fj2tiY6zs9BZkR/p0aMHAGDChAlZfi+1wL5IJMqV+kREeBMnAuHhQKVKwLhxQmdDNN3IkcCWLcCTJ8C0acCaNUJnRIjuSkpKwpcvXzB16lT88ssvqEE10TSe4JsQly5dikGDBqFfv36oUKEC1q9fD7FYLGslnhWJRIJevXph5syZKFmypBqzJcpYvx64eROwtARWrBA6G6JuAQF8iwnAOzybmwubjyaj8TH/WL+ev8kFeLfyIUOEzYeoRiIB+vQBzpwBxGJekqFiRaGzIsoIDAxUeHnz5o3sX6L5Ll0CUkttbdgAKChtRohSDA3TGrSsXw/cuydsPoTositXrsDBwQG3bt36YT1MohkEXaGYmJiIO3fuYPLkybLb9PT04OrqimvXrmV7v1mzZqFw4cIYMGAALl++rPAxEhISkJCQIPs6Kirq5xMn2fr4EUh9OX18gCJFhM0nT8TEyGbJYkJDYZ5SSyE6OlrlLmfaJrWIelIS0K4d0L690BlpLnWMjwCNkcr45x8gtVzOlClAFgulSHr5bIxMrXu5Zw9/Y3nwIJBS751oACcnJ6FTIGqSkJD2Yc3gwUD9+sLmkyfy2fioK5o0Abp3B3bvBoYPB/77j2p7EyKEJk2ayHW6JppP0AnF8PBwSCSSTMUt7ezs8OzZsyzv899//2Hz5s1Kt8f28fHBzJkzfzZVoqRRo4Dv34FffgGGDhU6G6JuO3YAFy8CpqZ8daLWFFEXgDrGR4DGyB85epSvbGOMvwmZM0fojIiqZs5Ma+qwfTvvGEs0z9OnT/Hu3TskJibK3f77778LlBHJbYsWAc+eAYULA/PnC50N0TaLFgHHjgHXrvHzVXd3oTMihBDNJ3gNRVV8//4dvXv3xqZNm2BjY6PUfSZPngxPT0/Z11FRUXB0dMyrFHXakSN85YeBAbBxI33yp2u+fQO8vPj16dOpNpm65WR8BGiMVOTff4GuXdO2y9IkueZZtYpPKAK8blb37sLmQ1T35s0bdOzYEY8ePZLVSwR4F0YAVDdRS7x6lfaBzbJlvM4pIbmpWDF+fjpxIt9p0L49UKCA0FkRQohmE3RC0cbGBvr6+ggNDZW7PTQ0FPb29pniX79+jaCgILRr1052m1QqBQAYGBjg+fPnKFWqlNx9jI2NYWxsnAfZk/S+fk1bkejlBVSuLGw+RP0mTwY+fwYqVADSzU+RHFLH+AjQGJmdGzeA33/nW/A6duQ1vehDEs2ycydfNQ/wScVhw4TNh+TM6NGjUaJECfj5+aFEiRK4efMmvnz5gnHjxmHx4sVCp0dyAWN8q3NCAtCiBZDSh4eQXDdmDODry1fCentTrXdCCPlZgr49MjIyQs2aNeHn5ye7TSqVws/PD/WyKHBUrlw5PHr0CPfv35ddfv/9dzRt2hT379+nVTUCGjMGCAkBypXjf6CJbrl+nRdPB3j3WyMjYfPRBjQ+CufhQ6B1a17qqkULYNcuvvKaaI6TJ4G+ffn1kSN5Z0+ima5du4ZZs2bBxsYGenp60NPTQ8OGDeHj44NRqTPGRKNt2sRXhJua8nMIWglO8oqREV+5DvBGLQ8fCpsPIYRoOsHfInl6esLd3R21atVCnTp1sHz5csTExKBfv34AgD59+qBo0aLw8fGBiYkJKlWqJHf/Ailr1TPeTtTn2DFei0RPj3/qZ2IidEZEnZKT01anursDv/4qbD7ahMZH9Xv5EmjZkm/hr18fOHQIoAWcmuXKFaBzZz429ewJLF9OExSaTCKRwMLCAgBfuf3p0ye4uLjAyckJz58/V/o4Pj4+OHjwIJ49ewZTU1PUr18fCxYsgIuLS16lTpTw/n1auZS5c4EsFtITkqtcXfnfiP37eW3kS5fobwQhhOSU4BOK3bp1w+fPnzF9+nSEhISgWrVqOH36tKwRwbt376BH+8zyrW/f0jryjRvHm7EQ3bJ6NfDgAa93tGiR0NloFxof1evDB/5GIzQUqFoVOHECoKabmuXhQ6BtWyAujq8y3bqVtqprukqVKuHBgwcoUaIE6tati4ULF8LIyAgbN25EyZIllT7OxYsXMXz4cNSuXRvJycmYMmUKWrZsiadPn1J3XYGkbnX+/p13XqcFp0Rdli7lK9n/+4+Xx/jzT6EzIoQQDcV0TGRkJAPAIiMjhU5FK7i7MwYwVrYsY7GxQmejJnFxjLVpw1ibNizu2zfWpk0b1qZNGxYXFyd0Zmr3/j1j5ub8d2DjRqGzyTkaF9Lo6nMRFsZYuXJp41loqNAZaTCBxsjXrxmzt+evYYMGjMXE5OnD6RQhx4XTp0+zAwcOMMYYe/nyJXNxcWEikYjZ2NgwPz+/HB83LCyMAWAXL15U+j66Oj7mla1b+f9XY2PGAgKEzkZN6Bwy35g3j//+2dsz9jP/pYUeF+Li4tjTp0919nfI19eXWVlZ5drxAgMDGQB27969fHEcZXh7e7PChQszAOzQoUN5/nh5zd3dnbVv3172dePGjdno0aMFyyc3qPP3IbcoO7YIvkKRaK4TJ4Bt2/g2AV9fXvtGJ5iY8B8egAmAEynXddHYsUB0NF9ZMGCA0NkQkjNRUXw127NngKMjcO4cULiw0FlpMAHGyJAQXu8yJIQ3BTt2DBCL8/xhiRq4ubnJrpcuXRrPnj3D169fUbBgQVmn55yIjIwEAFhbW/90jkR1wcG8/jYAzJjBa3DrBDqHzDc8Pfn7l5cveeOuJUuEzkj3hISEYO7cuThx4gQ+fvyIwoULo1q1ahgzZgyaN28udHpK69u3LyIiInD48GHZbY6OjggODoaNjU2ePnZAQABmzpyJQ4cO4ZdffkHBggXz9PFIzmT8ffD390fTpk3x7ds3WYkqTUUTiiRHIiKAwYP59bFjea0xoltOneL1Z/T1gfXraVsh0Uxxcbyb8507gK0tn0wsXlzorIgqIiKAVq2AN2+AEiWAM2d4CQaiHSIjIyGRSOQm/qytrfH161cYGBjA0tJS5WNKpVKMGTMGDRo0UFhjNiEhAQkJCbKvo6KiVH4skhljvOt6RARQs2ZaDUVC1MnYGFi5kn+guGIF0L8/ULGi0FnpjqCgIDRo0AAFChTAokWLULlyZSQlJeHMmTMYPnw4nj17JnSKP0VfXx/29vZ5/jivX78GALRv3z7bD9kSExNhRB0zBaWu3wch0BQAyRFPT+DTJ6BMGWD2bKGzIeoWFweMGMGvjx4NVKkibD6E5ERSEtC1K3DxImBpCZw+DVB/Bs0SGwu0a8fruNrZ8QlhBwehsyK5qXv37ti9e3em2/fu3Yvu3bvn6JjDhw/H48ePszxuej4+PrCyspJdHB0dc/R4RN6ePcCRI4ChIV8hZkDLG4hAWrUCOnQAJBJ+XsuY0BnpDg8PD4hEIty8eROdOnVC2bJlUbFiRXh6euL69euyuKVLl6Jy5cowMzODo6MjPDw8EB0drfDYx44dQ+3atWFiYgIbGxt07NhR9j2RSCS3khDgTQy3bt2a5bEkEgkGDBiAEiVKwNTUFC4uLlixYoXs+zNmzMC2bdtw5MgRiEQiiEQi+Pv7IygoCCKRCPfv35fFXrx4EXXq1IGxsTEcHBwwadIkJCcny77fpEkTjBo1ChMmTIC1tTXs7e0xY8aMbH/OGTNmoF27dgAAPT092YRi37590aFDB8ydOxdFihSRNR979OgRmjVrBlNTUxQqVAiDBw+Wey5T7zdv3jzY2dmhQIECmDVrFpKTkzF+/HhYW1ujWLFi8PX1Vfj8S6VSLFy4EKVLl4axsTGKFy+OuXPnyr7//v17dO3aFQUKFIC1tTXat2+PoKAghcf8EUWv+Y4dO1CrVi1YWFjA3t4ePXv2RFhYmOz7/v7+EIlEOHHiBKpUqQITExP88ssvePz4sSzmy5cv6NGjB4oWLQqxWIzKlStj165dSv/c6X8fgoKC0LRpUwCQ7bbo27cvtm/fjkKFCsl9kAkAHTp0QO/evX/q+clLNKFIVHb4MD8BFImALVt0cFtZTAzv1GBmhpiwMJiZmcHMzAwxMTFCZ6Y28+bx1UDFivGtSoRoGqkU6NsXOH6c70A7dgyoUUPorLSEmsbIpCSgWzdeVN/Kiq9MpA6x2ufGjRuyE+/0mjRpghs3bqh8vBEjRuD48eO4cOECihUrpjB28uTJiIyMlF3ev3+v8uMReZ8/AyNH8ut//cVLFOgUOofMd5Yt4+cB/v7A3r1CZ5PLYmKyv8THKx8bF/fjWBV8/foVp0+fxvDhw7NsipV+C6ienh5WrlyJJ0+eYNu2bfj3338xYcKEbI994sQJdOzYEW3atMG9e/fg5+eHOnXqqJRfelKpFMWKFcO+ffvw9OlTTJ8+HVOmTMHelF8WLy8vdO3aFa1atUJwcDCCg4NRP4utex8/fkSbNm1Qu3ZtPHjwAOvWrcPmzZsxZ84cubht27bBzMwMN27cwMKFCzFr1iycO3cuy9y8vLxkk3upj53Kz88Pz58/x7lz53D8+HHExMTAzc0NBQsWxK1bt7Bv3z6cP38eI1JXiKT4999/8enTJ1y6dAlLly6Ft7c32rZti4IFC+LGjRsYOnQohgwZgg8fPmT7nE2ePBnz58/HtGnT8PTpU/zzzz+yhpJJSUlwc3ODhYUFLl++jCtXrsDc3BytWrVCYmKiEq9IZj96zZOSkjB79mw8ePAAhw8fRlBQEPr27ZvpOOPHj8eSJUtw69Yt2Nraol27dkhKSgIAxMfHo2bNmjhx4gQeP36MwYMHo3fv3rh586ZSP3d6jo6OOHDgAADg+fPnCA4OxooVK9ClSxdIJBIcPXpUFhsWFoYTJ06gf//+OXpu1EJNNR3zDaEL52q6oCDGChTgRYzHjRM6G4FER/MnAGDRoaEMAAPAoqOjhc5MLQICGDMy4k9BSp18jUfjQhpdeC6kUsaGD+e/wwYGjB0/LnRGWkYNY6REwljv3vxhTEwYu3Qp1w5NsiDkuCAWi9nDhw8z3f7w4UNmamqq9HGkUikbPnw4K1KkCHvx4kWOctGF8TEvSaWM/f47/39bpQpjCQlCZyQAHT+HzK9mzuQvS9GijH3/rtp9hR4XFDZOSPldy/LSpo18rFicfWzjxvKxNjaZY1Rw48YNBoAdPHhQtR+WMbZv3z5WqFAh2dcZm7LUq1eP9erVK9v7I4vGJVZWVszX15cxplzzjOHDh7NOnTrJvs7YRCSr40yZMoW5uLgwqVQqi1mzZg0zNzdnEomEMcabjzRs2FDuOLVr12YTJ07MNpdDhw6xjFM67u7uzM7OjiWkG2Q3btzIChYsKDfWnDhxgunp6bGQkBDZ/ZycnGT5MMaYi4sLa9Sokezr5ORkZmZmxnbt2pVlPlFRUczY2Jht2rQpy+/v2LEj0/OQkJDATE1N2ZkzZ2R5qNKU5UeveUa3bt1iANj3lP/sFy5cYADY7t27ZTFfvnxhpqambM+ePdke57fffmPjUiZEfvRzZ/x9SH3Mb9++ycUNGzaMtW7dWvb1kiVLWMmSJeWeL3VRtikLrVAkSktKArp35zVv6tThq9SIbmEM8PAAEhOBNm2AdKvJCdEY3t7AmjV8lfX27cBvvwmdEVEFY7zm2o4dvIbrvn1Ao0ZCZ0XySp06dbBx48ZMt69fvx41a9ZU+jjDhw/H33//jX/++QcWFhYICQlBSEgI4jKuvCF5ZtUq4OhRwMgI2LqV/0tIfjBhAlCyJPDxI5VyUgemwt7y8+fPo3nz5ihatCgsLCzQu3dvfPnyBbGxsVnG379/P9cbuqxZswY1a9aEra0tzM3NsXHjRrx7906lYwQEBKBevXpydQ4bNGiA6OhoudV+VTLUkXJwcJDbnqusypUry9VNDAgIQNWqVeVWhDZo0ABSqRTPnz+X3VaxYkXopSuMb2dnh8rplpLr6+ujUKFC2eYUEBCAhISEbF+DBw8e4NWrV7CwsIC5uTnMzc1hbW2N+Ph4WT1IVf3oNb9z5w7atWuH4sWLw8LCAo0bNwaATK9hvXr1ZNetra3h4uKCgIAAAHzr++zZs1G5cmVYW1vD3NwcZ86ckR3jRz+3sgYNGoSzZ8/i48ePAICtW7eib9++P9WELq9R1RKitL/+Aq5f51vL9uyhE0Fd9M8/wIULvKP36tV8QoYQTbJsWdqbhTVrgB49hM2HqG7+fP46Arz8Rtu2wuZD8tacOXPg6uqKBw8eyE7U/fz8cOvWLZw9e1bp46xbtw4A3yqdnq+vb5Zbn0juunMnrfnK4sVA9erC5kNIeiYmvDFLu3bA0qVAv35a0nlcUa1BfX35rxVNWmXsvPiT9e7KlCkDkUj0w8YrQUFBaNu2LYYNG4a5c+fC2toa//33HwYMGIDExESIs6i7ZWpqqvCYIpEo04Rm6rbWrOzevRteXl5YsmQJ6tWrBwsLCyxatChHJTeUYWhomClfqVSq8nGy2kqe08dXJacfPf/R0dGoWbMmdu7cmel7tra2Kmb748dM3ert5uaGnTt3wtbWFu/evYObm5tKW6wXLVqEFStWYPny5bKanmPGjJEd40c/t7KqV6+OqlWrYvv27WjZsiWePHmCEydO5Mqx8wqtUCRKOXkSWLSIX/f1BZydBU2HCODbN96MBwCmTuXdVAnRJNu2pf0Oz5nDu4wSzbJxIzBlCr++bBmQj2tUk1zSoEEDXLt2DY6Ojti7dy+OHTuG0qVL4+HDh2ikwtJUxliWF5pMzHtRUbzeaVISb4CRoWQXIflC27Z8x0JyMq/zqRUNWlLqdWZ5MTFRPjbjZElWMSqwtraGm5sb1qxZk2X90IiICAB8ZZlUKsWSJUvwyy+/oGzZsvj06ZPCY1epUgV+fn7Zft/W1lau1uDLly+zXe0IAFeuXEH9+vXh4eGB6tWro3Tp0plW0hkZGUEikSjMq3z58rh27ZrcZOaVK1dgYWHxw3q+uaF8+fJ48OCB3PN95coV6OnpyZq25IYyZcrA1NQ029egRo0aePnyJQoXLozSpUvLXaysrHL0mIpe82fPnuHLly+YP38+GjVqhHLlymW7ujJ9M6Bv377hxYsXKF++PAD+XLVv3x5//vknqlatipIlS+LFixdK/9wZpa4ezer3ZuDAgdi6dSt8fX3h6uqa7xvC0YQi+aEPH4A+ffj1kSNpm6uu+usv/uFl+fJpqwwI0RSHDwMDBvDrnp5pk1JEc+zfDwwdyq9PmQKMGSNoOkSNqlWrhp07d+LJkye4ffs2tmzZgjJlygidFlECY8CQIcDr10Dx4ryZH+1uIPnVihWAsTFw/jxw8KDQ2Wi3NWvWQCKRoE6dOjhw4ABevnyJgIAArFy5Urb1tHTp0khKSsKqVavw5s0b7NixA+vXr1d4XG9vb+zatQve3t4ICAjAo0ePsGDBAtn3mzVrhtWrV+PevXu4ffs2hg4dmmkFXnplypTB7du3cebMGbx48QLTpk3DrVu35GKcnZ3x8OFDPH/+HOHh4VmuePTw8MD79+8xcuRIPHv2DEeOHIG3tzc8PT3lthjnlV69esHExATu7u54/PgxLly4gJEjR6J3795ZNg7JKRMTE0ycOBETJkzA9u3b8fr1a1y/fh2bN2+W5WFjY4P27dvj8uXLCAwMhL+/P0aNGqWw0Ysiil7z4sWLw8jISPY7dPToUczOpq7BrFmz4Ofnh8ePH6Nv376wsbFBhw4dAPDfg3PnzuHq1asICAjAkCFDEBoaqvTPnZGTkxNEIhGOHz+Oz58/y3Xb7tmzJz58+IBNmzbl72YsKWhCkSiUnAz07Al8+cI7oKauUiS65eZNIPXv99q1tN2daJZ//+WrYyQSvo1p8WJ6Q6tpzp3jf4tSJycyNEUkhORTmzcDu3fz3ZW7dgEFCwqdESHZK1WK11MEgLFjVW5eTFRQsmRJ3L17F02bNsW4ceNQqVIltGjRAn5+frISFVWrVsXSpUuxYMECVKpUCTt37oSPj4/C4zZp0gT79u3D0aNHUa1aNTRr1kyuE++SJUvg6OiIRo0aoWfPnvDy8spy63SqIUOG4I8//kC3bt1Qt25dfPnyBR4eHnIxgwYNgouLC2rVqgVbW1tcuXIl03GKFi2KkydP4ubNm6hatSqGDh2KAQMGYOrUqao8bTkmFotx5swZfP36FbVr10bnzp3RvHlzrF69Otcfa9q0aRg3bhymT5+O8uXLo1u3brJVgWKxGJcuXULx4sXxxx9/oHz58hgwYADi4+NhaWmZo8dT9Jrb2tpi69at2LdvHypUqID58+dj8eLFWR5n/vz5GD16NGrWrImQkBAcO3ZMtpJw6tSpqFGjBtzc3NCkSRPY29vLJhuV+bkzKlq0KGbOnIlJkybBzs5Ortu2lZUVOnXqBHNz80yPkR+JmCpVUbVAVFQUrKysEBkZmeNfWl0ydSowdy5gYQHcvQuULi10RvlAXBzQujW/evAgWv/xBwDg1KlTuVY/IT9JTuZNeO7d4ytVt20TOqPcR+NCGm17Lm7dApo142WEOnYE9u4FDKh6cN7K5THy5k3+GsbEAF268EmJjKWfSN7StnEhp+h5UM2TJ0Dt2nxI8PEBJk0SOqN8QMfOITVRbCxQoQLw9i1fDT93ruJ4oceF+Ph4BAYGokSJEjDJuI2ZEPJD/v7+aNq0Kb59+4YCBQoInQ4AoHnz5qhYsSJWrlwpWA7Kji30topk68iRtD+imzbRZKKMqSng78+vgg9C2mztWj6ZWLAgrVAlmuXJE/6+LToaaN6cNxWiyUQ1yMUxMiCAv4YxMUCLFmmdnQkh+VtsLNC1K58/a9kybdWXztOxc0hNJBbzGr1//MF3NPTtC1CFBUKIOnz79g3+/v7w9/fH2rVrhU5HKbTlmWTpyRPgzz/59REj+HZBons+feKrVAG+uqBwYWHzIURZr14Brq68XEOdOsChQ5nrj5P87e1bPon49St/DQ8e5LWtCCH5G2PAwIHA06eAvT3/IEANJcIIyTUdOgBubkBiIjBqlJY0aCGE5HvVq1dH3759sWDBglxtlpOXaK0GyeTrV6B9e76qp2lTYOlSoTMiQhk7Fvj+HfjlF2DQIKGzIUQ579/zFYkhIUDlysCpU7xsA9EcQUH8zdzHj7wR1MmTgLm50FkRQpSxaBEvTWBgwOsn0oeRRNOIRMDKlUClSsDp08DRo/y9ESFE+zRp0gT5pQpgUFCQ0CmojCYUiZzkZKB7d96Nz9mZ1xtT0PhKN8XE8CcHQMyTJ3CuWBEAHwDMzMwETCx3nTnDX389PWDdOlpdQDRDaChfmfjuHd+idO4cYG0tdFY65ifGSMaArVuB0aP5hxnFiwNnzwKFCuVxziRf+SOlrpwyDlIr1nzl5Mm0WokrVgCNGwubT76jI+eQ2qBsWcDLi+/QGTOGb92nMpeEECKPJhSJnAkT+BtwMzNeQ9HGRuiM8qnw8HRXwxUEaqa4OGD4cH591CigWjVB0yFEKV+/8hP+Fy/4RNT584CdndBZ6agcjJGhocDgwXwlCAA0aAD8/TdQrFheJEjyMysrK6FTIDnw/DnQowf/YGDwYGDYMKEzyqe0/BxSm/z1F/87FBQEzJ8PzJwpdEaEEJK/0IQikdm2jRchTr1epYqw+RDhzJ/PV6kWLQrMmiV0NkQIMZ8/Qz8+PtPt+kZGMEnXAS0mLCzbY+gZGMA03fJAVWJjw8PBpNIsY0V6ehCn+7QjNjwc36Ok6NQJePUQcLYFju4BCpkAseHysXFfv0KanJxtHmbp9uapEhsfEQFJYmKuxIptbCBKWRKcEBWF5Cxeh5zEmlpbQy+lK01idDSSYmNzJdakQAHoGxmlxX7+jNR1NjGfP8viYsLCYFK0qCw2KTYWidHRAIATJ4Dx44HwL4CVATB5MjBmoiWMzUwyxWbF2NISBilFMpPj45EQFZVtrJG5OQzFYpVjJYmJiI+IyDbWUCyGUcq+bFVipcnJiPv6NVdiDUxMYJzSZZRJpYhVMFmhSmy8gt+rvODr66vWxyM/LyIC+P13ICqKfxiwahXfNkqIJjMz46WfunQBFiwA+vQBSpUSOqus5Zctm4QQ7aD0mMJ0TGRkJAPAIiMjhU4lX7l+nTFjY8YAxqZPFzqbfC46mj9RAIsODWUAGAAWHR0tdGa54vlzxoyM+I+4b5/Q2agHjQtpZM9Fyu94xstNW1u5+Ohs4hjA7llZycV+FomyjX0iFsvFvtfXzzb2pbGxXOxLI+NsY9/r68vFPhGLs439LBLJxd6zsso2NjrDn8+btrbZxrIMsVeLFlUYGx0aKou9XKqUwtjPT5/KYv0rVVIYu2/RZebry5iPD2Pb7GopjPVocpj17s1Y796MrS3cWGHsqHpb2Z9/Mvbnn4wtK9Im0/MkGyMB5lljmSx2QfEuCo9709s77XkYMEBh7NWxY9Oe37FjFcZeHjAg7XXz9lYY69+lS9rvw7JlCmMvtGmT9nu2davi2MaN035/Dx9WHFurVtr/i8uXFedbqZIs9vPTp4qfh1KlZLHRoaEKY885ODAaI+lvRXaSkxlrk/Jfv1gxxkJChM4oH9Pyc0htJJUy5urKX7Z27TJ/X+hxITk5mT19+pSFh4cL8viEEO0UHh7Onj59ypKTkxXG0QpFgpAQ4I8/gIQE3tXM21vojIhQGAM8PHhXu1atgE6dhM6IEMUSEoCk7BcRknS8xgNvU64v/EGsvz/wNOV6yR/EXr0G3L7Gr9v/IPbOXeDiXX7d4wexRHdVr14dIiWXt929ezePsyE/8tdfvHaiiQlw+DCVmiDaJbVBS5UqwLFjfEX9b78JnVUafX19FChQAGEpu0DEYrHS4ychhGTEGENsbCzCwsJQoEAB6OvrK4wXMcaYmnLLF6KiomBlZYXIyEhYpmz10WVJSbwb6uXLQIUKwPXr1A31h2JiZO1GY0JDYZ5y5hwdHa3xBbV37QJ69uRvCh4/zr/bOnIbjQtpUp+LT69ewTKLwSA/bXlOTga6dgVOHwqH2ESKPXt4R/KsYlMJteXZ1KYwIiOB6GggOjwC8TGJSEriY3BiIiCV8gl9qRQwsLQBRHqQSoGk6ChIEuLlvp+UBHz7Bnz5AoTH2uDLVz2EhwNBz6MQ9Drz1lQ9EWBrC1jZW8O+iAHs7AC7gtEwM4qFnh7kLrL3IKZpW56l8dFAUvZbnkWmBSAyMJLFGsZ8xhgfPg25cNxjTFxSCQAwZ/IbGFsVhZ4hj2WJsWAJ0RCJgPr1M5fZSL+NmbY8C7vlOSY+HnZOTmobI2eqUKjMW42fgtLfisz27gW6dePX//mH11AkCmjxOaS2mzCBdzAvWRJ48oSfKwP5Y1xgjCEkJAQRCv7uEEKIKgoUKAB7e/sffkBBE4o6bswY3oXP0hK4eRNwcRE6Iw2gpSeDERFAuXK8McLs2cDUqUJnpD40LqTRlOdCIgHc3YGdOwEjI75iwNVVuHykUuDlS+D+fX55/Rr4/JnX3k/9VyJRTy5lywL16qVdKlYEfvDhYu7S0jFSl2nKuJDX6HmQ9+QJULcu/y8/fjyw8EdLnwmNjxrs+3d+nvzpE68vPm0avz0/jQsSiQRJSUmC5kAI0XyGhoY/XJmYirY867CdO/lkIgBs306TiUrT0wNq1eJXDQxQK/V6SmMETTV1Kp9MdHHhbwwIya8Y491Dd+4EDAyA/fvVP5kYGgr4+QH//ccnEB8+5O8Tf8TQEDA25pOghoZp/+rrp60QTL9aMKvr+vpAoUKAjQ1feWhjwy9FigC1a/PvCUpLx0ginIiICOzfvx+vX7/G+PHjYW1tjbt378LOzg5FixYVOj2dFBkJdOzIx73mzYF584TOSEPQ+KixLCyAJUv4Ktx584DevQFnZ6Gzkqevr6/0JAAhhOQGWqGoox484CtX4uL4RNLs2UJnRIR06xZfZcAYnyRp1kzojNSLxoU0+f25YAzw9ASWL+fvy/75J227XV6KjgYuXQLOn+eXR48yx5iaAlWrAtWq8VUMhQunTfil/mtsnPe5EpLbhBwXHj58CFdXV1hZWSEoKAjPnz9HyZIlMXXqVLx79w7bt29XWy75fXxUF6mUTyYePQo4OgJ37vAxjhBtxxifQL9wgdedP3SIxgVCiG6jFYo66OtXfiIYF8cbb8yYIXRGREgSCV/txRivn6hrk4lEs0yfzicTAWDz5rybTPz2DbhyhdeXvXQJuH0byFhOsXp1oGlTvtikenWgTBk1by0mRAd4enqib9++WLhwISzS1XVt06YNevbsKWBmusvHh08mGhsDBw/SZCLRHSIRsGoV/+Dw8GHg9GleA5gQQnQVTSjqGIkE6NULCAwESpTgWwbpDbBuW7eOry6wsuJbOQjJrxYuBObM4ddXrwb69s2d4yYnAwEB/P/BrVt8EvHxYz7Jnp6zM9CiBd9e3bQpvYkmRB1u3bqFDRs2ZLq9aNGiCAkJESAj3Xb6dFrtuLVrZbt3CdEZFSsCo0YBS5cCI0cCV68KnREhhAiHJhR1zKxZ/GTQ1JQv00/XVJUoKzaWt8QGEHv7NiqknE0/ffoU4pRuoJoiOBj46y9+fd48wN5e2HwIyc769cDEifz6/PnA8OE5P1ZCAnDkCK9/ePs2r4EYF5c5rmxZoFEj4Ndf+b8lSuT8MXWKFo2RRHjGxsaIyqIT94sXL2BLs/pqFRjIdzIwBgweDPTvL3RGGojGR63g7c1Lrrx6xVcsEkKIrsoXFYDXrFkDZ2dnmJiYoG7durh582a2sZs2bUKjRo1QsGBBFCxYEK6urgrjSZqzZ9NqJW7cyGt9kRxgDHj7Fnj7Fkwqxdu3b/H27VtoYjlST08gKoqvMBgyROhsyP/bu+/wqMq0f+DfSW8kEEoaIRQDKE2lZANKkWikikSJqBtAeGWFoAj8BNyFgMpG2sIiAdSXpihNJe4LEcRAUCH0oBRBwFDUNBZSJp2Z5/fHQyYZMhMmIZkz5fu5rnNxZuaZM/eZTG4m93mKIcyPwKZNwKRJcv/ttysLi7WVlQXMnw+EhMih0h98AKSmymKil5csHL75JrB9O5CZCVy4APzv/wIxMSwm1ooN5UhS3vDhw/HOO+/oVi5VqVS4du0aZs6ciaioKIWjsx8lJcDIkXI6iF69gBUrlI7ISjE/2gRvb2DxYrlf8S8RkT1SvKC4detWTJs2DXFxcTh58iS6deuGyMhIZGdnG2yfkpKC0aNHY//+/UhNTUVwcDCeeuop/PHHH2aO3Lr88Ycc6iyELBy9/LLSEZHS9u4FtmyRC1usWcOh75aI+VH2pB47Vuau2NjKIc+1ceIEMGYM0KqVnDM2K0uuiDx1qixWnj8vVyw9cEAOYXruOcDPr55PhIjqZOnSpVCr1WjRogWKi4vRr18/PPDAA2jUqBEWLFigdHh2Y/p02Zu7eXPgyy+5wBTRSy/J0QslJUpHQkSkHMVXeQ4LC0PPnj2xcuVKAIBWq0VwcDCmTJmCWbNm3fP5Go0GTZo0wcqVKxETE3PP9va4Etft23KhjR9+kJMIp6YCbm5KR2XFCgtldyYAhVlZ8LpTeVCr1fD09FQyMpOVlABdusihGlOmsKeBpeYFc+dHwLLei2+/BYYNA8rKZEFw3TpZADfV9evA+PGyeF4hPFzOfRQVBTg713/MBJvIkaTPEvLCwYMH8dNPP0GtVuPRRx9FRESE2WOwhPdBCV98ATz/vNzfswd46ill47FqzI825eefgUceyYdWa395gYgIUHgOxbKyMpw4cQKzZ8/W3efg4ICIiAikpqaadIyioiKUl5fD18hkgKWlpSgtLdXdNjQPj62bM0cWExs1kkP5WEykhQtlMTEgoHIYPFkWc+RHwHJz5MGDwIgRspgYFSWHHtemmLhtm+yNnZsrC4ejRgFvvAH07NlQERNRQ+rTpw/69OmjdBh257ff5IUZAJg9m8VEoqq6dpVzkfO7NBHZK0WHPN+4cQMajQZ+d40t8/PzM3nlvpkzZyIwMNDoler4+Hj4+PjotuDg4PuO25okJckFDABg7VrggQeUjYeUd/EiEB8v95ctk6s7k+UxR34ELDNHpqUBgwfLuQ2fflpOfO5k4uWvggJg3Dg5R2Jurpzr69w5ObSZxUQi67Fv3z489NBDBi9y5OXloVOnTvjhhx9qdczvv/8ew4YNQ2BgIFQqFRITE+spWttUViZzaX4+0KePXNiPiPTNmKF0BEREylF8DsX78f7772PLli3YsWMH3Ix0u5s9ezby8vJ02/Xr180cpXKuXwf++le5P3ly5XAVsl9CyM9CaSnw5JOy1xbZJlPyI2B5OfLCBSAyUv4B+/jjcq4uFxfTnnv0KPDII8CGDYBKJXsN/PgjL6QQWaPly5fjf/7nfwwOIfTx8cHEiRPxr3/9q1bHLCwsRLdu3ZCQkFBfYdq0WbOA48cBX19g82bTL+wQERGRfVD0q0GzZs3g6OiIrKwsvfuzsrLg7+9f43OXLFmC999/H9999x26du1qtJ2rqytc7XDm6PJyeVX55k2ge3dg6VKlI7IhKhXw0ENy18EBD1Xsq1RKRmWSbdvkfHKurkBCgjwVskzmyI+AZeXIa9dkoTsnB3j0UeD//g/w8Lj384QAliyRK0Dfvi0XX/n0U7lqMynAinMkWY6ffvoJCxcuNPr4U089hSVLltTqmIMGDcKgQYPuNzS78J//yFEMgLxIYwGd120D8yMREdkQRQuKLi4u6N69O5KTkzFixAgActGB5ORkxMbGGn3eokWLsGDBAuzZswc9evQwU7TWQ6OR892kpsrhrNu2cTW+euXhAZw9K3cBnL2zb+ny8oA335T7s2cDoaHKxkM1s7f8mJUli4nXrwMdOwK7d5s2HL+0FHj1VeCTT+Tt6Gi5annjxg0aLtXESnMkWZasrCw417BykpOTE3JycswYkf24dg0YO1buv/mmXByL6gnzIxER2RDFBy9MmzYNY8aMQY8ePdCrVy8sX74chYWFGDduHAAgJiYGQUFBiL8z6dvChQsxd+5cfP7552jdurVuLjEvLy943Vk1zZ7dvg3ExMihKY6O8o/stm2VjooswZw5QEaGLCTOnKl0NGQKe8mPublymPOvvwIhIbIXbfPm935eTg7w7LNyARdHR+Df/wYmTWLPWyJbEBQUhDNnzuABI3MW/PzzzwgICGjQGCx10aqGlJsLjBwJ3LoF9OhROQ83ERER0d0ULyhGR0cjJycHc+fORWZmJh5++GHs3r1btxDBtWvX4FBlac/Vq1ejrKwMzz33nN5x4uLiMG/ePHOGbnHKy4EXXwS++ELOc7NlCzB8uNJRkSU4eVIOcQaAVau40re1sIf8WFgIDBkC/PQT4Ocni4ktW977eWfPAkOHAleuyJ6M27fLHo5EZBsGDx6MOXPm4Omnn642D2xxcTHi4uIwdOjQBo0hPj4e8+fPb9DXsCS5uXIV5xMngKZNga1bTZ/DloiIiOyPSgghlA7CnPLz8+Hj44O8vDyDE31bq4qV+BITAWdnWVRkMbGBFBXplostOnAAPfv1AwAcO3YMHqZM+GZmGg0QHg4cOwa88ILsvUr6bDUv1IU534uyMpmn9uyRQ5QPHADuMeUjAOCbb2S+KygA2rWTcy0++GCDhkq1YWU5ku5NiRyZlZWFRx99FI6OjoiNjUWHDh0AAOfPn0dCQgI0Gg1Onjypu8BSWyqVCjt27NBNKWGIoR6KwcHBNvl/xa1bsph4/DjQrBmQnGxaPqZaYn60OfwOSUT2TPEeinT/SkqA554Ddu2ScyV+9RUweLDSUdkwIYBz5+SuVotzFfsWWpv/8ENZTPT2Bmq5ICZRg9FogJdflsVEDw8gKcm0P14TEoDXXwe0WrnoyldfyZ40ZEGsLEeSZfLz88OhQ4fw2muvYfbs2brPj0qlQmRkJBISEupcTDSVJS1a1ZBu3ZI9vE+ckMXEffuALl2UjspGMT8SEZENYUHRyt28KYc579kjh7F+/bW8wkwEAJmZcuVbAFiwAGjg6aaITCIEMHGiHKbs7Cx7VoeH3/s5b79dOZ/XuHFy8RUOxyOyXSEhIUhKSsKtW7dw6dIlCCEQGhqKJk2a1Ol4arUaly5d0t1OT0/HqVOn4Ovri1atWtVX2Fbl5k1ZTDx5Us5du28f0Lmz0lERERGRNWBB0UqVlsqeOu++K+e88fCQw/6eeELpyMiSzJghV3fu3h147TWloyGShcG33gLWrgUcHOQQ/HvNfVhWBkyYAHz6qbz97rvA3//OxVeI7EWTJk3Q884w0ftx/PhxDBgwQHd72rRpAIAxY8Zgw4YN9318a3PjhrwInZYGtGghi4mdOikdFREREVkLFhStjBCyV8+sWUB6uryvSxfgo4+Av/xF2djIsiQnA599Josua9bIVXCJlPb++8CSJXL/44+BqKia2xcUyCkdvv1WfoY//lj2TiQiqq3+/ftzaCnkYlgrVwILF8rhzn5+spj40ENKR0ZERETWhAVFKyEE8OOPsmfP4cPyvoAA4L33gDFjWCwifaWlwKRJcn/SJKBHD2XjIQKA1asrh+AvXQq88krN7TMz5XywaWmyF/YXXwCDBjV8nEREtqisTF6Uee89mV8BWUT84gsubEVERES1x4Kihbt+Hdi0CfjkE+D8eXmfh4csLE6fDnh5KRsfWabFi4FffwX8/eXciURK27wZmDxZ7v/978CdkYZG/forEBkJXLki5/XatUu3MCYREdVCSQmwZQswf77MqQDQpo28/eKLvChNREREdcOCogVSq+XKpZ98IoegVIzOcXOTq6LOnw8EBiobo11TqYCQELnr4ICQin0LmdDt8mXZ+wCQqzr7+CgbD1FSEhATI3PZpElyDsSaHD8ueyLeuAG0aycXnWrXzjyxUj2w8BxJZA8KCmTu3bFDXpBRq+X9AQHAnDnA+PFc1EoRzI9ERGRDWFC0EBcvyi9+SUlASoocllKhXz/5x/hzzwHe3oqFSBU8PHSX+D0AXKm43G8BhABiY+WQ54EDgRdeUDoisnc//CDnSbx9W/aE+eCDmhdT+e474Nln5R+/3bvLnNiihfnipXpgwTmSyJZdvSpzaGIisHev/C5QISgIeP11+R3Bw0OxEIn5kYiIbAgLigpRq+WciHv2yCvHFy/qPx4aCvz1r7JHYps2ysRI1ueLL4Ddu2Wvg1WruAouKSstDRg6VA63GzIE2LBBruxszLZtMueVl8uC+I4dQKNGZguXiMiq/PknsH9/5fbbb/qPP/CAvKDz7LNyyoia8i8RERFRbbGgaCZlZXIxlX375Oq7hw/LHjsVnJyAxx+Xf3QPGQJ06MBiENVOfj4wdarcnzULaN9e0XDIzlXMgZifD/TtK1end3Y23j4hAZgyRfayHTVKTvng6mq+eImILJkQsmPbDz/I7fvvZZ6tytFRLsI2ZIgsInbqxO+SRERE1HBYUGwgpaXAsWPAgQNyCPOhQ0BRkX6bVq2AiAi5iumTT3I4s9UoLpYVEgDFe/agb2QkAOD777+Hu7u7YmHFxcneCu3aAbNnKxYGEX7/Xea0nBzgkUeA//wHMParIQQwbx7wzjvy9qRJwIoVXCTAqllojiSyJlotcPasHM1SUUD84w/9NiqVzLFPPAEMGCAvTLNXt4VjfiQiIhvCgmI9KSwEjhyRX/oOHABSU+Uwv6qaN5df+gYOlP+2bcsrx1ZJq5WrRgDQ3r6N4xX7Wq1iIaWlySIMIHt6ubkpFgrZuRs3ZDHx2jXZS3b3buMLA2k0cj6vNWvk7XnzgLlzmRetngXmSCJLV1wsf21+/FFuBw8CeXn6bZycZA/Exx+X22OPAU2aKBMv1RHzIxER2RAWFOsoM1N+2av44peWJv84rqpFC3kRsl8/uXXuzD+Uqf5ptcBrr8l/R42Sw0yJlJCfL1dnPn8eaNlSLgpgbEGVsjI5T+y2bTIvJiTIzzERka0TArh+XV58Tk2Vo1jS0vSnwgEALy8gPBzo00d+nwwL44IqREREZDlYUDRBYSFw4gRw9KjshXj0qOx9c7dWrSq/9PXrB3TsyAIiNbyPP5afy0aNgGXLlI6G7FVJCTBihOx40ayZLCa2amW4rVotFwr49ls5r+KmTbIYTkRki27dkrnx2DH579Gj1YcvA0BAgOx1WLF17Sp7JRIRERFZIn5NqcGNG8BLLwHffSd7f1WlUskven36yC99ffoY/+OZqKFkZ8sFWADgvfeAwEBl4yH7dPs28MILcpXRRo3kMOeOHQ23/e9/5YIBR47InjY7dgBPPWXeeImIGtr168A//iFHs1y+XP1xR0fg4YeB3r1lL8TeveX3SF6IJiIiImvBgqIRN27IuQ5//lneDgyUQ03CwoBeveQcNpz4mpQ2YwaQmysnZZ80SeloyB5ptcCECcDXX8tVmf/v/4Du3Q23/eMPWTw8dw7w9QV27QL+8hfzxktE1NCuXQP69wfS0yvva9cO6NlTbj16yI3Dl4mIiMiasaBoQE6OLCaePg34+wPffCOvIhNZkv37gU8/lb0Z1qzhsCgyPyGAadOAjRtlb5vt2+V0D4ZcvCgXa7l6VV6g+fZboFMn88ZLRNTQfv9drricni6LiAkJsojo66t0ZERERET1iyWIu9xdTNy/3/jQPbJjzZpV2W1WQ8OGUVpauYDFa6/JXrNE5vbee8C//y33N2wAhg0z3O7UKblYUHY2EBoqi4mtW5spSFKGwjmSSAl//CGLib/9BrRtC6SkyAWqiPQwPxIRkY1gQbGK7GxZTDxzRk6MvX8/0KGD0lGRxfH0lJVnAJ4Acu7sm9PixcCFC4CfH7BggdlfnggrVwJz58r9FSuAl1823O7HH4GhQ4G8PNnTe88e4ys/k42wgBxJZG5//gk88QRw6RLQpo38DsliIlXD/EhERDbEQekALEV2tvwieOaMHI6XksJiIlmmy5dlzzBArurcuLGi4ZAd+uwzYMoUuT9vXuX+3b75Rs6ZmJcHPP64zKssJhKRrcnIkN8hf/0VCAmRxUQu1EdERES2jgVFAOXlwNNPA2fPAkFB8o/e9u2VjoqoOiGA2Fg55DkiQq6sS2ROO3cCY8bI/ddfr+yleLctW4Dhw4HiYmDwYLnys4+P+eIkIjKHW7fk6JYLF2QRcf9+WVQkIiIisnUsKAJYvhxISwOaNpVfBENDlY6ILFpxsVy+sX9/FN+8if79+6N///4oLi5u8Jf+4gtZmHF1BVatkguyEJnL998Dzz8PaDRyiPOyZYY/gx9+CLz4InD7NjB6NJCYyNVM7YqCOZLI3P7f/wN++UUOb96/Xw53JjKK+ZGIiGyI3c+heO2aHLIHAEuWsJhIJtBqgQMH5O7t2zhQsa/VNujL5ucDb7wh92fP5meVzOvkSbnoSkmJ/HfdOsDBwCWpRYuAmTPl/qRJwAcfGG5HNkyhHElkbikpwNq1cn/zZrkQC1GNmB+JiMiG2H1B8fXXgaIioG/fymF8ROak1coiTXGx3EpK5GeyoABQq+VWUADs2iXnaQoNrSzYEJnDhQtyWoj8fKBfP2DrVsDZWb+NEMA//gH885/y9ttvy7k+2YuWiGxRcTHw6qty/29/Ax57TNl4iIiIiMzNbguKhTk52PO1Fl9/3RhOTgJL3r2JohwNHF1c4FZllYvC7Gyjx3BwcoK7r2+d2hbduAFh5GqkysEBHs2a1alt8c2b0N6+bTQOzyorItSmbUluLjRlZfXS1qNZM6judFkqzc/H7ZKSemnr7usLByf5kS5Tq1FeVGRS25J8NdS3ilBWJucmvHvTODdGudZFFvpy1SjPyUHFgrYJiypX5/v7m9nQOAWhpFy2LVUXoUytRmlpZcGwrEz+W1oK5JV4o7DUDaWlgBOK4AK10XhL4Q0N3AAAK5eXQJOfj8J8w21dvLzgfGd86e2SEpTmG2l4V1tNWRlKcnONtnX28ICLl1et22pv30bxzZv10tbJzQ2u3t4AAKHVoujGjXppW1LD58reXbokFxvIyQEefRT4z38Ad3f9Nlqt7D27cqW8vXAh8NZb5o+ViMhcFiwALl4EAgKA999XOhoiIiIiBQg7k5eXJwCIP+EuQpAuACFmIl4I2cFGHG3eXK+9+s79hrY0Hx+9tjkqldG2Zz089Nped3Q02vaiq6te24uurkbbXnd01Gt71sPDaNsclUqvbZqPj9G26rs+GkebNzfaVtzV9lBQUI1t1VlZurY/tGtXY9ttCefE1q1CbNwoxFeBnWts+7enfxAvvCDEs88K8aFPjxrb9vJMFJ6eQjg5CRGHfjW27YENupszMLja+4Q7mxoQ/bBM9/AkPF/jcQcjTndzDMbX2HZG6zfF888LkZAgxKE336yx7Q/jx1f+3OLiamyb8vzzlZ+HZctqbLt/8ODKz9mGDTW37dev8vObmFhz2x49Kn8vfvih5ng7d678fTt3rub3oV07XVt1VlaNbfcGBAgAIi8vT9i7ihyZl5cnfvtNiOBg+TZ16iREdnb19uXlQowZI9uoVEKsWmX2kMnSqNV6+V6XI9VqpSOjOqqaF+xZxftw6FCecHKSH/Mvv1Q6KrIqzI82h/mRiOyZRcxslZCQgNatW8PNzQ1hYWE4evRoje23b9+Ojh07ws3NDV26dEFSUlKtX3MR3sJVtEYIrmAO3q1r6DbtyhU50XhamlwJuyaLFgHvvCOHOebm1dy2Rw+gRQvA0xO4dLnmtpMmA9HRcjj6n3/W3Pab3XJl2R07gLx7xKAuBAoL5aIR9+LXAujSBejZE/BtUnPbJyPkMM8lS4BHH6m5bexk+R5nZQFjYmpuO/JZYNs2OScd2Rcl8iMg55cdMAC4fh3o2BFITgaaN9dvU1oqfz83bgQcHYFPPwVee61OL0dEZBa1zamGTJkivz+MGAGMHFn/MRIRERFZA5UQQigZwNatWxETE4M1a9YgLCwMy5cvx/bt23HhwgW0qDKMtsKhQ4fQt29fxMfHY+jQofj888+xcOFCnDx5Ep07d77n6+Xn58PHxweOjrnQaHyw7ZNcDI6sHJ5rKUOe3XybIT9fFsZyrt5AXq5WN6deYWHlpi50gFrbDOXlcuVVjfomhOY2NBr5Zff2bVkMvH1bPl6kaoGyMjkE93bBTZQW39Ybkqut8mkoQuX774pcOML4MObatW2GigXGXZAPJ8jhpi7OciVYd/fKDR7N4OHpADc3wN0hHx4uJXBxAVxcADc36PZdXQEXH1+4ezrB1RVwFmo4iSLdY66u0Nv3al7ZVlWuhqNWtnV2rj7nm1vjxnB0cQFwZyh1Tg4878y8nn3mDPzufO6yfvsNTYOCdG3Li+SQZ2Ncvb3h5OZW67a1GcbMIc+mtS0sKYFfSAjy8vLgfec5lsDc+RGozJGtW+fhyhVvhIbK+eMDAirbFBTI4uEHHwDnz8vfrW3bgGeeqa8zJ6tWWAjc+Z0uzMqCl58fAECtVsPT01PJyKiOKvKCpeXI2qptTr1bxfsAyPfh3DkgKKjh4yYbwvxoc2wlPxIR1YXiBcWwsDD07NkTK+9MvqXVahEcHIwpU6Zg1qxZ1dpHR0ejsLAQO3fu1N33l7/8BQ8//DDWrFlzz9er+mXwmWe8kZgo/2//809ZvKsovFX8W3W/okBXdf/u2+XlxjdD8/OVlFQvElZsSnN3l0U7N7fKfVdX/X+rbhX3VS0IVt08PeXm5VW5X7G5u8seTlahsFB2sQRQmJ6OFm3aAACys7P5ZdBKWeqXQXPnR0A/R7Zr540DByr/YD5/Hli1CtiwQRYVAcDHB/jiCyAi4n7OlGwKc6TNsdQcWVu1zal3q5ofV63yZo9sqj3mR5tjK/mRiKguFF2UpaysDCdOnMDs2bN19zk4OCAiIgKpqakGn5Oamopp06bp3RcZGYnExESD7UtLS1FaWqq7nX+nt5ZKBZw5I/8YrqEDl+JcXGSMFVujRnLz8tLfXFxkQc7JSf9fZ2e57+ysv1+10GeoCOjqytVZjfL01FV8PQEUWkL1l2yOOfIjYDxHenrK6Qni4uT96enAvn2Vz+vQAYiNBWJiAH5/Jj3MkWSB6pJTjeXHsDBg4sSGjZdsFPMjERHZEEULijdu3IBGo4Hfne7+Ffz8/HD+/HmDz8nMzDTYPjMz02D7+Ph4zJ8/v9r9QgCXq8zf5+kJNGkii21VC3IV+3ffrvi3okhXdbu7eOfsrD/cturm5la9t15FLz4fH/k4Edkfc+RHwHiOLCwEtm7Vv8/BARg2TBYSBw7kRQcish51yanG8uOKFTIfEhEREdkzRQuK5jB79my9Hjv5+fkIDg7G6tWyh01goNwaNVIwSCIihRjLkXPn6l/QcHeXCxC0bm32EImIFGEsP3bsqGBQRERERBZC0YJis2bN4OjoiKysLL37s7Ky4O/vb/A5/v7+tWrv6uoKV1fXave/+CKH6VEdlZQAUVFy97PPEPXSSwCAL7/8Em7sUkr1xBz5ETCeI6dPZ46kOmKOJAtUl5xqLD8S1RnzIxER2RBFB2y4uLige/fuSE5O1t2n1WqRnJyM8PBwg88JDw/Xaw8Ae/fuNdqeqN5pNEBSEpCUBE1ZGZKSkpCUlASNRqN0ZGRDmB/JajFHkgWqS04lqnfMj0REZEMUH/I8bdo0jBkzBj169ECvXr2wfPlyFBYWYty4cQCAmJgYBAUFIT4+HgDwxhtvoF+/fli6dCmGDBmCLVu24Pjx4/joo4+UPA0ionrH/EhEVH/ulVOJiIiIyHSKFxSjo6ORk5ODuXPnIjMzEw8//DB2796tmzT72rVrcKgy83Xv3r3x+eef4x//+AfefvtthIaGIjExEZ07d1bqFIiIGgTzIxFR/blXTiUiIiIi06mEEELpIMwpPz8fPj4+yMvLgzcnCKO6KCyUy3ADKMzKgtedP0TUajU8PT2VjIzqiHmhEt8Lum/MkTaHeUHi+0D3jfnR5jAvEJE9U3QORSIiIiIiIiIiIrIuLCgSERERERERERGRyRSfQ9HcKkZ45+fnKxwJWa3CwsrdggLdfn5+Plfps1IV+cDOZoAwiDmS7htzpM1hjpSYH+m+MT/aHOZHIrJndjeH4u+//47g4GClwyAiC3T9+nW0bNlS6TAUxRxJRMbYe45kfiQiY+w9PxKRfbK7gqJWq8Wff/6JRo0aQaVSKR1OneTn5yM4OBjXr1+32sl/eQ6WgecgCSFQUFCAwMBAvVWT7RFzpGXgOVgGnoPEHCkxP1oGnoNl4DlIzI9EZM/sbsizg4ODzVw98vb2ttr/wCvwHCwDzwHw8fGpx2isF3OkZeE5WAaeA3MkwPxoaXgOloHnwPxIRPaLl1GIiIiIiIiIiIjIZCwoEhERERERERERkclYULRCrq6uiIuLg6urq9Kh1BnPwTLwHMgW2cJngudgGXgOZGts4fPAc7AMPAciIrK7RVmIiIiIiIiIiIio7thDkYiIiIiIiIiIiEzGgiIRERERERERERGZjAVFIiIiIiIiIiIiMhkLihYmPj4ePXv2RKNGjdCiRQuMGDECFy5cqPE5GzZsgEql0tvc3NzMFHF18+bNqxZPx44da3zO9u3b0bFjR7i5uaFLly5ISkoyU7SGtW7duto5qFQqTJ482WB7S/gZfP/99xg2bBgCAwOhUqmQmJio97gQAnPnzkVAQADc3d0RERGBixcv3vO4CQkJaN26Ndzc3BAWFoajR4820BnUfA7l5eWYOXMmunTpAk9PTwQGBiImJgZ//vlnjcesy+eRLBdzJHNkXTFHGsYcaTuYH5kf64r50TDmRyKimrGgaGEOHDiAyZMn4/Dhw9i7dy/Ky8vx1FNPobCwsMbneXt7IyMjQ7ddvXrVTBEb1qlTJ714fvzxR6NtDx06hNGjR2P8+PFIS0vDiBEjMGLECJw5c8aMEes7duyYXvx79+4FADz//PNGn6P0z6CwsBDdunVDQkKCwccXLVqEFStWYM2aNThy5Ag8PT0RGRmJkpISo8fcunUrpk2bhri4OJw8eRLdunVDZGQksrOzzX4ORUVFOHnyJObMmYOTJ0/iq6++woULFzB8+PB7Hrc2n0eybMyRzJF1xRxpHHOkbWB+ZH6sK+ZH45gfiYhqIMiiZWdnCwDiwIEDRtusX79e+Pj4mC+oe4iLixPdunUzuf2oUaPEkCFD9O4LCwsTEydOrOfI6u6NN94Q7dq1E1qt1uDjlvYzACB27Nihu63VaoW/v79YvHix7r7c3Fzh6uoqNm/ebPQ4vXr1EpMnT9bd1mg0IjAwUMTHxzdI3FXdfQ6GHD16VAAQV69eNdqmtp9Hsi7MkZaBOVJijiRLwvxoGZgfJeZHIiLbwx6KFi4vLw8A4OvrW2M7tVqNkJAQBAcH45lnnsHZs2fNEZ5RFy9eRGBgINq2bYuXXnoJ165dM9o2NTUVERERevdFRkYiNTW1ocM0SVlZGTZt2oRXXnkFKpXKaDtL+xlUlZ6ejszMTL332cfHB2FhYUbf57KyMpw4cULvOQ4ODoiIiLCYn01eXh5UKhUaN25cY7vafB7JujBHKo85kjmSLBPzo/KYH5kfiYhsGQuKFkyr1WLq1Kno06cPOnfubLRdhw4dsG7dOnz99dfYtGkTtFotevfujd9//92M0VYKCwvDhg0bsHv3bqxevRrp6el4/PHHUVBQYLB9ZmYm/Pz89O7z8/NDZmamOcK9p8TEROTm5mLs2LFG21jaz+BuFe9lbd7nGzduQKPRWOzPpqSkBDNnzsTo0aPh7e1ttF1tP49kPZgjlf89BJgjLfVnwxxp35gflf8dBJgfLfVnw/xIRFQ/nJQOgIybPHkyzpw5c8+5OsLDwxEeHq673bt3bzz44IP48MMP8e677zZ0mNUMGjRIt9+1a1eEhYUhJCQE27Ztw/jx480ez/1au3YtBg0ahMDAQKNtLO1nYOvKy8sxatQoCCGwevXqGtva2ueRKjFHWgbmSMvDHEnMj5aB+dHyMD8SEdUf9lC0ULGxsdi5cyf279+Pli1b1uq5zs7OeOSRR3Dp0qUGiq52GjdujPbt2xuNx9/fH1lZWXr3ZWVlwd/f3xzh1ejq1av47rvvMGHChFo9z9J+BhXvZW3e52bNmsHR0dHifjYVXwSvXr2KvXv31nhl2ZB7fR7JOjBHMkfWJ+bISsyR1o/5kfmxPjE/VmJ+JCLSx4KihRFCIDY2Fjt27MC+ffvQpk2bWh9Do9Hg9OnTCAgIaIAIa0+tVuPy5ctG4wkPD0dycrLefXv37tW7WquU9evXo0WLFhgyZEitnmdpP4M2bdrA399f733Oz8/HkSNHjL7PLi4u6N69u95ztFotkpOTFfvZVHwRvHjxIr777js0bdq01se41+eRLBtzpMQcWb+YIysxR1ov5keJ+bF+MT9WYn4kIrqLkivCUHWvvfaa8PHxESkpKSIjI0O3FRUV6dr89a9/FbNmzdLdnj9/vtizZ4+4fPmyOHHihHjhhReEm5ubOHv2rBKnIKZPny5SUlJEenq6OHjwoIiIiBDNmjUT2dnZBuM/ePCgcHJyEkuWLBG//PKLiIuLE87OzuL06dOKxF9Bo9GIVq1aiZkzZ1Z7zBJ/BgUFBSItLU2kpaUJAOJf//qXSEtL061e9/7774vGjRuLr7/+Wvz888/imWeeEW3atBHFxcW6YzzxxBPigw8+0N3esmWLcHV1FRs2bBDnzp0Tr776qmjcuLHIzMw0+zmUlZWJ4cOHi5YtW4pTp07p/X6UlpYaPYd7fR7JujBHMkfWFXOk4XNgjrQdzI/Mj3XF/Gj4HJgfiYhqxoKihQFgcFu/fr2uTb9+/cSYMWN0t6dOnSpatWolXFxchJ+fnxg8eLA4efKk+YO/Izo6WgQEBAgXFxcRFBQkoqOjxaVLl3SP3x2/EEJs27ZNtG/fXri4uIhOnTqJXbt2mTnq6vbs2SMAiAsXLlR7zBJ/Bvv37zf42amIU6vVijlz5gg/Pz/h6uoqBg4cWO3cQkJCRFxcnN59H3zwge7cevXqJQ4fPqzIOaSnpxv9/di/f7/Rc7jX55GsC3Mkc2RdMUcaPgfmSNvB/Mj8WFfMj4bPgfmRiKhmKiGEqGPnRiIiIiIiIiIiIrIznEORiIiIiIiIiIiITMaCIhEREREREREREZmMBUUiIiIiIiIiIiIyGQuKREREREREREREZDIWFImIiIiIiIiIiMhkLCgSERERERERERGRyVhQJCIiIiIiIiIiIpOxoEhEREREREREREQmY0GR6uzKlStQqVQ4deqUyc8ZO3YsRowYUWOb/v37Y+rUqfcVm0qlQmJiIgDT4zTldase15zmzZsHlUoFlUqF5cuX39exNmzYgMaNG5vt9YjsFXOk+TBHElkX5kfzYX4kIqKGwoKiDcvMzMSUKVPQtm1buLq6Ijg4GMOGDUNycrLSoZlVcHAwMjIy0LlzZwBASkoKVCoVcnNza32sjIwMDBo0qJ4jNE2nTp2QkZGBV199tdpj8fHxcHR0xOLFi+vltWbMmIGMjAy0bNmyXo5HZImYIyXmyNpjjiRbx/woMT/WHvMjEZH9YEHRRl25cgXdu3fHvn37sHjxYpw+fRq7d+/GgAEDMHnyZKXDMytHR0f4+/vDycnpvo/l7+8PV1fXeoiq9pycnODv7w8PD49qj61btw5vvfUW1q1bVy+v5eXlBX9/fzg6OtbL8YgsDXNkJebI2mOOJFvG/FiJ+bH2mB+JiOwHC4o2atKkSVCpVDh69CiioqLQvn17dOrUCdOmTcPhw4cBAK+88gqGDh2q97zy8nK0aNECa9euBQBotVosWrQIDzzwAFxdXdGqVSssWLDA4GtqNBqMHz8ebdq0gbu7Ozp06IB///vfBtvOnz8fzZs3h7e3N/72t7+hrKzM6LmUlpZixowZCAoKgqenJ8LCwpCSkmLye1F1uMqVK1cwYMAAAECTJk2gUqkwduxYXVutVou33noLvr6+8Pf3x7x58/SOVXW4iqGr1KdOnYJKpcKVK1cAVA4N2blzJzp06AAPDw8899xzKCoqwsaNG9G6dWs0adIEr7/+OjQajcnnVNWBAwdQXFyMd955B/n5+Th06JBJz9uzZw8efPBBeHl54emnn0ZGRkadXp/IGjFHVmKONIw5kuwV82Ml5kfDmB+JiAgA7v9yG1mcmzdvYvfu3ViwYAE8PT2rPV4x98mECRPQt29fZGRkICAgAACwc+dOFBUVITo6GgAwe/ZsfPzxx1i2bBkee+wxZGRk4Pz58wZfV6vVomXLlti+fTuaNm2KQ4cO4dVXX0VAQABGjRqla5ecnAw3NzekpKTgypUrGDduHJo2bWr0S2ZsbCzOnTuHLVu2IDAwEDt27MDTTz+N06dPIzQ0tFbvTXBwML788ktERUXhwoUL8Pb2hru7u+7xjRs3Ytq0aThy5AhSU1MxduxY9OnTB08++WStXqeqoqIirFixAlu2bEFBQQFGjhyJZ599Fo0bN0ZSUhJ+++03REVFoU+fPrr3vTbWrl2L0aNHw9nZGaNHj8batWvRu3fve8a0ZMkSfPrpp3BwcMDLL7+MGTNm4LPPPqvraRJZDeZI45gjK2NijiR7xPxoHPNjZUzMj0REBAAQZHOOHDkiAIivvvrqnm0feughsXDhQt3tYcOGibFjxwohhMjPzxeurq7i448/Nvjc9PR0AUCkpaUZPf7kyZNFVFSU7vaYMWOEr6+vKCws1N23evVq4eXlJTQajRBCiH79+ok33nhDCCHE1atXhaOjo/jjjz/0jjtw4EAxe/Zso68LQOzYscNgnPv37xcAxK1bt/Se069fP/HYY4/p3dezZ08xc+ZMg8c1dJy0tDQBQKSnpwshhFi/fr0AIC5duqRrM3HiROHh4SEKCgp090VGRoqJEycaPZ+4uDjRrVu3avfn5eUJd3d3cerUKd3re3l56R37boZiSkhIEH5+ftXahoSEiGXLlhk9FpE1Yo5kjmSOJDKM+ZH5kfmRiIhMxSHPNkgIYXLbCRMmYP369QCArKwsfPPNN3jllVcAAL/88gtKS0sxcOBAk4+XkJCA7t27o3nz5vDy8sJHH32Ea9eu6bXp1q2b3hwu4eHhUKvVuH79erXjnT59GhqNBu3bt4eXl5duO3DgAC5fvmxyXKbq2rWr3u2AgABkZ2ff1zE9PDzQrl073W0/Pz+0bt0aXl5eevfV5XU2b96Mdu3aoVu3bgCAhx9+GCEhIdi6dWutYqqP8ySyFsyRdcccSWTbmB/rjvmRiIjsDYc826DQ0FCoVCqjw0qqiomJwaxZs5CamopDhw6hTZs2ePzxxwFAbxiHKbZs2YIZM2Zg6dKlCA8PR6NGjbB48WIcOXKkTucBAGq1Go6Ojjhx4kS1yZ2rfpmqL87Oznq3VSoVtFqtwbYODrIeX/XLd3l5uUnHrM3r1GTt2rU4e/as3mThWq0W69atw/jx440+z9Dr1+aPCCJrxhxZd8yRRLaN+bHumB+JiMjesKBog3x9fREZGYmEhAS8/vrr1ebAyc3N1c2B07RpU4wYMQLr169Hamoqxo0bp2sXGhoKd3d3JCcnY8KECfd83YMHD6J3796YNGmS7j5DV4B/+uknFBcX675sHj58GF5eXggODq7W9pFHHoFGo0F2drbuS+r9cnFxAYA6T2BdoXnz5gCAjIwMNGnSBICcUNtcTp8+jePHjyMlJQW+vr66+2/evIn+/fvj/Pnz6Nixo9niIbIWzJE1Y44ksl/MjzVjfiQiIqrEIc82KiEhARqNBr169cKXX36Jixcv4pdffsGKFSsQHh6u13bChAnYuHEjfvnlF4wZM0Z3v5ubG2bOnIm33noLn3zyCS5fvozDhw/rVu+7W2hoKI4fP449e/bg119/xZw5c3Ds2LFq7crKyjB+/HicO3cOSUlJiIuLQ2xsrO5qbVXt27fHSy+9hJiYGHz11VdIT0/H0aNHER8fj127dtXpvQkJCYFKpcLOnTuRk5MDtVpdp+M88MADCA4Oxrx583Dx4kXs2rULS5curdOx6mLt2rXo1asX+vbti86dO+u2vn37omfPnrqf08qVK2s15IjIHjBHGsccSWTfmB+NY34kIiKqxIKijWrbti1OnjyJAQMGYPr06ejcuTOefPJJJCcnY/Xq1XptIyIiEBAQgMjISAQGBuo9NmfOHEyfPh1z587Fgw8+iOjoaKPzpEycOBEjR45EdHQ0wsLC8N///lfvSnOFgQMHIjQ0FH379kV0dDSGDx+OefPmGT2X9evXIyYmBtOnT0eHDh0wYsQIHDt2DK1atar9GwMgKCgI8+fPx6xZs+Dn54fY2Ng6HcfZ2RmbN2/G+fPn0bVrVyxcuBDvvfdenY5VW2VlZdi0aROioqIMPh4VFYVPPvkE5eXluHHjRoPMFURkzZgjjWOOJLJvzI/GMT8SERFVUglOemH31Go1goKCsH79eowcOVLpcMiAefPmITEx0azDYQCgdevWmDp1KqZOnWrW1yWyJMyRlo85kkgZzI+Wj/mRiIgaCnso2jGtVovs7Gy8++67aNy4MYYPH650SFSD06dPw8vLC6tWrWrw1/rnP/8JLy+vaqsrEtkT5kjrwhxJZD7Mj9aF+ZGIiBoCeyjasStXrqBNmzZo2bIlNmzYwDlSLNjNmzdx8+ZNAHIibx8fH5t6PSJLxBxpPZgjicyL+dF6MD8SEVFDYUGRiIiIiIiIiIiITMYhz0RERERERERERGQyFhSJiIiIiIiIiIjIZCwoEhERERERERERkclYUCQiIiIiIiIiIiKTsaBIREREREREREREJmNBkYiIiIiIiIiIiEzGgiIRERERERERERGZjAVFIiIiIiIiIiIiMhkLikRERERERERERGSy/w+RAKCcTB2y2AAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1000x300 with 3 Axes>"
       ]
@@ -466,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -487,7 +481,17 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
+      "text/plain": [
+       "<Figure size 1000x300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x300 with 3 Axes>"
       ]
@@ -497,7 +501,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 3 Axes>"
       ]
@@ -507,7 +511,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 3 Axes>"
       ]
@@ -517,7 +521,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 3 Axes>"
       ]
@@ -527,7 +531,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 3 Axes>"
       ]
@@ -537,7 +541,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 3 Axes>"
       ]
@@ -547,7 +551,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x300 with 3 Axes>"
       ]
@@ -557,14 +561,16 @@
     }
    ],
    "source": [
+    "# Skip the MSMR example parameter set since we need to set up the ESOH solver differently\n",
+    "all_parameter_sets.remove(\"MSMR_Example\")\n",
+    "# Loop over all parameter sets and solve the ESOH problem\n",
     "for parameter_set in all_parameter_sets:\n",
     "    print(parameter_set)\n",
     "    try:\n",
     "        sweep, sol_init_QLi, sol_init_Q = solve_esoh_sweep_QLi(parameter_set, param)\n",
     "        fig, axes = plot_sweep(sweep, sol_init_QLi, sol_init_Q, parameter_set)\n",
     "    except ValueError:\n",
-    "        pass\n",
-    "    # print(\"success\")"
+    "        pass"
    ]
   },
   {
@@ -579,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -588,26 +594,26 @@
      "text": [
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-      "[17] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n",
-      "[18] Eric Prada, D. Di Domenico, Y. Creff, J. Bernard, Valérie Sauvant-Moynot, and François Huet. A simplified electrochemical and thermal aging model of LiFePO4-graphite Li-ion batteries: power and capacity fade simulations. Journal of The Electrochemical Society, 160(4):A616, 2013. doi:10.1149/2.053304jes.\n",
-      "[19] P Ramadass, Bala Haran, Parthasarathy M Gomadam, Ralph White, and Branko N Popov. Development of first principles capacity fade model for li-ion cells. Journal of the Electrochemical Society, 151(2):A196, 2004. doi:10.1149/1.1634273.\n",
-      "[20] Giles Richardson, Ivan Korotkin, Rahifa Ranom, Michael Castle, and Jamie M. Foster. Generalised single particle models for high-rate operation of graded lithium-ion electrodes: systematic derivation and validation. Electrochimica Acta, 339:135862, 2020. doi:10.1016/j.electacta.2020.135862.\n",
-      "[21] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-      "[22] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
+      "[10] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[11] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n",
+      "[12] Peyman Mohtat, Suhak Lee, Valentin Sulzer, Jason B. Siegel, and Anna G. Stefanopoulou. Differential Expansion and Voltage Model for Li-ion Batteries at Practical Charging Rates. Journal of The Electrochemical Society, 167(11):110561, 2020. doi:10.1149/1945-7111/aba5d1.\n",
+      "[13] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n",
+      "[14] Kieran O'Regan, Ferran Brosa Planella, W. Dhammika Widanage, and Emma Kendrick. Thermal-electrochemical parameters of a high energy lithium-ion cylindrical battery. Electrochimica Acta, 425:140700, 2022. doi:10.1016/j.electacta.2022.140700.\n",
+      "[15] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n",
+      "[16] Eric Prada, D. Di Domenico, Y. Creff, J. Bernard, Valérie Sauvant-Moynot, and François Huet. A simplified electrochemical and thermal aging model of LiFePO4-graphite Li-ion batteries: power and capacity fade simulations. Journal of The Electrochemical Society, 160(4):A616, 2013. doi:10.1149/2.053304jes.\n",
+      "[17] P Ramadass, Bala Haran, Parthasarathy M Gomadam, Ralph White, and Branko N Popov. Development of first principles capacity fade model for li-ion cells. Journal of the Electrochemical Society, 151(2):A196, 2004. doi:10.1149/1.1634273.\n",
+      "[18] Giles Richardson, Ivan Korotkin, Rahifa Ranom, Michael Castle, and Jamie M. Foster. Generalised single particle models for high-rate operation of graded lithium-ion electrodes: systematic derivation and validation. Electrochimica Acta, 339:135862, 2020. doi:10.1016/j.electacta.2020.135862.\n",
+      "[19] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "[20] Mark Verbrugge, Daniel Baker, Brian Koch, Xingcheng Xiao, and Wentian Gu. Thermodynamic model for substitutional materials: application to lithiated graphite, spinel manganese oxide, iron phosphate, and layered nickel-manganese-cobalt oxide. Journal of The Electrochemical Society, 164(11):E3243, 2017.\n",
+      "[21] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
+      "[22] Andrew Weng, Jason B Siegel, and Anna Stefanopoulou. Differential voltage analysis for battery manufacturing process control. arXiv preprint arXiv:2303.07088, 2023.\n",
       "[23] Yan Zhao, Yatish Patel, Teng Zhang, and Gregory J Offer. Modeling the effects of thermal gradients induced by tab and surface cooling on lithium ion cell performance. Journal of The Electrochemical Society, 165(13):A3169, 2018.\n",
       "\n"
      ]
@@ -620,7 +626,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pybamm",
+   "display_name": "dev",
    "language": "python",
    "name": "python3"
   },
@@ -651,7 +657,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c"
+    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
    }
   }
  },
diff --git a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
index f03c328abf..933d27aa78 100644
--- a/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
+++ b/docs/source/examples/notebooks/models/jelly-roll-model.ipynb
@@ -280,23 +280,36 @@
    "outputs": [],
    "source": [
     "# define spiral \n",
+    "\n",
+    "\n",
     "def spiral_pos_inner(t):\n",
     "    return r0 - eps * delta + eps * t / (2 * pi)\n",
+    "\n",
+    "\n",
     "def spiral_pos_outer(t):\n",
     "    return r0 + eps * delta + eps * t / (2 * pi)\n",
     "\n",
+    "\n",
     "def spiral_neg_inner(t):\n",
     "    return r0 - eps * delta + eps / 2 + eps * t / (2 * pi)\n",
+    "\n",
+    "\n",
     "def spiral_neg_outer(t):\n",
     "    return r0 + eps * delta + eps / 2 + eps * t / (2 * pi)\n",
     "\n",
+    "\n",
     "def spiral_am1_inner(t):\n",
     "    return r0 + eps * delta + eps * t / (2 * pi)\n",
+    "\n",
+    "\n",
     "def spiral_am1_outer(t):\n",
     "    return r0 - eps * delta + eps / 2 + eps * t / (2 * pi)\n",
     "\n",
+    "\n",
     "def spiral_am2_inner(t):\n",
     "    return r0 + eps * delta + eps / 2 + eps * t / (2 * pi)\n",
+    "\n",
+    "\n",
     "def spiral_am2_outer(t):\n",
     "    return r0 - eps * delta + eps + eps * t / (2 * pi)"
    ]
diff --git a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
index f1e81796a1..7eae36e725 100644
--- a/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
+++ b/docs/source/examples/notebooks/models/loss_of_active_materials.ipynb
@@ -312,6 +312,7 @@
     "def current_LAM(i, T):\n",
     "    return -1e-10 * (abs(i) + 1e3 * abs(i) ** 0.5)\n",
     "\n",
+    "\n",
     "model = pybamm.lithium_ion.DFN(\n",
     "    options=\n",
     "    {\n",
diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
index b7315a62e4..35226ed89f 100644
--- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb
+++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb
@@ -1,1817 +1,1770 @@
 {
-    "cells": [
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Parameterisation\n",
-                "\n",
-                "In this notebook, we show how to find which parameters are needed in a model and define them.\n",
-                "\n",
-                "For other notebooks about parameterization, see:\n",
-                "\n",
-                "- The API documentation of [Parameters](https://docs.pybamm.org/en/latest/source/api/parameters/index.html)\n",
-                "- [Setting parameter values](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb) can be found at `pybamm/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb`. This explains the basics of how to set the parameters of a model (in less detail than here).\n",
-                "- [parameter-values.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/parameterization/parameter-values.ipynb) can be found at `pybamm/examples/notebooks/parameterization/parameter-values.ipynb`. This explains the basics of the `ParameterValues` class.\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Adding your own parameter sets (using a dictionary)\n",
-                "\n",
-                "We will be using the model defined and explained in more detail in [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example notebook. We begin by importing the required libraries"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 1,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Note: you may need to restart the kernel to use updated packages.\n"
-                    ]
-                }
-            ],
-            "source": [
-                "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
-                "import pybamm\n",
-                "import numpy as np\n",
-                "import matplotlib.pyplot as plt"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Setting up the model\n",
-                "\n",
-                "We define all the parameters and variables using `pybamm.Parameter` and `pybamm.Variable` respectively."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 2,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n",
-                "\n",
-                "R = pybamm.Parameter(\"Particle radius [m]\")\n",
-                "D = pybamm.FunctionParameter(\"Diffusion coefficient [m2.s-1]\", {\"Concentration [mol.m-3]\": c})\n",
-                "j = pybamm.InputParameter(\"Interfacial current density [A.m-2]\")\n",
-                "c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n",
-                "c_e = pybamm.Parameter(\"Electrolyte concentration [mol.m-3]\")"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we define our model equations, boundary and initial conditions. We also add the variables required using the dictionary `model.variables`"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 3,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "model = pybamm.BaseModel()\n",
-                "\n",
-                "# governing equations\n",
-                "N = -D * pybamm.grad(c)  # flux\n",
-                "dcdt = -pybamm.div(N)\n",
-                "model.rhs = {c: dcdt}  \n",
-                "\n",
-                "# boundary conditions \n",
-                "lbc = pybamm.Scalar(0)\n",
-                "rbc = -j\n",
-                "model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}\n",
-                "\n",
-                "# initial conditions \n",
-                "model.initial_conditions = {c: c0}\n",
-                "\n",
-                "model.variables = {\n",
-                "    \"Concentration [mol.m-3]\": c,\n",
-                "    \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n",
-                "    \"Flux [mol.m-2.s-1]\": N,\n",
-                "}"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We also define the geometry, since there are parameters in the geometry too"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 4,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n",
-                "geometry = pybamm.Geometry({\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}})"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Finding the parameters required"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "To know what parameters are required by the model and geometry, we can do"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 5,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Initial concentration [mol.m-3] (Parameter)\n",
-                        "Interfacial current density [A.m-2] (InputParameter)\n",
-                        "Diffusion coefficient [m2.s-1] (FunctionParameter with input(s) 'Concentration [mol.m-3]')\n",
-                        "\n",
-                        "Particle radius [m] (Parameter)\n"
-                    ]
-                }
-            ],
-            "source": [
-                "model.print_parameter_info()\n",
-                "geometry.print_parameter_info()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "This tells us that we need to provide parameter values for the initial concentration and Faraday constant, an `InputParameter` at solve time for the interfacial current density, and diffusivity as a function of concentration. Since the electrolyte concentration does not appear anywhere in the model, there is no need to provide a value for it."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Adding the parameters\n",
-                "\n",
-                "Now we can proceed to the step where we add the `parameter` values using a dictionary. We set up a dictionary with parameter names as the dictionary keys and their respective values as the dictionary values."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 6,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "def D_fun(c):\n",
-                "    return 3.9 #* pybamm.exp(-c)\n",
-                "\n",
-                "values = {\n",
-                "    \"Particle radius [m]\": 2,\n",
-                "    \"Diffusion coefficient [m2.s-1]\": D_fun,\n",
-                "    \"Initial concentration [mol.m-3]\": 2.5,\n",
-                "}"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Now we can pass this dictionary in `pybamm.ParameterValues` class which accepts a dictionary of parameter names and values. We can then print `param` to check if it was initialised."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 7,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "{'Diffusion coefficient [m2.s-1]': <function D_fun at 0x14c4a6310>,\n",
-                            " 'Initial concentration [mol.m-3]': 2.5,\n",
-                            " 'Particle radius [m]': 2}"
-                        ]
-                    },
-                    "execution_count": 7,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "param = pybamm.ParameterValues(values)\n",
-                "\n",
-                "param"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Updating the parameter values\n",
-                "\n",
-                "The parameter values or `param` can be further updated by using the `update` function of `ParameterValues` class. The `update` function takes a dictionary with keys being the parameters to be updated and their respective values being the updated values. Here we update the `\"Particle radius [m]\"` parameter's value. Additionally, a function can also be passed as a `parameter`'s value which we will see ahead, and a new `parameter` can also be added by passing `check_already_exists=False` in the `update` function."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 8,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "{'Diffusion coefficient [m2.s-1]': <function D_fun at 0x14c4a6310>,\n",
-                            " 'Initial concentration [mol.m-3]': 1.5,\n",
-                            " 'Particle radius [m]': 2}"
-                        ]
-                    },
-                    "execution_count": 8,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "param.update({\"Initial concentration [mol.m-3]\": 1.5})\n",
-                "param"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Solving the model "
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Finding the parameters in a model\n",
-                "\n",
-                "The `parameter` function of the `BaseModel` class can be used to obtain the parameters of a model."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 9,
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "[Parameter(-0x7c3ebfeae2200290, Initial concentration [mol.m-3], children=[], domains={}),\n",
-                            " InputParameter(-0x4a08933302b1e44e, Interfacial current density [A.m-2], children=[], domains={}),\n",
-                            " FunctionParameter(0x66f7cbc27c44053b, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]"
-                        ]
-                    },
-                    "execution_count": 9,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "parameters = model.parameters\n",
-                "parameters"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "As explained in the [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example, we first process both the `model` and the `geometry`."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 10,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "param.process_model(model)\n",
-                "param.process_geometry(geometry)"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We can now set up our mesh, choose a spatial method, and discretise our model"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 11,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n",
-                "var_pts = {r: 20}\n",
-                "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n",
-                "\n",
-                "spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n",
-                "disc = pybamm.Discretisation(mesh, spatial_methods)\n",
-                "disc.process_model(model);"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We choose a solver and times at which we want the solution returned, and solve the model. Here we give a value for the current density `j`."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 12,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 1300x400 with 2 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
-            "source": [
-                "# solve\n",
-                "solver = pybamm.ScipySolver()\n",
-                "t = np.linspace(0, 3600, 600)\n",
-                "solution = solver.solve(model, t, inputs={\"Interfacial current density [A.m-2]\": 1.4})\n",
-                "\n",
-                "# post-process, so that the solution can be called at any time t or space r\n",
-                "# (using interpolation)\n",
-                "c = solution[\"Concentration [mol.m-3]\"]\n",
-                "c_surf = solution[\"Surface concentration [mol.m-3]\"]\n",
-                "\n",
-                "# plot\n",
-                "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n",
-                "\n",
-                "ax1.plot(solution.t, c_surf(solution.t))\n",
-                "ax1.set_xlabel(\"Time [s]\")\n",
-                "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n",
-                "\n",
-                "rsol = mesh[\"negative particle\"].nodes # radial position\n",
-                "time = 1000  # time in seconds\n",
-                "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=\"t={}[s]\".format(time))\n",
-                "ax2.set_xlabel(\"Particle radius [microns]\")\n",
-                "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n",
-                "ax2.legend()\n",
-                "\n",
-                "plt.tight_layout()\n",
-                "plt.show()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Using pre-defined models in `PyBaMM`\n",
-                "\n",
-                "In the next few steps, we will be showing the same workflow with the Single Particle Model (`SPM`). We will also see how you can pass a function as a `parameter`'s value and how to plot such `parameter functions`.\n",
-                "\n",
-                "We start by initializing our model"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 13,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "spm = pybamm.lithium_ion.SPM()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Finding the parameters in a model\n",
-                "\n",
-                "We can print the `parameters` of a model by using the `get_parameters_info` function."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 14,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n",
-                        "Initial concentration in electrolyte [mol.m-3] (Parameter)\n",
-                        "Separator thickness [m] (Parameter)\n",
-                        "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n",
-                        "Negative electrode thickness [m] (Parameter)\n",
-                        "Electrode height [m] (Parameter)\n",
-                        "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n",
-                        "Number of cells connected in series to make a battery (Parameter)\n",
-                        "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n",
-                        "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n",
-                        "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n",
-                        "Lower voltage cut-off [V] (Parameter)\n",
-                        "Nominal cell capacity [A.h] (Parameter)\n",
-                        "Typical electrolyte concentration [mol.m-3] (Parameter)\n",
-                        "Upper voltage cut-off [V] (Parameter)\n",
-                        "Positive electrode electrons in reaction (Parameter)\n",
-                        "Negative electrode electrons in reaction (Parameter)\n",
-                        "Initial temperature [K] (Parameter)\n",
-                        "Reference temperature [K] (Parameter)\n",
-                        "Positive electrode thickness [m] (Parameter)\n",
-                        "Number of electrodes connected in parallel to make a cell (Parameter)\n",
-                        "Electrode width [m] (Parameter)\n",
-                        "Separator Bruggeman coefficient (electrolyte) (Parameter)\n",
-                        "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-                        "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n",
-                        "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-                        "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n",
-                        "Negative electrode OCP [V] (FunctionParameter with input(s) 'Negative particle stoichiometry')\n",
-                        "Negative electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]')\n",
-                        "Ambient temperature [K] (FunctionParameter with input(s) 'Time [s]')\n",
-                        "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-                        "Positive electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]')\n",
-                        "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-                        "Positive electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-                        "Initial concentration in positive electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n",
-                        "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-                        "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n",
-                        "Negative electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]')\n",
-                        "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n",
-                        "Positive electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]')\n",
-                        "Positive electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n",
-                        "Positive electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "spm.print_parameter_info()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Note that there are no `InputParameter` objects in the default SPM. Also, note that if a `FunctionParameter` is expected, it is ok to provide a scalar (parameter) instead. However, if a `Parameter` is expected, you cannot provide a function instead."
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Another way to view what parameters are needed is to print the default parameter values. This can also be used to get some good defaults (but care must be taken when combining parameters across datasets and chemistries)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 15,
-            "metadata": {
-                "scrolled": true
-            },
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "{'Negative electrode thickness [m]': 0.0001,\n",
-                            " 'Separator thickness [m]': 2.5e-05,\n",
-                            " 'Positive electrode thickness [m]': 0.0001,\n",
-                            " 'Electrode height [m]': 0.137,\n",
-                            " 'Electrode width [m]': 0.207,\n",
-                            " 'Nominal cell capacity [A.h]': 0.680616,\n",
-                            " 'Current function [A]': 0.680616,\n",
-                            " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n",
-                            " 'Negative electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T)>,\n",
-                            " 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_ocp_Dualfoil1998(sto)>,\n",
-                            " 'Negative electrode porosity': 0.3,\n",
-                            " 'Negative electrode active material volume fraction': 0.6,\n",
-                            " 'Negative particle radius [m]': 1e-05,\n",
-                            " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-                            " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
-                            " 'Negative electrode electrons in reaction': 1.0,\n",
-                            " 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n",
-                            " 'Negative electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_entropic_change_Moura2016(sto, c_s_max)>,\n",
-                            " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n",
-                            " 'Positive electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_diffusivity_Dualfoil1998(sto, T)>,\n",
-                            " 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_ocp_Dualfoil1998(sto)>,\n",
-                            " 'Positive electrode porosity': 0.3,\n",
-                            " 'Positive electrode active material volume fraction': 0.5,\n",
-                            " 'Positive particle radius [m]': 1e-05,\n",
-                            " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-                            " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
-                            " 'Positive electrode electrons in reaction': 1.0,\n",
-                            " 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n",
-                            " 'Positive electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_entropic_change_Moura2016(sto, c_s_max)>,\n",
-                            " 'Separator porosity': 1.0,\n",
-                            " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-                            " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
-                            " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
-                            " 'Reference temperature [K]': 298.15,\n",
-                            " 'Ambient temperature [K]': 298.15,\n",
-                            " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-                            " 'Number of cells connected in series to make a battery': 1.0,\n",
-                            " 'Lower voltage cut-off [V]': 3.105,\n",
-                            " 'Upper voltage cut-off [V]': 4.1,\n",
-                            " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n",
-                            " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565,\n",
-                            " 'Initial temperature [K]': 298.15}"
-                        ]
-                    },
-                    "execution_count": 15,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "{k: v for k,v in spm.default_parameter_values.items() if k in spm._parameter_info}"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "We now define a dictionary of values for `ParameterValues` as before (here, a subset of the `Chen2020` parameters)"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "{'Ambient temperature [K]': 298.15,\n",
-                            " 'Current function [A]': 5.0,\n",
-                            " 'Electrode height [m]': 0.065,\n",
-                            " 'Electrode width [m]': 1.58,\n",
-                            " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
-                            " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n",
-                            " 'Initial temperature [K]': 298.15,\n",
-                            " 'Lower voltage cut-off [V]': 2.5,\n",
-                            " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
-                            " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
-                            " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
-                            " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-                            " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n",
-                            "                                ([array([0.        , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n",
-                            "       0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n",
-                            "       0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n",
-                            "       0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n",
-                            "       0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n",
-                            "       0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n",
-                            "       0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n",
-                            "       0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n",
-                            "       0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n",
-                            "       0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n",
-                            "       0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n",
-                            "       0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n",
-                            "       0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n",
-                            "       0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n",
-                            "       0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n",
-                            "       0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n",
-                            "       0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n",
-                            "       0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n",
-                            "       0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n",
-                            "       0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n",
-                            "       0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n",
-                            "       0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n",
-                            "       0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n",
-                            "       0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n",
-                            "       0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n",
-                            "       0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n",
-                            "       0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n",
-                            "       0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n",
-                            "       0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n",
-                            "       0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n",
-                            "       0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n",
-                            "       0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n",
-                            "       0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n",
-                            "       0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361  ,\n",
-                            "       0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n",
-                            "       0.67557767, 0.67928044, 0.68298322, 0.686686  , 0.69038878,\n",
-                            "       0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n",
-                            "       0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n",
-                            "       0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n",
-                            "       0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n",
-                            "       0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n",
-                            "       0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n",
-                            "       0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n",
-                            "       0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n",
-                            "       0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n",
-                            "       0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n",
-                            "       0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n",
-                            "       0.89774404, 0.9014468 , 1.        ])],\n",
-                            "                                 array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n",
-                            "       0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n",
-                            "       0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n",
-                            "       0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n",
-                            "       0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n",
-                            "       0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n",
-                            "       0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n",
-                            "       0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n",
-                            "       0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n",
-                            "       0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n",
-                            "       0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n",
-                            "       0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n",
-                            "       0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n",
-                            "       0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n",
-                            "       0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n",
-                            "       0.160027  , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n",
-                            "       0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n",
-                            "       0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n",
-                            "       0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n",
-                            "       0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n",
-                            "       0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n",
-                            "       0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n",
-                            "       0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n",
-                            "       0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n",
-                            "       0.13315202, 0.132713  , 0.1330184 , 0.13278936, 0.13225491,\n",
-                            "       0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n",
-                            "       0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n",
-                            "       0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n",
-                            "       0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n",
-                            "       0.13011713, 0.129869  , 0.12992626, 0.12942998, 0.12796026,\n",
-                            "       0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n",
-                            "       0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n",
-                            "       0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n",
-                            "       0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n",
-                            "       0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n",
-                            "       0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n",
-                            "       0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n",
-                            "       0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n",
-                            "       0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n",
-                            "       0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n",
-                            "       0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n",
-                            "       0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n",
-                            "       0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n",
-                            "       0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n",
-                            "       0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n",
-                            "       0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n",
-                            "       0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n",
-                            "       0.08709427, 0.08503284, 0.07601531]))),\n",
-                            " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
-                            " 'Negative electrode active material volume fraction': 0.75,\n",
-                            " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
-                            " 'Negative electrode electrons in reaction': 1.0,\n",
-                            " 'Negative electrode exchange-current density [A.m-2]': <function graphite_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x14d04df70>,\n",
-                            " 'Negative electrode porosity': 0.25,\n",
-                            " 'Negative electrode thickness [m]': 8.52e-05,\n",
-                            " 'Negative particle radius [m]': 5.86e-06,\n",
-                            " 'Nominal cell capacity [A.h]': 5.0,\n",
-                            " 'Number of cells connected in series to make a battery': 1.0,\n",
-                            " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-                            " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
-                            " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-                            " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n",
-                            "                                ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n",
-                            "       0.27703505, 0.27975753, 0.28248   , 0.28520247, 0.28792495,\n",
-                            "       0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n",
-                            "       0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n",
-                            "       0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n",
-                            "       0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n",
-                            "       0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n",
-                            "       0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n",
-                            "       0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n",
-                            "       0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n",
-                            "       0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n",
-                            "       0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n",
-                            "       0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n",
-                            "       0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n",
-                            "       0.453996  , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n",
-                            "       0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n",
-                            "       0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n",
-                            "       0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n",
-                            "       0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n",
-                            "       0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n",
-                            "       0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656   ,\n",
-                            "       0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n",
-                            "       0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n",
-                            "       0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n",
-                            "       0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n",
-                            "       0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n",
-                            "       0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n",
-                            "       0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n",
-                            "       0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n",
-                            "       0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n",
-                            "       0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n",
-                            "       0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n",
-                            "       0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n",
-                            "       0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n",
-                            "       0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n",
-                            "       0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n",
-                            "       0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n",
-                            "       0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n",
-                            "       0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n",
-                            "       0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n",
-                            "       0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n",
-                            "       0.82152961, 0.82425208, 0.82697453, 0.829697  , 0.83241946,\n",
-                            "       0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n",
-                            "       0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n",
-                            "       0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n",
-                            "       0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n",
-                            "       0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n",
-                            "       0.90320364, 0.90592613, 1.        ])],\n",
-                            "                                 array([4.4       , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n",
-                            "       4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n",
-                            "       4.213294  , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n",
-                            "       4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n",
-                            "       4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n",
-                            "       4.1768146 , 4.1768146 , 4.1752872 , 4.173111  , 4.1726718 ,\n",
-                            "       4.1710877 , 4.1702285 , 4.168797  , 4.1669831 , 4.1655135 ,\n",
-                            "       4.1634517 , 4.1598248 , 4.1571712 , 4.154079  , 4.1504135 ,\n",
-                            "       4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n",
-                            "       4.1272392 , 4.1228104 , 4.1186109 , 4.114182  , 4.1096005 ,\n",
-                            "       4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n",
-                            "       4.0818448 , 4.077683  , 4.0733309 , 4.0690737 , 4.0647216 ,\n",
-                            "       4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n",
-                            "       4.041986  , 4.0384736 , 4.035171  , 4.0320406 , 4.0289288 ,\n",
-                            "       4.02597   , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n",
-                            "       4.0122066 , 4.009954  , 4.0075679 , 4.0050669 , 4.0023184 ,\n",
-                            "       3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n",
-                            "       3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n",
-                            "       3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n",
-                            "       3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n",
-                            "       3.9192028 , 3.9166257 , 3.9117961 , 3.90815   , 3.9038739 ,\n",
-                            "       3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n",
-                            "       3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n",
-                            "       3.8581741 , 3.854146  , 3.8499846 , 3.8450022 , 3.8422534 ,\n",
-                            "       3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164  ,\n",
-                            "       3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n",
-                            "       3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n",
-                            "       3.788383  , 3.7855389 , 3.7838206 , 3.78111   , 3.7794874 ,\n",
-                            "       3.7769294 , 3.773608  , 3.7695992 , 3.7690265 , 3.7662776 ,\n",
-                            "       3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n",
-                            "       3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n",
-                            "       3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n",
-                            "       3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861  ,\n",
-                            "       3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n",
-                            "       3.7099447 , 3.7071004 , 3.7045615 , 3.703588  , 3.70208   ,\n",
-                            "       3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n",
-                            "       3.6890991 , 3.686522  , 3.6849759 , 3.6821697 , 3.6808143 ,\n",
-                            "       3.6786573 , 3.6761947 , 3.674763  , 3.6712887 , 3.6697233 ,\n",
-                            "       3.6678908 , 3.6652565 , 3.6630611 , 3.660274  , 3.6583652 ,\n",
-                            "       3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n",
-                            "       3.6383405 , 3.635076  , 3.633549  , 3.6322317 , 3.6306856 ,\n",
-                            "       3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n",
-                            "       3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n",
-                            "       3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n",
-                            "       3.5976417 , 3.5951984 , 3.593843  , 3.5916286 , 3.5894907 ,\n",
-                            "       3.587429  , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n",
-                            "       3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n",
-                            "       3.5684922 , 3.5672133 , 3.52302167]))),\n",
-                            " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
-                            " 'Positive electrode active material volume fraction': 0.665,\n",
-                            " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
-                            " 'Positive electrode electrons in reaction': 1.0,\n",
-                            " 'Positive electrode exchange-current density [A.m-2]': <function nmc_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x14d04d160>,\n",
-                            " 'Positive electrode porosity': 0.335,\n",
-                            " 'Positive electrode thickness [m]': 7.56e-05,\n",
-                            " 'Positive particle radius [m]': 5.22e-06,\n",
-                            " 'Reference temperature [K]': 298.15,\n",
-                            " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-                            " 'Separator porosity': 0.47,\n",
-                            " 'Separator thickness [m]': 1.2e-05,\n",
-                            " 'Typical current [A]': 5.0,\n",
-                            " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
-                            " 'Upper voltage cut-off [V]': 4.4}"
-                        ]
-                    },
-                    "execution_count": 16,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "def graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T):\n",
-                "    D_ref = 3.9 * 10 ** (-14)\n",
-                "    E_D_s = 42770\n",
-                "    arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T))\n",
-                "    return D_ref * arrhenius\n",
-                "\n",
-                "neg_ocp = np.array([[0.        , 1.81772748],\n",
-                "         [0.03129623, 1.0828807 ],\n",
-                "         [0.03499902, 0.99593794],\n",
-                "         [0.0387018 , 0.90023398],\n",
-                "         [0.04240458, 0.79649431],\n",
-                "         [0.04610736, 0.73354429],\n",
-                "         [0.04981015, 0.66664314],\n",
-                "         [0.05351292, 0.64137149],\n",
-                "         [0.05721568, 0.59813869],\n",
-                "         [0.06091845, 0.5670836 ],\n",
-                "         [0.06462122, 0.54746181],\n",
-                "         [0.06832399, 0.53068399],\n",
-                "         [0.07202675, 0.51304734],\n",
-                "         [0.07572951, 0.49394092],\n",
-                "         [0.07943227, 0.47926274],\n",
-                "         [0.08313503, 0.46065259],\n",
-                "         [0.08683779, 0.45992726],\n",
-                "         [0.09054054, 0.43801501],\n",
-                "         [0.09424331, 0.42438665],\n",
-                "         [0.09794607, 0.41150269],\n",
-                "         [0.10164883, 0.40033659],\n",
-                "         [0.10535158, 0.38957134],\n",
-                "         [0.10905434, 0.37756538],\n",
-                "         [0.1127571 , 0.36292541],\n",
-                "         [0.11645985, 0.34357086],\n",
-                "         [0.12016261, 0.3406314 ],\n",
-                "         [0.12386536, 0.32299468],\n",
-                "         [0.12756811, 0.31379458],\n",
-                "         [0.13127086, 0.30795386],\n",
-                "         [0.13497362, 0.29207319],\n",
-                "         [0.13867638, 0.28697687],\n",
-                "         [0.14237913, 0.27405477],\n",
-                "         [0.14608189, 0.2670497 ],\n",
-                "         [0.14978465, 0.25857493],\n",
-                "         [0.15348741, 0.25265783],\n",
-                "         [0.15719018, 0.24826777],\n",
-                "         [0.16089294, 0.2414345 ],\n",
-                "         [0.1645957 , 0.23362778],\n",
-                "         [0.16829847, 0.22956218],\n",
-                "         [0.17200122, 0.22370236],\n",
-                "         [0.17570399, 0.22181271],\n",
-                "         [0.17940674, 0.22089651],\n",
-                "         [0.1831095 , 0.2194268 ],\n",
-                "         [0.18681229, 0.21830064],\n",
-                "         [0.19051504, 0.21845333],\n",
-                "         [0.1942178 , 0.21753715],\n",
-                "         [0.19792056, 0.21719357],\n",
-                "         [0.20162334, 0.21635373],\n",
-                "         [0.2053261 , 0.21667822],\n",
-                "         [0.20902886, 0.21738444],\n",
-                "         [0.21273164, 0.21469313],\n",
-                "         [0.2164344 , 0.21541846],\n",
-                "         [0.22013716, 0.21465495],\n",
-                "         [0.22383993, 0.2135479 ],\n",
-                "         [0.2275427 , 0.21392964],\n",
-                "         [0.23124547, 0.21074206],\n",
-                "         [0.23494825, 0.20873788],\n",
-                "         [0.23865101, 0.20465319],\n",
-                "         [0.24235377, 0.20205732],\n",
-                "         [0.24605653, 0.19774358],\n",
-                "         [0.2497593 , 0.19444147],\n",
-                "         [0.25346208, 0.19190285],\n",
-                "         [0.25716486, 0.18850531],\n",
-                "         [0.26086762, 0.18581399],\n",
-                "         [0.26457039, 0.18327537],\n",
-                "         [0.26827314, 0.18157659],\n",
-                "         [0.2719759 , 0.17814088],\n",
-                "         [0.27567867, 0.17529686],\n",
-                "         [0.27938144, 0.1719375 ],\n",
-                "         [0.28308421, 0.16934161],\n",
-                "         [0.28678698, 0.16756649],\n",
-                "         [0.29048974, 0.16609676],\n",
-                "         [0.29419251, 0.16414985],\n",
-                "         [0.29789529, 0.16260378],\n",
-                "         [0.30159806, 0.16224113],\n",
-                "         [0.30530083, 0.160027  ],\n",
-                "         [0.30900361, 0.15827096],\n",
-                "         [0.31270637, 0.1588054 ],\n",
-                "         [0.31640913, 0.15552238],\n",
-                "         [0.32011189, 0.15580869],\n",
-                "         [0.32381466, 0.15220118],\n",
-                "         [0.32751744, 0.1511132 ],\n",
-                "         [0.33122021, 0.14987253],\n",
-                "         [0.33492297, 0.14874637],\n",
-                "         [0.33862575, 0.14678037],\n",
-                "         [0.34232853, 0.14620776],\n",
-                "         [0.34603131, 0.14555879],\n",
-                "         [0.34973408, 0.14389819],\n",
-                "         [0.35343685, 0.14359279],\n",
-                "         [0.35713963, 0.14242846],\n",
-                "         [0.36084241, 0.14038612],\n",
-                "         [0.36454517, 0.13882096],\n",
-                "         [0.36824795, 0.13954628],\n",
-                "         [0.37195071, 0.13946992],\n",
-                "         [0.37565348, 0.13780934],\n",
-                "         [0.37935626, 0.13973714],\n",
-                "         [0.38305904, 0.13698858],\n",
-                "         [0.38676182, 0.13523254],\n",
-                "         [0.3904646 , 0.13441178],\n",
-                "         [0.39416737, 0.1352898 ],\n",
-                "         [0.39787015, 0.13507985],\n",
-                "         [0.40157291, 0.13647321],\n",
-                "         [0.40527567, 0.13601512],\n",
-                "         [0.40897844, 0.13435452],\n",
-                "         [0.41268121, 0.1334765 ],\n",
-                "         [0.41638398, 0.1348317 ],\n",
-                "         [0.42008676, 0.13275118],\n",
-                "         [0.42378953, 0.13286571],\n",
-                "         [0.4274923 , 0.13263667],\n",
-                "         [0.43119506, 0.13456447],\n",
-                "         [0.43489784, 0.13471718],\n",
-                "         [0.43860061, 0.13395369],\n",
-                "         [0.44230338, 0.13448814],\n",
-                "         [0.44600615, 0.1334765 ],\n",
-                "         [0.44970893, 0.13298023],\n",
-                "         [0.45341168, 0.13259849],\n",
-                "         [0.45711444, 0.13338107],\n",
-                "         [0.46081719, 0.13309476],\n",
-                "         [0.46451994, 0.13275118],\n",
-                "         [0.46822269, 0.13443087],\n",
-                "         [0.47192545, 0.13315202],\n",
-                "         [0.47562821, 0.132713  ],\n",
-                "         [0.47933098, 0.1330184 ],\n",
-                "         [0.48303375, 0.13278936],\n",
-                "         [0.48673651, 0.13225491],\n",
-                "         [0.49043926, 0.13317111],\n",
-                "         [0.49414203, 0.13263667],\n",
-                "         [0.49784482, 0.13187316],\n",
-                "         [0.50154759, 0.13265574],\n",
-                "         [0.50525036, 0.13250305],\n",
-                "         [0.50895311, 0.13324745],\n",
-                "         [0.51265586, 0.13204496],\n",
-                "         [0.51635861, 0.13242669],\n",
-                "         [0.52006139, 0.13233127],\n",
-                "         [0.52376415, 0.13198769],\n",
-                "         [0.52746692, 0.13254122],\n",
-                "         [0.53116969, 0.13145325],\n",
-                "         [0.53487245, 0.13298023],\n",
-                "         [0.53857521, 0.13168229],\n",
-                "         [0.54227797, 0.1313578 ],\n",
-                "         [0.54598074, 0.13235036],\n",
-                "         [0.5496835 , 0.13120511],\n",
-                "         [0.55338627, 0.13089971],\n",
-                "         [0.55708902, 0.13109058],\n",
-                "         [0.56079178, 0.13082336],\n",
-                "         [0.56449454, 0.13011713],\n",
-                "         [0.5681973 , 0.129869  ],\n",
-                "         [0.57190006, 0.12992626],\n",
-                "         [0.57560282, 0.12942998],\n",
-                "         [0.57930558, 0.12796026],\n",
-                "         [0.58300835, 0.12862831],\n",
-                "         [0.58671112, 0.12656689],\n",
-                "         [0.59041389, 0.12734947],\n",
-                "         [0.59411664, 0.12509716],\n",
-                "         [0.59781941, 0.12110791],\n",
-                "         [0.60152218, 0.11839751],\n",
-                "         [0.60522496, 0.11244226],\n",
-                "         [0.60892772, 0.11307214],\n",
-                "         [0.61263048, 0.1092165 ],\n",
-                "         [0.61633325, 0.10683058],\n",
-                "         [0.62003603, 0.10433014],\n",
-                "         [0.6237388 , 0.10530359],\n",
-                "         [0.62744156, 0.10056993],\n",
-                "         [0.63114433, 0.09950104],\n",
-                "         [0.63484711, 0.09854668],\n",
-                "         [0.63854988, 0.09921473],\n",
-                "         [0.64225265, 0.09541635],\n",
-                "         [0.64595543, 0.09980643],\n",
-                "         [0.64965823, 0.0986612 ],\n",
-                "         [0.653361  , 0.09560722],\n",
-                "         [0.65706377, 0.09755413],\n",
-                "         [0.66076656, 0.09612258],\n",
-                "         [0.66446934, 0.09430929],\n",
-                "         [0.66817212, 0.09661885],\n",
-                "         [0.67187489, 0.09366032],\n",
-                "         [0.67557767, 0.09522548],\n",
-                "         [0.67928044, 0.09535909],\n",
-                "         [0.68298322, 0.09316404],\n",
-                "         [0.686686  , 0.09450016],\n",
-                "         [0.69038878, 0.0930877 ],\n",
-                "         [0.69409156, 0.09343126],\n",
-                "         [0.69779433, 0.0932404 ],\n",
-                "         [0.70149709, 0.09350762],\n",
-                "         [0.70519988, 0.09339309],\n",
-                "         [0.70890264, 0.09291591],\n",
-                "         [0.7126054 , 0.09303043],\n",
-                "         [0.71630818, 0.0926296 ],\n",
-                "         [0.72001095, 0.0932404 ],\n",
-                "         [0.72371371, 0.09261052],\n",
-                "         [0.72741648, 0.09249599],\n",
-                "         [0.73111925, 0.09240055],\n",
-                "         [0.73482204, 0.09253416],\n",
-                "         [0.7385248 , 0.09209515],\n",
-                "         [0.74222757, 0.09234329],\n",
-                "         [0.74593034, 0.09366032],\n",
-                "         [0.74963312, 0.09333583],\n",
-                "         [0.75333589, 0.09322131],\n",
-                "         [0.75703868, 0.09264868],\n",
-                "         [0.76074146, 0.09253416],\n",
-                "         [0.76444422, 0.09243873],\n",
-                "         [0.76814698, 0.09230512],\n",
-                "         [0.77184976, 0.09310678],\n",
-                "         [0.77555253, 0.09165615],\n",
-                "         [0.77925531, 0.09159888],\n",
-                "         [0.78295807, 0.09207606],\n",
-                "         [0.78666085, 0.09175158],\n",
-                "         [0.79036364, 0.09177067],\n",
-                "         [0.79406641, 0.09236237],\n",
-                "         [0.79776918, 0.09241964],\n",
-                "         [0.80147197, 0.09320222],\n",
-                "         [0.80517474, 0.09199972],\n",
-                "         [0.80887751, 0.09167523],\n",
-                "         [0.81258028, 0.09322131],\n",
-                "         [0.81628304, 0.09190428],\n",
-                "         [0.81998581, 0.09167523],\n",
-                "         [0.82368858, 0.09285865],\n",
-                "         [0.82739136, 0.09180884],\n",
-                "         [0.83109411, 0.09150345],\n",
-                "         [0.83479688, 0.09186611],\n",
-                "         [0.83849965, 0.0920188 ],\n",
-                "         [0.84220242, 0.09320222],\n",
-                "         [0.84590519, 0.09131257],\n",
-                "         [0.84960797, 0.09117896],\n",
-                "         [0.85331075, 0.09133166],\n",
-                "         [0.85701353, 0.09089265],\n",
-                "         [0.86071631, 0.09058725],\n",
-                "         [0.86441907, 0.09051091],\n",
-                "         [0.86812186, 0.09033912],\n",
-                "         [0.87182464, 0.09041547],\n",
-                "         [0.87552742, 0.0911217 ],\n",
-                "         [0.87923019, 0.0894611 ],\n",
-                "         [0.88293296, 0.08999555],\n",
-                "         [0.88663573, 0.08921297],\n",
-                "         [0.89033849, 0.08881213],\n",
-                "         [0.89404126, 0.08797229],\n",
-                "         [0.89774404, 0.08709427],\n",
-                "         [0.9014468 , 0.08503284],\n",
-                "         [1.        , 0.07601531]])\n",
-                "\n",
-                "pos_ocp = np.array([[0.24879728, 4.4       ],\n",
-                "         [0.26614516, 4.2935653 ],\n",
-                "         [0.26886763, 4.2768621 ],\n",
-                "         [0.27159011, 4.2647018 ],\n",
-                "         [0.27431258, 4.2540312 ],\n",
-                "         [0.27703505, 4.2449446 ],\n",
-                "         [0.27975753, 4.2364879 ],\n",
-                "         [0.28248   , 4.2302647 ],\n",
-                "         [0.28520247, 4.2225528 ],\n",
-                "         [0.28792495, 4.2182574 ],\n",
-                "         [0.29064743, 4.213294  ],\n",
-                "         [0.29336992, 4.2090373 ],\n",
-                "         [0.29609239, 4.2051239 ],\n",
-                "         [0.29881487, 4.2012677 ],\n",
-                "         [0.30153735, 4.1981564 ],\n",
-                "         [0.30425983, 4.1955218 ],\n",
-                "         [0.30698231, 4.1931167 ],\n",
-                "         [0.30970478, 4.1889744 ],\n",
-                "         [0.31242725, 4.1881533 ],\n",
-                "         [0.31514973, 4.1865883 ],\n",
-                "         [0.3178722 , 4.1850228 ],\n",
-                "         [0.32059466, 4.1832285 ],\n",
-                "         [0.32331714, 4.1808805 ],\n",
-                "         [0.32603962, 4.1805749 ],\n",
-                "         [0.32876209, 4.1789522 ],\n",
-                "         [0.33148456, 4.1768146 ],\n",
-                "         [0.33420703, 4.1768146 ],\n",
-                "         [0.3369295 , 4.1752872 ],\n",
-                "         [0.33965197, 4.173111  ],\n",
-                "         [0.34237446, 4.1726718 ],\n",
-                "         [0.34509694, 4.1710877 ],\n",
-                "         [0.34781941, 4.1702285 ],\n",
-                "         [0.3505419 , 4.168797  ],\n",
-                "         [0.35326438, 4.1669831 ],\n",
-                "         [0.35598685, 4.1655135 ],\n",
-                "         [0.35870932, 4.1634517 ],\n",
-                "         [0.3614318 , 4.1598248 ],\n",
-                "         [0.36415428, 4.1571712 ],\n",
-                "         [0.36687674, 4.154079  ],\n",
-                "         [0.36959921, 4.1504135 ],\n",
-                "         [0.37232169, 4.1466532 ],\n",
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-                "         [0.89231371, 3.5763381 ],\n",
-                "         [0.8950362 , 3.5737801 ],\n",
-                "         [0.89775868, 3.5721002 ],\n",
-                "         [0.90048116, 3.5702102 ],\n",
-                "         [0.90320364, 3.5684922 ],\n",
-                "         [0.90592613, 3.5672133 ],\n",
-                "         [1.        , 3.52302167]])\n",
-                "\n",
-                "from pybamm import exp, constants\n",
-                "\n",
-                "\n",
-                "def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_n_max, T):\n",
-                "    m_ref = 6.48e-7  # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n",
-                "    E_r = 35000\n",
-                "    arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n",
-                "\n",
-                "    return (\n",
-                "        m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5\n",
-                "    )\n",
-                "\n",
-                "def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_p_max, T):\n",
-                "    m_ref = 3.42e-6  # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n",
-                "    E_r = 17800\n",
-                "    arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n",
-                "\n",
-                "    return (\n",
-                "        m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5\n",
-                "    )\n",
-                "\n",
-                "\n",
-                "values = {\n",
-                " 'Negative electrode thickness [m]': 8.52e-05,\n",
-                " 'Separator thickness [m]': 1.2e-05,\n",
-                " 'Positive electrode thickness [m]': 7.56e-05,\n",
-                " 'Electrode height [m]': 0.065,\n",
-                " 'Electrode width [m]': 1.58,\n",
-                " 'Nominal cell capacity [A.h]': 5.0,\n",
-                " 'Typical current [A]': 5.0,\n",
-                " 'Current function [A]': 5.0,\n",
-                " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
-                " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
-                " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020', neg_ocp),\n",
-                " 'Negative electrode porosity': 0.25,\n",
-                " 'Negative electrode active material volume fraction': 0.75,\n",
-                " 'Negative particle radius [m]': 5.86e-06,\n",
-                " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-                " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
-                " 'Negative electrode electrons in reaction': 1.0,\n",
-                " 'Negative electrode exchange-current density [A.m-2]': graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\n",
-                " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
-                " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
-                " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
-                " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020', pos_ocp),\n",
-                " 'Positive electrode porosity': 0.335,\n",
-                " 'Positive electrode active material volume fraction': 0.665,\n",
-                " 'Positive particle radius [m]': 5.22e-06,\n",
-                " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-                " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
-                " 'Positive electrode electrons in reaction': 1.0,\n",
-                " 'Positive electrode exchange-current density [A.m-2]': nmc_LGM50_electrolyte_exchange_current_density_Chen2020,\n",
-                " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
-                " 'Separator porosity': 0.47,\n",
-                " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-                " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
-                " 'Reference temperature [K]': 298.15,\n",
-                " 'Ambient temperature [K]': 298.15,\n",
-                " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-                " 'Number of cells connected in series to make a battery': 1.0,\n",
-                " 'Lower voltage cut-off [V]': 2.5,\n",
-                " 'Upper voltage cut-off [V]': 4.4,\n",
-                " \"Initial concentration in electrolyte [mol.m-3]\": 1000,\n",
-                " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
-                " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n",
-                " 'Initial temperature [K]': 298.15\n",
-                "}\n",
-                "param = pybamm.ParameterValues(values)\n",
-                "param"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Here we would have got the same result by doing"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 17,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "{'Negative electrode thickness [m]': 8.52e-05,\n",
-                            " 'Separator thickness [m]': 1.2e-05,\n",
-                            " 'Positive electrode thickness [m]': 7.56e-05,\n",
-                            " 'Electrode height [m]': 0.065,\n",
-                            " 'Electrode width [m]': 1.58,\n",
-                            " 'Nominal cell capacity [A.h]': 5.0,\n",
-                            " 'Current function [A]': 5.0,\n",
-                            " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
-                            " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
-                            " 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_ocp_Chen2020(sto)>,\n",
-                            " 'Negative electrode porosity': 0.25,\n",
-                            " 'Negative electrode active material volume fraction': 0.75,\n",
-                            " 'Negative particle radius [m]': 5.86e-06,\n",
-                            " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-                            " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n",
-                            " 'Negative electrode electrons in reaction': 1.0,\n",
-                            " 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n",
-                            " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
-                            " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
-                            " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
-                            " 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_ocp_Chen2020(sto)>,\n",
-                            " 'Positive electrode porosity': 0.335,\n",
-                            " 'Positive electrode active material volume fraction': 0.665,\n",
-                            " 'Positive particle radius [m]': 5.22e-06,\n",
-                            " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
-                            " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n",
-                            " 'Positive electrode electrons in reaction': 1.0,\n",
-                            " 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n",
-                            " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
-                            " 'Separator porosity': 0.47,\n",
-                            " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
-                            " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
-                            " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
-                            " 'Reference temperature [K]': 298.15,\n",
-                            " 'Ambient temperature [K]': 298.15,\n",
-                            " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
-                            " 'Number of cells connected in series to make a battery': 1.0,\n",
-                            " 'Lower voltage cut-off [V]': 2.5,\n",
-                            " 'Upper voltage cut-off [V]': 4.2,\n",
-                            " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
-                            " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n",
-                            " 'Initial temperature [K]': 298.15}"
-                        ]
-                    },
-                    "execution_count": 17,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "param_same = pybamm.ParameterValues(\"Chen2020\")\n",
-                "{k: v for k,v in param_same.items() if k in spm._parameter_info}"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Updating a specific parameter"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Once a parameter set has been defined (either via a dictionary or a pre-built set), single parameters can be updated"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Using a constant value:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 18,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Current function [A]\t5.0\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "4.0"
-                        ]
-                    },
-                    "execution_count": 18,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "param.search(\"Current function [A]\")\n",
-                "\n",
-                "param.update({\"Current function [A]\": 4.0})\n",
-                "\n",
-                "param[\"Current function [A]\"]"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Using a function:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 19,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "text/plain": [
-                            "<function __main__.curren_func(time)>"
-                        ]
-                    },
-                    "execution_count": 19,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "def curren_func(time):\n",
-                "    return 1 + pybamm.sin(2 * np.pi * time / 60)\n",
-                "\n",
-                "param.update({\"Current function [A]\": curren_func})\n",
-                "\n",
-                "param[\"Current function [A]\"]"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Plotting parameter functions\n",
-                "\n",
-                "As seen above, functions can be passed as parameter values. These parameter values can then be plotted by using `pybamm.plot`"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Plotting \"Current function \\[A]\""
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 20,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 640x480 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot: >"
-                        ]
-                    },
-                    "execution_count": 20,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "currentfunc = param[\"Current function [A]\"]\n",
-                "time = pybamm.linspace(0, 120, 60)\n",
-                "evaluated = param.evaluate(currentfunc(time))\n",
-                "evaluated = pybamm.Array(evaluated)\n",
-                "pybamm.plot(time, evaluated)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "Taking another such example:\n",
-                "\n",
-                "### Plotting \"Negative electrode exchange-current density \\[A.m-2]\""
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 21,
-            "metadata": {},
-            "outputs": [
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 640x480 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "<AxesSubplot: >"
-                        ]
-                    },
-                    "execution_count": 21,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                }
-            ],
-            "source": [
-                "negative_electrode_exchange_current_density = param[\"Negative electrode exchange-current density [A.m-2]\"]\n",
-                "x = pybamm.linspace(3000,6000,100)\n",
-                "c_n_max = param[\"Maximum concentration in negative electrode [mol.m-3]\"]\n",
-                "evaluated = param.evaluate(negative_electrode_exchange_current_density(1000,x,c_n_max,300))\n",
-                "evaluated = pybamm.Array(evaluated)\n",
-                "pybamm.plot(x, evaluated)\n"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Simulating and solving the model\n",
-                "\n",
-                "Finally we can simulate the model and solve it using `pybamm.Simulation` and `solve` respectively."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 22,
-            "metadata": {},
-            "outputs": [
-                {
-                    "ename": "KeyError",
-                    "evalue": "\"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\"",
-                    "output_type": "error",
-                    "traceback": [
-                        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
-                        "\u001b[0;31mKeyError\u001b[0m: PrimaryBroadcast(0x55db2b43f3b99d37, broadcast, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1] - ((0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1])'], domains={'primary': ['positive electrode'], 'secondary': ['current collector']})",
-                        "\nDuring handling of the above exception, another exception occurred:\n",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
-                        "\u001b[0;31mKeyError\u001b[0m: Subtraction(0x6e57fdf0f90fdbb0, -, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])) + Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]', '(0.00017234666524563961 * Ambient temperature [K] / Negative electrode electrons in reaction) * arcsinh(Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / (2.0 * Negative electrode exchange-current density [A.m-2])) + Negative electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged negative particle concentration [mol.m-3]) / Maximum concentration in negative electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Negative electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})",
-                        "\nDuring handling of the above exception, another exception occurred:\n",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
-                        "\u001b[0;31mKeyError\u001b[0m: Addition(-0x524f51ed4f620efd, +, children=['(0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction) * arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))', 'Positive electrode OCP [V] + 1e-06 * (1.0 / (maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10)) + 1.0 / (-1.0 + maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]) / Maximum concentration in positive electrode [mol.m-3], 0.9999999999), 1e-10))) + (Ambient temperature [K] - Reference temperature [K]) * Positive electrode OCP entropic change [V.K-1]'], domains={'primary': ['current collector']})",
-                        "\nDuring handling of the above exception, another exception occurred:\n",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
-                        "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x36e2f7f52718fd84, *, children=['0.00017234666524563961 * Ambient temperature [K] / Positive electrode electrons in reaction', 'arcsinh(-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2]))'], domains={'primary': ['current collector']})",
-                        "\nDuring handling of the above exception, another exception occurred:\n",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
-                        "\u001b[0;31mKeyError\u001b[0m: Arcsinh(-0x70bcbae05a17171a, function (arcsinh), children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m]) / (2.0 * Positive electrode exchange-current density [A.m-2])'], domains={'primary': ['current collector']})",
-                        "\nDuring handling of the above exception, another exception occurred:\n",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
-                        "\u001b[0;31mKeyError\u001b[0m: Division(0x2d06e4ce68936693, /, children=['-Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Positive electrode thickness [m] / x-average(3.0 * Positive electrode active material volume fraction / Positive particle radius [m])', '2.0 * Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})",
-                        "\nDuring handling of the above exception, another exception occurred:\n",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
-                        "\u001b[0;31mKeyError\u001b[0m: Multiplication(-0x533055788c0787e9, *, children=['2.0', 'Positive electrode exchange-current density [A.m-2]'], domains={'primary': ['current collector']})",
-                        "\nDuring handling of the above exception, another exception occurred:\n",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
-                        "\u001b[0;31mKeyError\u001b[0m: FunctionParameter(0x79a3a2c645f54668, Positive electrode exchange-current density [A.m-2], children=['maximum(Initial concentration in electrolyte [mol.m-3], 1e-08)', 'maximum(minimum(boundary value(X-averaged positive particle concentration [mol.m-3]), 0.99999999 * Maximum concentration in positive electrode [mol.m-3]), 1e-08 * Maximum concentration in positive electrode [mol.m-3])', 'Maximum concentration in positive electrode [mol.m-3]', 'broadcast(Ambient temperature [K])'], domains={'primary': ['current collector']})",
-                        "\nDuring handling of the above exception, another exception occurred:\n",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
-                        "\u001b[0;31mKeyError\u001b[0m: Maximum(-0x10b1c06354524acc, maximum, children=['Initial concentration in electrolyte [mol.m-3]', '1e-08'], domains={})",
-                        "\nDuring handling of the above exception, another exception occurred:\n",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:609\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    608\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
-                        "\u001b[0;31mKeyError\u001b[0m: Parameter(-0x23a8868071606836, Initial concentration in electrolyte [mol.m-3], children=[], domains={})",
-                        "\nDuring handling of the above exception, another exception occurred:\n",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:58\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__getitem__\u001b[39;49m(key)\n\u001b[1;32m     59\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n",
-                        "\u001b[0;31mKeyError\u001b[0m: 'Initial concentration in electrolyte [mol.m-3]'",
-                        "\nDuring handling of the above exception, another exception occurred:\n",
-                        "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-                        "Cell \u001b[0;32mIn[22], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m sim \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mSimulation(spm, parameter_values\u001b[39m=\u001b[39mparam)\n\u001b[1;32m      2\u001b[0m t_eval \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marange(\u001b[39m0\u001b[39m, \u001b[39m3600\u001b[39m, \u001b[39m1\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m sim\u001b[39m.\u001b[39;49msolve(t_eval\u001b[39m=\u001b[39;49mt_eval)\n\u001b[1;32m      4\u001b[0m sim\u001b[39m.\u001b[39mplot()\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:559\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m    556\u001b[0m logs \u001b[39m=\u001b[39m {}\n\u001b[1;32m    558\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moperating_mode \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mwithout experiment\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mdrive cycle\u001b[39m\u001b[39m\"\u001b[39m]:\n\u001b[0;32m--> 559\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbuild(check_model\u001b[39m=\u001b[39;49mcheck_model, initial_soc\u001b[39m=\u001b[39;49minitial_soc)\n\u001b[1;32m    560\u001b[0m     \u001b[39mif\u001b[39;00m save_at_cycles \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    561\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    562\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39msave_at_cycles\u001b[39m\u001b[39m'\u001b[39m\u001b[39m option can only be used if simulating an \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    563\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mExperiment \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    564\u001b[0m         )\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:449\u001b[0m, in \u001b[0;36mSimulation.build\u001b[0;34m(self, check_model, initial_soc)\u001b[0m\n\u001b[1;32m    447\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_built_model \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel\n\u001b[1;32m    448\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 449\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mset_parameters()\n\u001b[1;32m    450\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mMesh(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_geometry, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_submesh_types, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_var_pts)\n\u001b[1;32m    451\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_disc \u001b[39m=\u001b[39m pybamm\u001b[39m.\u001b[39mDiscretisation(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_mesh, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_spatial_methods)\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/simulation.py:399\u001b[0m, in \u001b[0;36mSimulation.set_parameters\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    397\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_unprocessed_model\n\u001b[1;32m    398\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 399\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_parameter_values\u001b[39m.\u001b[39;49mprocess_model(\n\u001b[1;32m    400\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_unprocessed_model, inplace\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m\n\u001b[1;32m    401\u001b[0m     )\n\u001b[1;32m    402\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_parameter_values\u001b[39m.\u001b[39mprocess_geometry(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgeometry)\n\u001b[1;32m    403\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodel \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_model_with_set_params\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:465\u001b[0m, in \u001b[0;36mParameterValues.process_model\u001b[0;34m(self, unprocessed_model, inplace)\u001b[0m\n\u001b[1;32m    462\u001b[0m     new_initial_conditions[new_variable] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(equation)\n\u001b[1;32m    463\u001b[0m model\u001b[39m.\u001b[39minitial_conditions \u001b[39m=\u001b[39m new_initial_conditions\n\u001b[0;32m--> 465\u001b[0m model\u001b[39m.\u001b[39mboundary_conditions \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_boundary_conditions(unprocessed_model)\n\u001b[1;32m    467\u001b[0m new_variables \u001b[39m=\u001b[39m {}\n\u001b[1;32m    468\u001b[0m \u001b[39mfor\u001b[39;00m variable, equation \u001b[39min\u001b[39;00m unprocessed_model\u001b[39m.\u001b[39mvariables\u001b[39m.\u001b[39mitems():\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:541\u001b[0m, in \u001b[0;36mParameterValues.process_boundary_conditions\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m    539\u001b[0m sides \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mleft\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mright\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mnegative tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mpositive tab\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mno tab\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[1;32m    540\u001b[0m \u001b[39mfor\u001b[39;00m variable, bcs \u001b[39min\u001b[39;00m model\u001b[39m.\u001b[39mboundary_conditions\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 541\u001b[0m     processed_variable \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(variable)\n\u001b[1;32m    542\u001b[0m     new_boundary_conditions[processed_variable] \u001b[39m=\u001b[39m {}\n\u001b[1;32m    543\u001b[0m     \u001b[39mfor\u001b[39;00m side \u001b[39min\u001b[39;00m sides:\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m     processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m    612\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m processed_symbol\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:731\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    729\u001b[0m \u001b[39m# Unary operators\u001b[39;00m\n\u001b[1;32m    730\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mUnaryOperator):\n\u001b[0;32m--> 731\u001b[0m     new_child \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mchild)\n\u001b[1;32m    732\u001b[0m     new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_unary_new_copy(new_child)\n\u001b[1;32m    733\u001b[0m     \u001b[39m# ensure domain remains the same\u001b[39;00m\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m     processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m    612\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m processed_symbol\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    718\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m    720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m    721\u001b[0m     \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m     new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m    723\u001b[0m     new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m    724\u001b[0m     \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m     processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m    612\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m processed_symbol\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    718\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m    720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m    721\u001b[0m     \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m     new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m    723\u001b[0m     new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m    724\u001b[0m     \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m     processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m    612\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m processed_symbol\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m    721\u001b[0m     \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m    722\u001b[0m     new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m     new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m    724\u001b[0m     \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m    725\u001b[0m     new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m     processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m    612\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m processed_symbol\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m    747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m     new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m    749\u001b[0m     \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m    751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:748\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    746\u001b[0m \u001b[39m# Functions\u001b[39;00m\n\u001b[1;32m    747\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mFunction):\n\u001b[0;32m--> 748\u001b[0m     new_children \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child) \u001b[39mfor\u001b[39;00m child \u001b[39min\u001b[39;00m symbol\u001b[39m.\u001b[39mchildren]\n\u001b[1;32m    749\u001b[0m     \u001b[39mreturn\u001b[39;00m symbol\u001b[39m.\u001b[39m_function_new_copy(new_children)\n\u001b[1;32m    751\u001b[0m \u001b[39m# Concatenations\u001b[39;00m\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m     processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m    612\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m processed_symbol\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m    721\u001b[0m     \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m    722\u001b[0m     new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m     new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m    724\u001b[0m     \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m    725\u001b[0m     new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m     processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m    612\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m processed_symbol\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:723\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m    721\u001b[0m     \u001b[39m# process children\u001b[39;00m\n\u001b[1;32m    722\u001b[0m     new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mleft)\n\u001b[0;32m--> 723\u001b[0m     new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mright)\n\u001b[1;32m    724\u001b[0m     \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n\u001b[1;32m    725\u001b[0m     new_symbol \u001b[39m=\u001b[39m symbol\u001b[39m.\u001b[39m_binary_new_copy(new_left, new_right)\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m     processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m    612\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m processed_symbol\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:657\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    655\u001b[0m             new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(new_child))\n\u001b[1;32m    656\u001b[0m         \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 657\u001b[0m             new_children\u001b[39m.\u001b[39mappend(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(child))\n\u001b[1;32m    659\u001b[0m \u001b[39m# Create Function or Interpolant or Scalar object\u001b[39;00m\n\u001b[1;32m    660\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(function_name, \u001b[39mtuple\u001b[39m):\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m     processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m    612\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m processed_symbol\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:722\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    718\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(function_out)\n\u001b[1;32m    720\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mBinaryOperator):\n\u001b[1;32m    721\u001b[0m     \u001b[39m# process children\u001b[39;00m\n\u001b[0;32m--> 722\u001b[0m     new_left \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprocess_symbol(symbol\u001b[39m.\u001b[39;49mleft)\n\u001b[1;32m    723\u001b[0m     new_right \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprocess_symbol(symbol\u001b[39m.\u001b[39mright)\n\u001b[1;32m    724\u001b[0m     \u001b[39m# make new symbol, ensure domain remains the same\u001b[39;00m\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:611\u001b[0m, in \u001b[0;36mParameterValues.process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    609\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol]\n\u001b[1;32m    610\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m:\n\u001b[0;32m--> 611\u001b[0m     processed_symbol \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_process_symbol(symbol)\n\u001b[1;32m    612\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_processed_symbols[symbol] \u001b[39m=\u001b[39m processed_symbol\n\u001b[1;32m    614\u001b[0m     \u001b[39mreturn\u001b[39;00m processed_symbol\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:620\u001b[0m, in \u001b[0;36mParameterValues._process_symbol\u001b[0;34m(self, symbol)\u001b[0m\n\u001b[1;32m    617\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"See :meth:`ParameterValues.process_symbol()`.\"\"\"\u001b[39;00m\n\u001b[1;32m    619\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(symbol, pybamm\u001b[39m.\u001b[39mParameter):\n\u001b[0;32m--> 620\u001b[0m     value \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m[symbol\u001b[39m.\u001b[39;49mname]\n\u001b[1;32m    621\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(value, numbers\u001b[39m.\u001b[39mNumber):\n\u001b[1;32m    622\u001b[0m         \u001b[39m# Check not NaN (parameter in csv file but no value given)\u001b[39;00m\n\u001b[1;32m    623\u001b[0m         \u001b[39mif\u001b[39;00m np\u001b[39m.\u001b[39misnan(value):\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/parameters/parameter_values.py:139\u001b[0m, in \u001b[0;36mParameterValues.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    138\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__getitem__\u001b[39m(\u001b[39mself\u001b[39m, key):\n\u001b[0;32m--> 139\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dict_items[key]\n",
-                        "File \u001b[0;32m~/Code/PyBaMM/pybamm/util.py:73\u001b[0m, in \u001b[0;36mFuzzyDict.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m key \u001b[39min\u001b[39;00m k \u001b[39mand\u001b[39;00m k\u001b[39m.\u001b[39mendswith(\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m     70\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\n\u001b[1;32m     71\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Use the dimensional version \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mk\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m instead.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m     72\u001b[0m         )\n\u001b[0;32m---> 73\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mkey\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m not found. Best matches are \u001b[39m\u001b[39m{\u001b[39;00mbest_matches\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n",
-                        "\u001b[0;31mKeyError\u001b[0m: \"'Initial concentration in electrolyte [mol.m-3]' not found. Best matches are ['Initial concentration in positive electrode [mol.m-3]', 'Initial concentration in negative electrode [mol.m-3]', 'Maximum concentration in positive electrode [mol.m-3]']\""
-                    ]
-                }
-            ],
-            "source": [
-                "sim = pybamm.Simulation(spm, parameter_values=param)\n",
-                "t_eval = np.arange(0, 3600, 1)\n",
-                "sim.solve(t_eval=t_eval)\n",
-                "sim.plot()"
-            ]
-        },
-        {
-            "attachments": {},
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## References\n",
-                "The relevant papers for this notebook are:"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 23,
-            "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
-                        "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n",
-                        "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
-                        "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
-                        "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
-                        "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
-                        "\n"
-                    ]
-                }
-            ],
-            "source": [
-                "pybamm.print_citations()"
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "pybamm",
-            "language": "python",
-            "name": "python3"
-        },
-        "language_info": {
-            "codemirror_mode": {
-                "name": "ipython",
-                "version": 3
-            },
-            "file_extension": ".py",
-            "mimetype": "text/x-python",
-            "name": "python",
-            "nbconvert_exporter": "python",
-            "pygments_lexer": "ipython3",
-            "version": "3.9.16"
-        },
-        "toc": {
-            "base_numbering": 1,
-            "nav_menu": {},
-            "number_sections": true,
-            "sideBar": true,
-            "skip_h1_title": false,
-            "title_cell": "Table of Contents",
-            "title_sidebar": "Contents",
-            "toc_cell": false,
-            "toc_position": {},
-            "toc_section_display": true,
-            "toc_window_display": true
-        },
-        "vscode": {
-            "interpreter": {
-                "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c"
-            }
-        }
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Parameterisation\n",
+    "\n",
+    "In this notebook, we show how to find which parameters are needed in a model and define them.\n",
+    "\n",
+    "For other notebooks about parameterization, see:\n",
+    "\n",
+    "- The API documentation of [Parameters](https://docs.pybamm.org/en/latest/source/api/parameters/index.html)\n",
+    "- [Setting parameter values](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb) can be found at `pybamm/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb`. This explains the basics of how to set the parameters of a model (in less detail than here).\n",
+    "- [parameter-values.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/parameterization/parameter-values.ipynb) can be found at `pybamm/examples/notebooks/parameterization/parameter-values.ipynb`. This explains the basics of the `ParameterValues` class.\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding your own parameter sets (using a dictionary)\n",
+    "\n",
+    "We will be using the model defined and explained in more detail in [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example notebook. We begin by importing the required libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zsh:1: no matches found: pybamm[plot,cite]\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install pybamm[plot,cite] -q    # install PyBaMM if it is not installed\n",
+    "import pybamm\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up the model\n",
+    "\n",
+    "We define all the parameters and variables using `pybamm.Parameter` and `pybamm.Variable` respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c = pybamm.Variable(\"Concentration [mol.m-3]\", domain=\"negative particle\")\n",
+    "\n",
+    "R = pybamm.Parameter(\"Particle radius [m]\")\n",
+    "D = pybamm.FunctionParameter(\"Diffusion coefficient [m2.s-1]\", {\"Concentration [mol.m-3]\": c})\n",
+    "j = pybamm.InputParameter(\"Interfacial current density [A.m-2]\")\n",
+    "c0 = pybamm.Parameter(\"Initial concentration [mol.m-3]\")\n",
+    "c_e = pybamm.Parameter(\"Electrolyte concentration [mol.m-3]\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we define our model equations, boundary and initial conditions. We also add the variables required using the dictionary `model.variables`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = pybamm.BaseModel()\n",
+    "\n",
+    "# governing equations\n",
+    "N = -D * pybamm.grad(c)  # flux\n",
+    "dcdt = -pybamm.div(N)\n",
+    "model.rhs = {c: dcdt}  \n",
+    "\n",
+    "# boundary conditions \n",
+    "lbc = pybamm.Scalar(0)\n",
+    "rbc = -j\n",
+    "model.boundary_conditions = {c: {\"left\": (lbc, \"Neumann\"), \"right\": (rbc, \"Neumann\")}}\n",
+    "\n",
+    "# initial conditions \n",
+    "model.initial_conditions = {c: c0}\n",
+    "\n",
+    "model.variables = {\n",
+    "    \"Concentration [mol.m-3]\": c,\n",
+    "    \"Surface concentration [mol.m-3]\": pybamm.surf(c),\n",
+    "    \"Flux [mol.m-2.s-1]\": N,\n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We also define the geometry, since there are parameters in the geometry too"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = pybamm.SpatialVariable(\"r\", domain=[\"negative particle\"], coord_sys=\"spherical polar\")\n",
+    "geometry = pybamm.Geometry({\"negative particle\": {r: {\"min\": pybamm.Scalar(0), \"max\": R}}})"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Finding the parameters required"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To know what parameters are required by the model and geometry, we can do"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Initial concentration [mol.m-3] (Parameter)\n",
+      "Interfacial current density [A.m-2] (InputParameter)\n",
+      "Diffusion coefficient [m2.s-1] (FunctionParameter with input(s) 'Concentration [mol.m-3]')\n",
+      "\n",
+      "Particle radius [m] (Parameter)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.print_parameter_info()\n",
+    "geometry.print_parameter_info()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This tells us that we need to provide parameter values for the initial concentration and Faraday constant, an `InputParameter` at solve time for the interfacial current density, and diffusivity as a function of concentration. Since the electrolyte concentration does not appear anywhere in the model, there is no need to provide a value for it."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding the parameters\n",
+    "\n",
+    "Now we can proceed to the step where we add the `parameter` values using a dictionary. We set up a dictionary with parameter names as the dictionary keys and their respective values as the dictionary values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def D_fun(c):\n",
+    "    return 3.9 #* pybamm.exp(-c)\n",
+    "\n",
+    "\n",
+    "values = {\n",
+    "    \"Particle radius [m]\": 2,\n",
+    "    \"Diffusion coefficient [m2.s-1]\": D_fun,\n",
+    "    \"Initial concentration [mol.m-3]\": 2.5,\n",
+    "}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can pass this dictionary in `pybamm.ParameterValues` class which accepts a dictionary of parameter names and values. We can then print `param` to check if it was initialised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
+       " 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x140e14b80>,\n",
+       " 'Electron charge [C]': 1.602176634e-19,\n",
+       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
+       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
+       " 'Initial concentration [mol.m-3]': 2.5,\n",
+       " 'Particle radius [m]': 2}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param = pybamm.ParameterValues(values)\n",
+    "\n",
+    "param"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Updating the parameter values\n",
+    "\n",
+    "The parameter values or `param` can be further updated by using the `update` function of `ParameterValues` class. The `update` function takes a dictionary with keys being the parameters to be updated and their respective values being the updated values. Here we update the `\"Particle radius [m]\"` parameter's value. Additionally, a function can also be passed as a `parameter`'s value which we will see ahead, and a new `parameter` can also be added by passing `check_already_exists=False` in the `update` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
+       " 'Diffusion coefficient [m2.s-1]': <function D_fun at 0x140e14b80>,\n",
+       " 'Electron charge [C]': 1.602176634e-19,\n",
+       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
+       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
+       " 'Initial concentration [mol.m-3]': 1.5,\n",
+       " 'Particle radius [m]': 2}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param.update({\"Initial concentration [mol.m-3]\": 1.5})\n",
+    "param"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Solving the model "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Finding the parameters in a model\n",
+    "\n",
+    "The `parameter` function of the `BaseModel` class can be used to obtain the parameters of a model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Parameter(-0x6a2dafa7592b0120, Initial concentration [mol.m-3], children=[], domains={}),\n",
+       " InputParameter(0x217db8be7d80d00, Interfacial current density [A.m-2], children=[], domains={}),\n",
+       " FunctionParameter(-0x1834ea6ea33ab3ac, Diffusion coefficient [m2.s-1], children=['Concentration [mol.m-3]'], domains={'primary': ['negative particle']})]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "parameters = model.parameters\n",
+    "parameters"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As explained in the [3-negative-particle-problem.ipynb](https://github.com/pybamm-team/PyBaMM/blob/develop/docs/source/examples/notebooks/creating_models/3-negative-particle-problem.ipynb) example, we first process both the `model` and the `geometry`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param.process_model(model)\n",
+    "param.process_geometry(geometry)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can now set up our mesh, choose a spatial method, and discretise our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submesh_types = {\"negative particle\": pybamm.Uniform1DSubMesh}\n",
+    "var_pts = {r: 20}\n",
+    "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n",
+    "\n",
+    "spatial_methods = {\"negative particle\": pybamm.FiniteVolume()}\n",
+    "disc = pybamm.Discretisation(mesh, spatial_methods)\n",
+    "disc.process_model(model);"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We choose a solver and times at which we want the solution returned, and solve the model. Here we give a value for the current density `j`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1300x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# solve\n",
+    "solver = pybamm.ScipySolver()\n",
+    "t = np.linspace(0, 3600, 600)\n",
+    "solution = solver.solve(model, t, inputs={\"Interfacial current density [A.m-2]\": 1.4})\n",
+    "\n",
+    "# post-process, so that the solution can be called at any time t or space r\n",
+    "# (using interpolation)\n",
+    "c = solution[\"Concentration [mol.m-3]\"]\n",
+    "c_surf = solution[\"Surface concentration [mol.m-3]\"]\n",
+    "\n",
+    "# plot\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 4))\n",
+    "\n",
+    "ax1.plot(solution.t, c_surf(solution.t))\n",
+    "ax1.set_xlabel(\"Time [s]\")\n",
+    "ax1.set_ylabel(\"Surface concentration [mol.m-3]\")\n",
+    "\n",
+    "rsol = mesh[\"negative particle\"].nodes # radial position\n",
+    "time = 1000  # time in seconds\n",
+    "ax2.plot(rsol * 1e6, c(t=time, r=rsol), label=\"t={}[s]\".format(time))\n",
+    "ax2.set_xlabel(\"Particle radius [microns]\")\n",
+    "ax2.set_ylabel(\"Concentration [mol.m-3]\")\n",
+    "ax2.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using pre-defined models in `PyBaMM`\n",
+    "\n",
+    "In the next few steps, we will be showing the same workflow with the Single Particle Model (`SPM`). We will also see how you can pass a function as a `parameter`'s value and how to plot such `parameter functions`.\n",
+    "\n",
+    "We start by initializing our model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spm = pybamm.lithium_ion.SPM()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Finding the parameters in a model\n",
+    "\n",
+    "We can print the `parameters` of a model by using the `get_parameters_info` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Negative electrode Bruggeman coefficient (electrolyte) (Parameter)\n",
+      "Positive electrode Bruggeman coefficient (electrode) (Parameter)\n",
+      "Lower voltage cut-off [V] (Parameter)\n",
+      "Faraday constant [C.mol-1] (Parameter)\n",
+      "Ideal gas constant [J.K-1.mol-1] (Parameter)\n",
+      "Electrode width [m] (Parameter)\n",
+      "Positive electrode thickness [m] (Parameter)\n",
+      "Separator Bruggeman coefficient (electrolyte) (Parameter)\n",
+      "Positive electrode Bruggeman coefficient (electrolyte) (Parameter)\n",
+      "Upper voltage cut-off [V] (Parameter)\n",
+      "Number of electrodes connected in parallel to make a cell (Parameter)\n",
+      "Maximum concentration in negative electrode [mol.m-3] (Parameter)\n",
+      "Nominal cell capacity [A.h] (Parameter)\n",
+      "Reference temperature [K] (Parameter)\n",
+      "Maximum concentration in positive electrode [mol.m-3] (Parameter)\n",
+      "Separator thickness [m] (Parameter)\n",
+      "Initial concentration in electrolyte [mol.m-3] (Parameter)\n",
+      "Negative electrode Bruggeman coefficient (electrode) (Parameter)\n",
+      "Electrode height [m] (Parameter)\n",
+      "Number of cells connected in series to make a battery (Parameter)\n",
+      "Negative electrode thickness [m] (Parameter)\n",
+      "Ambient temperature [K] (FunctionParameter with input(s) 'Distance across electrode width [m]', 'Distance across electrode height [m]', 'Time [s]')\n",
+      "Positive electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Maximum positive particle surface concentration [mol.m-3]')\n",
+      "Positive electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
+      "Negative electrode OCP [V] (FunctionParameter with input(s) 'Negative particle stoichiometry')\n",
+      "Negative electrode OCP entropic change [V.K-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Maximum negative particle surface concentration [mol.m-3]')\n",
+      "Negative particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
+      "Initial concentration in positive electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n",
+      "Positive particle radius [m] (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
+      "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
+      "Negative electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]')\n",
+      "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n",
+      "Positive electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n",
+      "Positive electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
+      "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n",
+      "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n",
+      "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
+      "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n",
+      "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n",
+      "Positive electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Positive particle surface concentration [mol.m-3]', 'Maximum positive particle surface concentration [mol.m-3]', 'Temperature [K]')\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "spm.print_parameter_info()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that there are no `InputParameter` objects in the default SPM. Also, note that if a `FunctionParameter` is expected, it is ok to provide a scalar (parameter) instead. However, if a `Parameter` is expected, you cannot provide a function instead."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another way to view what parameters are needed is to print the default parameter values. This can also be used to get some good defaults (but care must be taken when combining parameters across datasets and chemistries)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
+       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
+       " 'Negative electrode thickness [m]': 0.0001,\n",
+       " 'Separator thickness [m]': 2.5e-05,\n",
+       " 'Positive electrode thickness [m]': 0.0001,\n",
+       " 'Electrode height [m]': 0.137,\n",
+       " 'Electrode width [m]': 0.207,\n",
+       " 'Nominal cell capacity [A.h]': 0.680616,\n",
+       " 'Current function [A]': 0.680616,\n",
+       " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n",
+       " 'Negative electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T)>,\n",
+       " 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_mcmb2528_ocp_Dualfoil1998(sto)>,\n",
+       " 'Negative electrode porosity': 0.3,\n",
+       " 'Negative electrode active material volume fraction': 0.6,\n",
+       " 'Negative particle radius [m]': 1e-05,\n",
+       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
+       " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
+       " 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n",
+       " 'Negative electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.graphite_entropic_change_Moura2016(sto, c_s_max)>,\n",
+       " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n",
+       " 'Positive electrode diffusivity [m2.s-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_diffusivity_Dualfoil1998(sto, T)>,\n",
+       " 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_ocp_Dualfoil1998(sto)>,\n",
+       " 'Positive electrode porosity': 0.3,\n",
+       " 'Positive electrode active material volume fraction': 0.5,\n",
+       " 'Positive particle radius [m]': 1e-05,\n",
+       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
+       " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
+       " 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_electrolyte_exchange_current_density_Dualfoil1998(c_e, c_s_surf, c_s_max, T)>,\n",
+       " 'Positive electrode OCP entropic change [V.K-1]': <function pybamm.input.parameters.lithium_ion.Marquis2019.lico2_entropic_change_Moura2016(sto, c_s_max)>,\n",
+       " 'Separator porosity': 1.0,\n",
+       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
+       " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
+       " 'Reference temperature [K]': 298.15,\n",
+       " 'Ambient temperature [K]': 298.15,\n",
+       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
+       " 'Number of cells connected in series to make a battery': 1.0,\n",
+       " 'Lower voltage cut-off [V]': 3.105,\n",
+       " 'Upper voltage cut-off [V]': 4.1,\n",
+       " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n",
+       " 'Initial concentration in positive electrode [mol.m-3]': 30730.7554385565}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{k: v for k,v in spm.default_parameter_values.items() if k in spm._parameter_info}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now define a dictionary of values for `ParameterValues` as before (here, a subset of the `Chen2020` parameters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Ambient temperature [K]': 298.15,\n",
+       " 'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
+       " 'Current function [A]': 5.0,\n",
+       " 'Electrode height [m]': 0.065,\n",
+       " 'Electrode width [m]': 1.58,\n",
+       " 'Electron charge [C]': 1.602176634e-19,\n",
+       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
+       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
+       " 'Initial concentration in electrolyte [mol.m-3]': 1000,\n",
+       " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
+       " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n",
+       " 'Initial temperature [K]': 298.15,\n",
+       " 'Lower voltage cut-off [V]': 2.5,\n",
+       " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
+       " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
+       " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
+       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
+       " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020',\n",
+       "                                ([array([0.        , 0.03129623, 0.03499902, 0.0387018 , 0.04240458,\n",
+       "       0.04610736, 0.04981015, 0.05351292, 0.05721568, 0.06091845,\n",
+       "       0.06462122, 0.06832399, 0.07202675, 0.07572951, 0.07943227,\n",
+       "       0.08313503, 0.08683779, 0.09054054, 0.09424331, 0.09794607,\n",
+       "       0.10164883, 0.10535158, 0.10905434, 0.1127571 , 0.11645985,\n",
+       "       0.12016261, 0.12386536, 0.12756811, 0.13127086, 0.13497362,\n",
+       "       0.13867638, 0.14237913, 0.14608189, 0.14978465, 0.15348741,\n",
+       "       0.15719018, 0.16089294, 0.1645957 , 0.16829847, 0.17200122,\n",
+       "       0.17570399, 0.17940674, 0.1831095 , 0.18681229, 0.19051504,\n",
+       "       0.1942178 , 0.19792056, 0.20162334, 0.2053261 , 0.20902886,\n",
+       "       0.21273164, 0.2164344 , 0.22013716, 0.22383993, 0.2275427 ,\n",
+       "       0.23124547, 0.23494825, 0.23865101, 0.24235377, 0.24605653,\n",
+       "       0.2497593 , 0.25346208, 0.25716486, 0.26086762, 0.26457039,\n",
+       "       0.26827314, 0.2719759 , 0.27567867, 0.27938144, 0.28308421,\n",
+       "       0.28678698, 0.29048974, 0.29419251, 0.29789529, 0.30159806,\n",
+       "       0.30530083, 0.30900361, 0.31270637, 0.31640913, 0.32011189,\n",
+       "       0.32381466, 0.32751744, 0.33122021, 0.33492297, 0.33862575,\n",
+       "       0.34232853, 0.34603131, 0.34973408, 0.35343685, 0.35713963,\n",
+       "       0.36084241, 0.36454517, 0.36824795, 0.37195071, 0.37565348,\n",
+       "       0.37935626, 0.38305904, 0.38676182, 0.3904646 , 0.39416737,\n",
+       "       0.39787015, 0.40157291, 0.40527567, 0.40897844, 0.41268121,\n",
+       "       0.41638398, 0.42008676, 0.42378953, 0.4274923 , 0.43119506,\n",
+       "       0.43489784, 0.43860061, 0.44230338, 0.44600615, 0.44970893,\n",
+       "       0.45341168, 0.45711444, 0.46081719, 0.46451994, 0.46822269,\n",
+       "       0.47192545, 0.47562821, 0.47933098, 0.48303375, 0.48673651,\n",
+       "       0.49043926, 0.49414203, 0.49784482, 0.50154759, 0.50525036,\n",
+       "       0.50895311, 0.51265586, 0.51635861, 0.52006139, 0.52376415,\n",
+       "       0.52746692, 0.53116969, 0.53487245, 0.53857521, 0.54227797,\n",
+       "       0.54598074, 0.5496835 , 0.55338627, 0.55708902, 0.56079178,\n",
+       "       0.56449454, 0.5681973 , 0.57190006, 0.57560282, 0.57930558,\n",
+       "       0.58300835, 0.58671112, 0.59041389, 0.59411664, 0.59781941,\n",
+       "       0.60152218, 0.60522496, 0.60892772, 0.61263048, 0.61633325,\n",
+       "       0.62003603, 0.6237388 , 0.62744156, 0.63114433, 0.63484711,\n",
+       "       0.63854988, 0.64225265, 0.64595543, 0.64965823, 0.653361  ,\n",
+       "       0.65706377, 0.66076656, 0.66446934, 0.66817212, 0.67187489,\n",
+       "       0.67557767, 0.67928044, 0.68298322, 0.686686  , 0.69038878,\n",
+       "       0.69409156, 0.69779433, 0.70149709, 0.70519988, 0.70890264,\n",
+       "       0.7126054 , 0.71630818, 0.72001095, 0.72371371, 0.72741648,\n",
+       "       0.73111925, 0.73482204, 0.7385248 , 0.74222757, 0.74593034,\n",
+       "       0.74963312, 0.75333589, 0.75703868, 0.76074146, 0.76444422,\n",
+       "       0.76814698, 0.77184976, 0.77555253, 0.77925531, 0.78295807,\n",
+       "       0.78666085, 0.79036364, 0.79406641, 0.79776918, 0.80147197,\n",
+       "       0.80517474, 0.80887751, 0.81258028, 0.81628304, 0.81998581,\n",
+       "       0.82368858, 0.82739136, 0.83109411, 0.83479688, 0.83849965,\n",
+       "       0.84220242, 0.84590519, 0.84960797, 0.85331075, 0.85701353,\n",
+       "       0.86071631, 0.86441907, 0.86812186, 0.87182464, 0.87552742,\n",
+       "       0.87923019, 0.88293296, 0.88663573, 0.89033849, 0.89404126,\n",
+       "       0.89774404, 0.9014468 , 1.        ])],\n",
+       "                                 array([1.81772748, 1.0828807 , 0.99593794, 0.90023398, 0.79649431,\n",
+       "       0.73354429, 0.66664314, 0.64137149, 0.59813869, 0.5670836 ,\n",
+       "       0.54746181, 0.53068399, 0.51304734, 0.49394092, 0.47926274,\n",
+       "       0.46065259, 0.45992726, 0.43801501, 0.42438665, 0.41150269,\n",
+       "       0.40033659, 0.38957134, 0.37756538, 0.36292541, 0.34357086,\n",
+       "       0.3406314 , 0.32299468, 0.31379458, 0.30795386, 0.29207319,\n",
+       "       0.28697687, 0.27405477, 0.2670497 , 0.25857493, 0.25265783,\n",
+       "       0.24826777, 0.2414345 , 0.23362778, 0.22956218, 0.22370236,\n",
+       "       0.22181271, 0.22089651, 0.2194268 , 0.21830064, 0.21845333,\n",
+       "       0.21753715, 0.21719357, 0.21635373, 0.21667822, 0.21738444,\n",
+       "       0.21469313, 0.21541846, 0.21465495, 0.2135479 , 0.21392964,\n",
+       "       0.21074206, 0.20873788, 0.20465319, 0.20205732, 0.19774358,\n",
+       "       0.19444147, 0.19190285, 0.18850531, 0.18581399, 0.18327537,\n",
+       "       0.18157659, 0.17814088, 0.17529686, 0.1719375 , 0.16934161,\n",
+       "       0.16756649, 0.16609676, 0.16414985, 0.16260378, 0.16224113,\n",
+       "       0.160027  , 0.15827096, 0.1588054 , 0.15552238, 0.15580869,\n",
+       "       0.15220118, 0.1511132 , 0.14987253, 0.14874637, 0.14678037,\n",
+       "       0.14620776, 0.14555879, 0.14389819, 0.14359279, 0.14242846,\n",
+       "       0.14038612, 0.13882096, 0.13954628, 0.13946992, 0.13780934,\n",
+       "       0.13973714, 0.13698858, 0.13523254, 0.13441178, 0.1352898 ,\n",
+       "       0.13507985, 0.13647321, 0.13601512, 0.13435452, 0.1334765 ,\n",
+       "       0.1348317 , 0.13275118, 0.13286571, 0.13263667, 0.13456447,\n",
+       "       0.13471718, 0.13395369, 0.13448814, 0.1334765 , 0.13298023,\n",
+       "       0.13259849, 0.13338107, 0.13309476, 0.13275118, 0.13443087,\n",
+       "       0.13315202, 0.132713  , 0.1330184 , 0.13278936, 0.13225491,\n",
+       "       0.13317111, 0.13263667, 0.13187316, 0.13265574, 0.13250305,\n",
+       "       0.13324745, 0.13204496, 0.13242669, 0.13233127, 0.13198769,\n",
+       "       0.13254122, 0.13145325, 0.13298023, 0.13168229, 0.1313578 ,\n",
+       "       0.13235036, 0.13120511, 0.13089971, 0.13109058, 0.13082336,\n",
+       "       0.13011713, 0.129869  , 0.12992626, 0.12942998, 0.12796026,\n",
+       "       0.12862831, 0.12656689, 0.12734947, 0.12509716, 0.12110791,\n",
+       "       0.11839751, 0.11244226, 0.11307214, 0.1092165 , 0.10683058,\n",
+       "       0.10433014, 0.10530359, 0.10056993, 0.09950104, 0.09854668,\n",
+       "       0.09921473, 0.09541635, 0.09980643, 0.0986612 , 0.09560722,\n",
+       "       0.09755413, 0.09612258, 0.09430929, 0.09661885, 0.09366032,\n",
+       "       0.09522548, 0.09535909, 0.09316404, 0.09450016, 0.0930877 ,\n",
+       "       0.09343126, 0.0932404 , 0.09350762, 0.09339309, 0.09291591,\n",
+       "       0.09303043, 0.0926296 , 0.0932404 , 0.09261052, 0.09249599,\n",
+       "       0.09240055, 0.09253416, 0.09209515, 0.09234329, 0.09366032,\n",
+       "       0.09333583, 0.09322131, 0.09264868, 0.09253416, 0.09243873,\n",
+       "       0.09230512, 0.09310678, 0.09165615, 0.09159888, 0.09207606,\n",
+       "       0.09175158, 0.09177067, 0.09236237, 0.09241964, 0.09320222,\n",
+       "       0.09199972, 0.09167523, 0.09322131, 0.09190428, 0.09167523,\n",
+       "       0.09285865, 0.09180884, 0.09150345, 0.09186611, 0.0920188 ,\n",
+       "       0.09320222, 0.09131257, 0.09117896, 0.09133166, 0.09089265,\n",
+       "       0.09058725, 0.09051091, 0.09033912, 0.09041547, 0.0911217 ,\n",
+       "       0.0894611 , 0.08999555, 0.08921297, 0.08881213, 0.08797229,\n",
+       "       0.08709427, 0.08503284, 0.07601531]))),\n",
+       " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
+       " 'Negative electrode active material volume fraction': 0.75,\n",
+       " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
+       " 'Negative electrode electrons in reaction': 1.0,\n",
+       " 'Negative electrode exchange-current density [A.m-2]': <function graphite_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x10403e4c0>,\n",
+       " 'Negative electrode porosity': 0.25,\n",
+       " 'Negative electrode thickness [m]': 8.52e-05,\n",
+       " 'Negative particle radius [m]': 5.86e-06,\n",
+       " 'Nominal cell capacity [A.h]': 5.0,\n",
+       " 'Number of cells connected in series to make a battery': 1.0,\n",
+       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
+       " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
+       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
+       " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020',\n",
+       "                                ([array([0.24879728, 0.26614516, 0.26886763, 0.27159011, 0.27431258,\n",
+       "       0.27703505, 0.27975753, 0.28248   , 0.28520247, 0.28792495,\n",
+       "       0.29064743, 0.29336992, 0.29609239, 0.29881487, 0.30153735,\n",
+       "       0.30425983, 0.30698231, 0.30970478, 0.31242725, 0.31514973,\n",
+       "       0.3178722 , 0.32059466, 0.32331714, 0.32603962, 0.32876209,\n",
+       "       0.33148456, 0.33420703, 0.3369295 , 0.33965197, 0.34237446,\n",
+       "       0.34509694, 0.34781941, 0.3505419 , 0.35326438, 0.35598685,\n",
+       "       0.35870932, 0.3614318 , 0.36415428, 0.36687674, 0.36959921,\n",
+       "       0.37232169, 0.37504418, 0.37776665, 0.38048913, 0.38321161,\n",
+       "       0.38593408, 0.38865655, 0.39137903, 0.39410151, 0.39682398,\n",
+       "       0.39954645, 0.40226892, 0.4049914 , 0.40771387, 0.41043634,\n",
+       "       0.41315882, 0.41588129, 0.41860377, 0.42132624, 0.42404872,\n",
+       "       0.4267712 , 0.42949368, 0.43221616, 0.43493864, 0.43766111,\n",
+       "       0.44038359, 0.44310607, 0.44582856, 0.44855103, 0.45127351,\n",
+       "       0.453996  , 0.45671848, 0.45944095, 0.46216343, 0.46488592,\n",
+       "       0.46760838, 0.47033085, 0.47305333, 0.47577581, 0.47849828,\n",
+       "       0.48122074, 0.48394321, 0.48666569, 0.48938816, 0.49211064,\n",
+       "       0.4948331 , 0.49755557, 0.50027804, 0.50300052, 0.50572298,\n",
+       "       0.50844545, 0.51116792, 0.51389038, 0.51661284, 0.51933531,\n",
+       "       0.52205777, 0.52478024, 0.52750271, 0.53022518, 0.53294765,\n",
+       "       0.53567012, 0.53839258, 0.54111506, 0.54383753, 0.54656   ,\n",
+       "       0.54928247, 0.55200494, 0.5547274 , 0.55744986, 0.56017233,\n",
+       "       0.5628948 , 0.56561729, 0.56833976, 0.57106222, 0.57378469,\n",
+       "       0.57650716, 0.57922963, 0.5819521 , 0.58467456, 0.58739702,\n",
+       "       0.59011948, 0.59284194, 0.5955644 , 0.59828687, 0.60100935,\n",
+       "       0.60373182, 0.60645429, 0.60917677, 0.61189925, 0.61462172,\n",
+       "       0.61734419, 0.62006666, 0.62278914, 0.62551162, 0.62823408,\n",
+       "       0.63095656, 0.63367903, 0.6364015 , 0.63912397, 0.64184645,\n",
+       "       0.64456893, 0.6472914 , 0.65001389, 0.65273637, 0.65545884,\n",
+       "       0.65818131, 0.66090379, 0.66362625, 0.66634874, 0.66907121,\n",
+       "       0.67179369, 0.67451616, 0.67723865, 0.67996113, 0.68268361,\n",
+       "       0.68540608, 0.68812855, 0.69085103, 0.6935735 , 0.69629597,\n",
+       "       0.69901843, 0.7017409 , 0.70446338, 0.70718585, 0.70990833,\n",
+       "       0.71263081, 0.71535328, 0.71807574, 0.72079822, 0.72352069,\n",
+       "       0.72624317, 0.72896564, 0.7316881 , 0.73441057, 0.73713303,\n",
+       "       0.73985551, 0.74257799, 0.74530047, 0.74802293, 0.7507454 ,\n",
+       "       0.75346787, 0.75619034, 0.75891281, 0.76163529, 0.76435776,\n",
+       "       0.76708024, 0.7698027 , 0.77252517, 0.77524765, 0.77797012,\n",
+       "       0.78069258, 0.78341506, 0.78613753, 0.78885999, 0.79158246,\n",
+       "       0.79430494, 0.79702741, 0.79974987, 0.80247234, 0.8051948 ,\n",
+       "       0.80791727, 0.81063974, 0.81336221, 0.81608468, 0.81880714,\n",
+       "       0.82152961, 0.82425208, 0.82697453, 0.829697  , 0.83241946,\n",
+       "       0.83514192, 0.83786439, 0.84058684, 0.84330931, 0.84603177,\n",
+       "       0.84875424, 0.8514767 , 0.85419916, 0.85692162, 0.85964409,\n",
+       "       0.86236656, 0.86508902, 0.86781149, 0.87053395, 0.87325642,\n",
+       "       0.87597888, 0.87870135, 0.88142383, 0.8841463 , 0.88686877,\n",
+       "       0.88959124, 0.89231371, 0.8950362 , 0.89775868, 0.90048116,\n",
+       "       0.90320364, 0.90592613, 1.        ])],\n",
+       "                                 array([4.4       , 4.2935653 , 4.2768621 , 4.2647018 , 4.2540312 ,\n",
+       "       4.2449446 , 4.2364879 , 4.2302647 , 4.2225528 , 4.2182574 ,\n",
+       "       4.213294  , 4.2090373 , 4.2051239 , 4.2012677 , 4.1981564 ,\n",
+       "       4.1955218 , 4.1931167 , 4.1889744 , 4.1881533 , 4.1865883 ,\n",
+       "       4.1850228 , 4.1832285 , 4.1808805 , 4.1805749 , 4.1789522 ,\n",
+       "       4.1768146 , 4.1768146 , 4.1752872 , 4.173111  , 4.1726718 ,\n",
+       "       4.1710877 , 4.1702285 , 4.168797  , 4.1669831 , 4.1655135 ,\n",
+       "       4.1634517 , 4.1598248 , 4.1571712 , 4.154079  , 4.1504135 ,\n",
+       "       4.1466532 , 4.1423388 , 4.1382346 , 4.1338248 , 4.1305799 ,\n",
+       "       4.1272392 , 4.1228104 , 4.1186109 , 4.114182  , 4.1096005 ,\n",
+       "       4.1046948 , 4.1004758 , 4.0956464 , 4.0909696 , 4.0864644 ,\n",
+       "       4.0818448 , 4.077683  , 4.0733309 , 4.0690737 , 4.0647216 ,\n",
+       "       4.0608654 , 4.0564747 , 4.0527525 , 4.0492401 , 4.0450211 ,\n",
+       "       4.041986  , 4.0384736 , 4.035171  , 4.0320406 , 4.0289288 ,\n",
+       "       4.02597   , 4.0227437 , 4.0199757 , 4.0175133 , 4.0149746 ,\n",
+       "       4.0122066 , 4.009954  , 4.0075679 , 4.0050669 , 4.0023184 ,\n",
+       "       3.9995501 , 3.9969349 , 3.9926589 , 3.9889555 , 3.9834003 ,\n",
+       "       3.9783037 , 3.9755929 , 3.9707632 , 3.9681098 , 3.9635665 ,\n",
+       "       3.9594433 , 3.9556634 , 3.9521511 , 3.9479132 , 3.9438281 ,\n",
+       "       3.9400866 , 3.9362304 , 3.9314201 , 3.9283848 , 3.9242232 ,\n",
+       "       3.9192028 , 3.9166257 , 3.9117961 , 3.90815   , 3.9038739 ,\n",
+       "       3.8995597 , 3.8959136 , 3.8909314 , 3.8872662 , 3.8831048 ,\n",
+       "       3.8793442 , 3.8747628 , 3.8702576 , 3.8666878 , 3.8623927 ,\n",
+       "       3.8581741 , 3.854146  , 3.8499846 , 3.8450022 , 3.8422534 ,\n",
+       "       3.8380919 , 3.8341596 , 3.8309333 , 3.8272109 , 3.823164  ,\n",
+       "       3.8192315 , 3.8159864 , 3.8123021 , 3.8090379 , 3.8071671 ,\n",
+       "       3.8040555 , 3.8013639 , 3.7970879 , 3.7953317 , 3.7920673 ,\n",
+       "       3.788383  , 3.7855389 , 3.7838206 , 3.78111   , 3.7794874 ,\n",
+       "       3.7769294 , 3.773608  , 3.7695992 , 3.7690265 , 3.7662776 ,\n",
+       "       3.7642922 , 3.7626889 , 3.7603791 , 3.7575538 , 3.7552056 ,\n",
+       "       3.7533159 , 3.7507198 , 3.7487535 , 3.7471499 , 3.7442865 ,\n",
+       "       3.7423012 , 3.7400677 , 3.7385788 , 3.7345319 , 3.7339211 ,\n",
+       "       3.7301605 , 3.7301033 , 3.7278316 , 3.7251589 , 3.723861  ,\n",
+       "       3.7215703 , 3.7191267 , 3.7172751 , 3.7157097 , 3.7130945 ,\n",
+       "       3.7099447 , 3.7071004 , 3.7045615 , 3.703588  , 3.70208   ,\n",
+       "       3.7002664 , 3.6972122 , 3.6952841 , 3.6929362 , 3.6898055 ,\n",
+       "       3.6890991 , 3.686522  , 3.6849759 , 3.6821697 , 3.6808143 ,\n",
+       "       3.6786573 , 3.6761947 , 3.674763  , 3.6712887 , 3.6697233 ,\n",
+       "       3.6678908 , 3.6652565 , 3.6630611 , 3.660274  , 3.6583652 ,\n",
+       "       3.6554828 , 3.6522949 , 3.6499848 , 3.6470451 , 3.6405547 ,\n",
+       "       3.6383405 , 3.635076  , 3.633549  , 3.6322317 , 3.6306856 ,\n",
+       "       3.6283948 , 3.6268487 , 3.6243098 , 3.6223626 , 3.6193655 ,\n",
+       "       3.6177621 , 3.6158531 , 3.6128371 , 3.6118062 , 3.6094582 ,\n",
+       "       3.6072438 , 3.6049912 , 3.6030822 , 3.6012688 , 3.5995889 ,\n",
+       "       3.5976417 , 3.5951984 , 3.593843  , 3.5916286 , 3.5894907 ,\n",
+       "       3.587429  , 3.5852909 , 3.5834775 , 3.5817785 , 3.5801177 ,\n",
+       "       3.5778842 , 3.5763381 , 3.5737801 , 3.5721002 , 3.5702102 ,\n",
+       "       3.5684922 , 3.5672133 , 3.52302167]))),\n",
+       " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
+       " 'Positive electrode active material volume fraction': 0.665,\n",
+       " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
+       " 'Positive electrode electrons in reaction': 1.0,\n",
+       " 'Positive electrode exchange-current density [A.m-2]': <function nmc_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x1411e7700>,\n",
+       " 'Positive electrode porosity': 0.335,\n",
+       " 'Positive electrode thickness [m]': 7.56e-05,\n",
+       " 'Positive particle radius [m]': 5.22e-06,\n",
+       " 'Reference temperature [K]': 298.15,\n",
+       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
+       " 'Separator porosity': 0.47,\n",
+       " 'Separator thickness [m]': 1.2e-05,\n",
+       " 'Typical current [A]': 5.0,\n",
+       " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
+       " 'Upper voltage cut-off [V]': 4.4}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def graphite_mcmb2528_diffusivity_Dualfoil1998(sto, T):\n",
+    "    D_ref = 3.9 * 10 ** (-14)\n",
+    "    E_D_s = 42770\n",
+    "    arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T))\n",
+    "    return D_ref * arrhenius\n",
+    "\n",
+    "\n",
+    "neg_ocp = np.array([[0.        , 1.81772748],\n",
+    "         [0.03129623, 1.0828807 ],\n",
+    "         [0.03499902, 0.99593794],\n",
+    "         [0.0387018 , 0.90023398],\n",
+    "         [0.04240458, 0.79649431],\n",
+    "         [0.04610736, 0.73354429],\n",
+    "         [0.04981015, 0.66664314],\n",
+    "         [0.05351292, 0.64137149],\n",
+    "         [0.05721568, 0.59813869],\n",
+    "         [0.06091845, 0.5670836 ],\n",
+    "         [0.06462122, 0.54746181],\n",
+    "         [0.06832399, 0.53068399],\n",
+    "         [0.07202675, 0.51304734],\n",
+    "         [0.07572951, 0.49394092],\n",
+    "         [0.07943227, 0.47926274],\n",
+    "         [0.08313503, 0.46065259],\n",
+    "         [0.08683779, 0.45992726],\n",
+    "         [0.09054054, 0.43801501],\n",
+    "         [0.09424331, 0.42438665],\n",
+    "         [0.09794607, 0.41150269],\n",
+    "         [0.10164883, 0.40033659],\n",
+    "         [0.10535158, 0.38957134],\n",
+    "         [0.10905434, 0.37756538],\n",
+    "         [0.1127571 , 0.36292541],\n",
+    "         [0.11645985, 0.34357086],\n",
+    "         [0.12016261, 0.3406314 ],\n",
+    "         [0.12386536, 0.32299468],\n",
+    "         [0.12756811, 0.31379458],\n",
+    "         [0.13127086, 0.30795386],\n",
+    "         [0.13497362, 0.29207319],\n",
+    "         [0.13867638, 0.28697687],\n",
+    "         [0.14237913, 0.27405477],\n",
+    "         [0.14608189, 0.2670497 ],\n",
+    "         [0.14978465, 0.25857493],\n",
+    "         [0.15348741, 0.25265783],\n",
+    "         [0.15719018, 0.24826777],\n",
+    "         [0.16089294, 0.2414345 ],\n",
+    "         [0.1645957 , 0.23362778],\n",
+    "         [0.16829847, 0.22956218],\n",
+    "         [0.17200122, 0.22370236],\n",
+    "         [0.17570399, 0.22181271],\n",
+    "         [0.17940674, 0.22089651],\n",
+    "         [0.1831095 , 0.2194268 ],\n",
+    "         [0.18681229, 0.21830064],\n",
+    "         [0.19051504, 0.21845333],\n",
+    "         [0.1942178 , 0.21753715],\n",
+    "         [0.19792056, 0.21719357],\n",
+    "         [0.20162334, 0.21635373],\n",
+    "         [0.2053261 , 0.21667822],\n",
+    "         [0.20902886, 0.21738444],\n",
+    "         [0.21273164, 0.21469313],\n",
+    "         [0.2164344 , 0.21541846],\n",
+    "         [0.22013716, 0.21465495],\n",
+    "         [0.22383993, 0.2135479 ],\n",
+    "         [0.2275427 , 0.21392964],\n",
+    "         [0.23124547, 0.21074206],\n",
+    "         [0.23494825, 0.20873788],\n",
+    "         [0.23865101, 0.20465319],\n",
+    "         [0.24235377, 0.20205732],\n",
+    "         [0.24605653, 0.19774358],\n",
+    "         [0.2497593 , 0.19444147],\n",
+    "         [0.25346208, 0.19190285],\n",
+    "         [0.25716486, 0.18850531],\n",
+    "         [0.26086762, 0.18581399],\n",
+    "         [0.26457039, 0.18327537],\n",
+    "         [0.26827314, 0.18157659],\n",
+    "         [0.2719759 , 0.17814088],\n",
+    "         [0.27567867, 0.17529686],\n",
+    "         [0.27938144, 0.1719375 ],\n",
+    "         [0.28308421, 0.16934161],\n",
+    "         [0.28678698, 0.16756649],\n",
+    "         [0.29048974, 0.16609676],\n",
+    "         [0.29419251, 0.16414985],\n",
+    "         [0.29789529, 0.16260378],\n",
+    "         [0.30159806, 0.16224113],\n",
+    "         [0.30530083, 0.160027  ],\n",
+    "         [0.30900361, 0.15827096],\n",
+    "         [0.31270637, 0.1588054 ],\n",
+    "         [0.31640913, 0.15552238],\n",
+    "         [0.32011189, 0.15580869],\n",
+    "         [0.32381466, 0.15220118],\n",
+    "         [0.32751744, 0.1511132 ],\n",
+    "         [0.33122021, 0.14987253],\n",
+    "         [0.33492297, 0.14874637],\n",
+    "         [0.33862575, 0.14678037],\n",
+    "         [0.34232853, 0.14620776],\n",
+    "         [0.34603131, 0.14555879],\n",
+    "         [0.34973408, 0.14389819],\n",
+    "         [0.35343685, 0.14359279],\n",
+    "         [0.35713963, 0.14242846],\n",
+    "         [0.36084241, 0.14038612],\n",
+    "         [0.36454517, 0.13882096],\n",
+    "         [0.36824795, 0.13954628],\n",
+    "         [0.37195071, 0.13946992],\n",
+    "         [0.37565348, 0.13780934],\n",
+    "         [0.37935626, 0.13973714],\n",
+    "         [0.38305904, 0.13698858],\n",
+    "         [0.38676182, 0.13523254],\n",
+    "         [0.3904646 , 0.13441178],\n",
+    "         [0.39416737, 0.1352898 ],\n",
+    "         [0.39787015, 0.13507985],\n",
+    "         [0.40157291, 0.13647321],\n",
+    "         [0.40527567, 0.13601512],\n",
+    "         [0.40897844, 0.13435452],\n",
+    "         [0.41268121, 0.1334765 ],\n",
+    "         [0.41638398, 0.1348317 ],\n",
+    "         [0.42008676, 0.13275118],\n",
+    "         [0.42378953, 0.13286571],\n",
+    "         [0.4274923 , 0.13263667],\n",
+    "         [0.43119506, 0.13456447],\n",
+    "         [0.43489784, 0.13471718],\n",
+    "         [0.43860061, 0.13395369],\n",
+    "         [0.44230338, 0.13448814],\n",
+    "         [0.44600615, 0.1334765 ],\n",
+    "         [0.44970893, 0.13298023],\n",
+    "         [0.45341168, 0.13259849],\n",
+    "         [0.45711444, 0.13338107],\n",
+    "         [0.46081719, 0.13309476],\n",
+    "         [0.46451994, 0.13275118],\n",
+    "         [0.46822269, 0.13443087],\n",
+    "         [0.47192545, 0.13315202],\n",
+    "         [0.47562821, 0.132713  ],\n",
+    "         [0.47933098, 0.1330184 ],\n",
+    "         [0.48303375, 0.13278936],\n",
+    "         [0.48673651, 0.13225491],\n",
+    "         [0.49043926, 0.13317111],\n",
+    "         [0.49414203, 0.13263667],\n",
+    "         [0.49784482, 0.13187316],\n",
+    "         [0.50154759, 0.13265574],\n",
+    "         [0.50525036, 0.13250305],\n",
+    "         [0.50895311, 0.13324745],\n",
+    "         [0.51265586, 0.13204496],\n",
+    "         [0.51635861, 0.13242669],\n",
+    "         [0.52006139, 0.13233127],\n",
+    "         [0.52376415, 0.13198769],\n",
+    "         [0.52746692, 0.13254122],\n",
+    "         [0.53116969, 0.13145325],\n",
+    "         [0.53487245, 0.13298023],\n",
+    "         [0.53857521, 0.13168229],\n",
+    "         [0.54227797, 0.1313578 ],\n",
+    "         [0.54598074, 0.13235036],\n",
+    "         [0.5496835 , 0.13120511],\n",
+    "         [0.55338627, 0.13089971],\n",
+    "         [0.55708902, 0.13109058],\n",
+    "         [0.56079178, 0.13082336],\n",
+    "         [0.56449454, 0.13011713],\n",
+    "         [0.5681973 , 0.129869  ],\n",
+    "         [0.57190006, 0.12992626],\n",
+    "         [0.57560282, 0.12942998],\n",
+    "         [0.57930558, 0.12796026],\n",
+    "         [0.58300835, 0.12862831],\n",
+    "         [0.58671112, 0.12656689],\n",
+    "         [0.59041389, 0.12734947],\n",
+    "         [0.59411664, 0.12509716],\n",
+    "         [0.59781941, 0.12110791],\n",
+    "         [0.60152218, 0.11839751],\n",
+    "         [0.60522496, 0.11244226],\n",
+    "         [0.60892772, 0.11307214],\n",
+    "         [0.61263048, 0.1092165 ],\n",
+    "         [0.61633325, 0.10683058],\n",
+    "         [0.62003603, 0.10433014],\n",
+    "         [0.6237388 , 0.10530359],\n",
+    "         [0.62744156, 0.10056993],\n",
+    "         [0.63114433, 0.09950104],\n",
+    "         [0.63484711, 0.09854668],\n",
+    "         [0.63854988, 0.09921473],\n",
+    "         [0.64225265, 0.09541635],\n",
+    "         [0.64595543, 0.09980643],\n",
+    "         [0.64965823, 0.0986612 ],\n",
+    "         [0.653361  , 0.09560722],\n",
+    "         [0.65706377, 0.09755413],\n",
+    "         [0.66076656, 0.09612258],\n",
+    "         [0.66446934, 0.09430929],\n",
+    "         [0.66817212, 0.09661885],\n",
+    "         [0.67187489, 0.09366032],\n",
+    "         [0.67557767, 0.09522548],\n",
+    "         [0.67928044, 0.09535909],\n",
+    "         [0.68298322, 0.09316404],\n",
+    "         [0.686686  , 0.09450016],\n",
+    "         [0.69038878, 0.0930877 ],\n",
+    "         [0.69409156, 0.09343126],\n",
+    "         [0.69779433, 0.0932404 ],\n",
+    "         [0.70149709, 0.09350762],\n",
+    "         [0.70519988, 0.09339309],\n",
+    "         [0.70890264, 0.09291591],\n",
+    "         [0.7126054 , 0.09303043],\n",
+    "         [0.71630818, 0.0926296 ],\n",
+    "         [0.72001095, 0.0932404 ],\n",
+    "         [0.72371371, 0.09261052],\n",
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+    "         [0.9014468 , 0.08503284],\n",
+    "         [1.        , 0.07601531]])\n",
+    "\n",
+    "pos_ocp = np.array([[0.24879728, 4.4       ],\n",
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+    "         [0.8950362 , 3.5737801 ],\n",
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+    "         [0.90048116, 3.5702102 ],\n",
+    "         [0.90320364, 3.5684922 ],\n",
+    "         [0.90592613, 3.5672133 ],\n",
+    "         [1.        , 3.52302167]])\n",
+    "\n",
+    "from pybamm import exp, constants\n",
+    "\n",
+    "\n",
+    "def graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_n_max, T):\n",
+    "    m_ref = 6.48e-7  # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n",
+    "    E_r = 35000\n",
+    "    arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n",
+    "\n",
+    "    return (\n",
+    "        m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_n_max - c_s_surf) ** 0.5\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_p_max, T):\n",
+    "    m_ref = 3.42e-6  # (A/m2)(m3/mol)**1.5 - includes ref concentrations\n",
+    "    E_r = 17800\n",
+    "    arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))\n",
+    "\n",
+    "    return (\n",
+    "        m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "values = {\n",
+    " 'Negative electrode thickness [m]': 8.52e-05,\n",
+    " 'Separator thickness [m]': 1.2e-05,\n",
+    " 'Positive electrode thickness [m]': 7.56e-05,\n",
+    " 'Electrode height [m]': 0.065,\n",
+    " 'Electrode width [m]': 1.58,\n",
+    " 'Nominal cell capacity [A.h]': 5.0,\n",
+    " 'Typical current [A]': 5.0,\n",
+    " 'Current function [A]': 5.0,\n",
+    " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
+    " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
+    " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020', neg_ocp),\n",
+    " 'Negative electrode porosity': 0.25,\n",
+    " 'Negative electrode active material volume fraction': 0.75,\n",
+    " 'Negative particle radius [m]': 5.86e-06,\n",
+    " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
+    " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
+    " 'Negative electrode electrons in reaction': 1.0,\n",
+    " 'Negative electrode exchange-current density [A.m-2]': graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\n",
+    " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
+    " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
+    " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
+    " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020', pos_ocp),\n",
+    " 'Positive electrode porosity': 0.335,\n",
+    " 'Positive electrode active material volume fraction': 0.665,\n",
+    " 'Positive particle radius [m]': 5.22e-06,\n",
+    " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
+    " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
+    " 'Positive electrode electrons in reaction': 1.0,\n",
+    " 'Positive electrode exchange-current density [A.m-2]': nmc_LGM50_electrolyte_exchange_current_density_Chen2020,\n",
+    " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
+    " 'Separator porosity': 0.47,\n",
+    " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
+    " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
+    " 'Reference temperature [K]': 298.15,\n",
+    " 'Ambient temperature [K]': 298.15,\n",
+    " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
+    " 'Number of cells connected in series to make a battery': 1.0,\n",
+    " 'Lower voltage cut-off [V]': 2.5,\n",
+    " 'Upper voltage cut-off [V]': 4.4,\n",
+    " \"Initial concentration in electrolyte [mol.m-3]\": 1000,\n",
+    " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
+    " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n",
+    " 'Initial temperature [K]': 298.15\n",
+    "}\n",
+    "param = pybamm.ParameterValues(values)\n",
+    "param"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we would have got the same result by doing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
+       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
+       " 'Negative electrode thickness [m]': 8.52e-05,\n",
+       " 'Separator thickness [m]': 1.2e-05,\n",
+       " 'Positive electrode thickness [m]': 7.56e-05,\n",
+       " 'Electrode height [m]': 0.065,\n",
+       " 'Electrode width [m]': 1.58,\n",
+       " 'Nominal cell capacity [A.h]': 5.0,\n",
+       " 'Current function [A]': 5.0,\n",
+       " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
+       " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n",
+       " 'Negative electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_ocp_Chen2020(sto)>,\n",
+       " 'Negative electrode porosity': 0.25,\n",
+       " 'Negative electrode active material volume fraction': 0.75,\n",
+       " 'Negative particle radius [m]': 5.86e-06,\n",
+       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
+       " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n",
+       " 'Negative electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.graphite_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n",
+       " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
+       " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
+       " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n",
+       " 'Positive electrode OCP [V]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_ocp_Chen2020(sto)>,\n",
+       " 'Positive electrode porosity': 0.335,\n",
+       " 'Positive electrode active material volume fraction': 0.665,\n",
+       " 'Positive particle radius [m]': 5.22e-06,\n",
+       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
+       " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n",
+       " 'Positive electrode exchange-current density [A.m-2]': <function pybamm.input.parameters.lithium_ion.Chen2020.nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T)>,\n",
+       " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
+       " 'Separator porosity': 0.47,\n",
+       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
+       " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
+       " 'Reference temperature [K]': 298.15,\n",
+       " 'Ambient temperature [K]': 298.15,\n",
+       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
+       " 'Number of cells connected in series to make a battery': 1.0,\n",
+       " 'Lower voltage cut-off [V]': 2.5,\n",
+       " 'Upper voltage cut-off [V]': 4.2,\n",
+       " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
+       " 'Initial concentration in positive electrode [mol.m-3]': 17038.0}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_same = pybamm.ParameterValues(\"Chen2020\")\n",
+    "{k: v for k,v in param_same.items() if k in spm._parameter_info}"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Updating a specific parameter"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once a parameter set has been defined (either via a dictionary or a pre-built set), single parameters can be updated"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using a constant value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current function [A]\t5.0\n"
+     ]
     },
-    "nbformat": 4,
-    "nbformat_minor": 2
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param.search(\"Current function [A]\")\n",
+    "\n",
+    "param.update({\"Current function [A]\": 4.0})\n",
+    "\n",
+    "param[\"Current function [A]\"]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using a function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.curren_func(time)>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def curren_func(time):\n",
+    "    return 1 + pybamm.sin(2 * np.pi * time / 60)\n",
+    "\n",
+    "\n",
+    "param.update({\"Current function [A]\": curren_func})\n",
+    "\n",
+    "param[\"Current function [A]\"]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting parameter functions\n",
+    "\n",
+    "As seen above, functions can be passed as parameter values. These parameter values can then be plotted by using `pybamm.plot`"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plotting \"Current function \\[A]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "currentfunc = param[\"Current function [A]\"]\n",
+    "time = pybamm.linspace(0, 120, 60)\n",
+    "evaluated = param.evaluate(currentfunc(time))\n",
+    "evaluated = pybamm.Array(evaluated)\n",
+    "pybamm.plot(time, evaluated)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Taking another such example:\n",
+    "\n",
+    "### Plotting \"Negative electrode exchange-current density \\[A.m-2]\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "negative_electrode_exchange_current_density = param[\"Negative electrode exchange-current density [A.m-2]\"]\n",
+    "x = pybamm.linspace(3000,6000,100)\n",
+    "c_n_max = param[\"Maximum concentration in negative electrode [mol.m-3]\"]\n",
+    "evaluated = param.evaluate(negative_electrode_exchange_current_density(1000,x,c_n_max,300))\n",
+    "evaluated = pybamm.Array(evaluated)\n",
+    "pybamm.plot(x, evaluated)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simulating and solving the model\n",
+    "\n",
+    "Finally we can simulate the model and solve it using `pybamm.Simulation` and `solve` respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eea07489478640aab13bd2aab1fe5020",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=3599.0, step=35.99), Output()), _dom_classes…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x1412c2e50>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sim = pybamm.Simulation(spm, parameter_values=param)\n",
+    "t_eval = np.arange(0, 3600, 1)\n",
+    "sim.solve(t_eval=t_eval)\n",
+    "sim.plot()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "The relevant papers for this notebook are:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n",
+      "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n",
+      "[3] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n",
+      "[4] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
+      "[5] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "[6] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pybamm.print_citations()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": true
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
 }
diff --git a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
index 905850a7fd..258c37c885 100644
--- a/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
+++ b/docs/source/examples/notebooks/solvers/speed-up-solver.ipynb
@@ -23,6 +23,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "zsh:1: no matches found: pybamm[plot,cite]\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -114,8 +115,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Safe: 153.951 ms\n",
-      "Fast: 88.029 ms\n"
+      "Safe: 125.714 ms\n",
+      "Fast: 77.698 ms\n"
      ]
     },
     {
@@ -160,17 +161,17 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 506.167 and h = 8.15178e-16, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+      "At t = 506.167, , mxstep steps taken before reaching tout.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Safe: 403.072 ms\n",
+      "Safe: 7.791 s\n",
       "Solving fast mode, error occured: Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:1401:\n",
       "Error in Function::call for 'F' [IdasInterface] at .../casadi/core/function.cpp:330:\n",
-      ".../casadi/interfaces/sundials/idas_interface.cpp:564: IDASolve returned \"IDA_CONV_FAIL\". Consult IDAS documentation.\n"
+      ".../casadi/interfaces/sundials/idas_interface.cpp:604: IDASolve returned \"IDA_CONV_FAIL\". Consult IDAS documentation.\n"
      ]
     },
     {
@@ -203,7 +204,7 @@
     "try:\n",
     "    sim.solve([0,4500], solver=fast_solver, inputs={\"Crate\": 1})\n",
     "except pybamm.SolverError as e:\n",
-    "    print(\"Solving fast mode, error occured:\", e.args[0])"
+    "    print(\"Solving fast mode, error occurred:\", e.args[0])"
    ]
   },
   {
@@ -221,7 +222,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "99244556077647bcaf7b21b9b1c40acb",
+       "model_id": "d84d30bf7d8d4df1a330e8c9a69267a1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -345,12 +346,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With dt_max=10, took 669.420 ms (integration time: 590.350 ms)\n",
-      "With dt_max=20, took 686.120 ms (integration time: 599.137 ms)\n",
-      "With dt_max=100, took 369.348 ms (integration time: 319.797 ms)\n",
-      "With dt_max=1000, took 91.474 ms (integration time: 57.455 ms)\n",
-      "With dt_max=3700, took 57.838 ms (integration time: 37.095 ms)\n",
-      "With 'fast' mode, took 49.520 ms (integration time: 37.400 ms)\n"
+      "With dt_max=10, took 610.021 ms (integration time: 534.636 ms)\n",
+      "With dt_max=20, took 686.939 ms (integration time: 536.861 ms)\n",
+      "With dt_max=100, took 338.657 ms (integration time: 291.815 ms)\n",
+      "With dt_max=1000, took 83.867 ms (integration time: 51.518 ms)\n",
+      "With dt_max=3700, took 52.384 ms (integration time: 32.960 ms)\n",
+      "With 'fast' mode, took 44.846 ms (integration time: 32.949 ms)\n"
      ]
     }
    ],
@@ -395,20 +396,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "With dt_max=10, took 588.138 ms (integration time: 489.722 ms)\n",
-      "With dt_max=20, took 581.809 ms (integration time: 484.621 ms)\n",
-      "With dt_max=100, took 329.091 ms (integration time: 272.181 ms)\n",
-      "With dt_max=1000, took 113.543 ms (integration time: 69.477 ms)\n",
-      "With dt_max=3600, took 939.024 ms (integration time: 36.933 ms)\n"
+      "With dt_max=10, took 541.980 ms (integration time: 447.827 ms)\n",
+      "With dt_max=20, took 518.415 ms (integration time: 428.332 ms)\n",
+      "With dt_max=100, took 300.344 ms (integration time: 245.695 ms)\n",
+      "With dt_max=1000, took 101.787 ms (integration time: 60.608 ms)\n",
+      "With dt_max=3600, took 516.396 ms (integration time: 32.718 ms)\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 460.712 and h = 1.83699e-16, the corrector convergence failed repeatedly or with |h| = hmin.\n",
-      "At t = 460.712, , mxstep steps taken before reaching tout.\n",
-      "At t = 460.712, , mxstep steps taken before reaching tout.\n"
+      "At t = 460.712 and h = 4.16966e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n",
+      "At t = 460.712 and h = 5.11965e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n",
+      "At t = 460.712 and h = 8.91111e-13, the corrector convergence failed repeatedly or with |h| = hmin.\n"
      ]
     }
    ],
@@ -461,7 +462,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  828.886 ms\n"
+      "Took  813.671 ms\n"
      ]
     }
    ],
@@ -524,7 +525,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  709.638 ms\n"
+      "Took  629.273 ms\n"
      ]
     },
     {
@@ -571,14 +572,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "At t = 1262.29 and h = 1.11482e-19, the corrector convergence failed repeatedly or with |h| = hmin.\n"
+      "At t = 1262.29 and h = 1.0534e-15, the corrector convergence failed repeatedly or with |h| = hmin.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  699.760 ms\n"
+      "Took  539.358 ms\n"
      ]
     },
     {
@@ -625,7 +626,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Took  309.979 ms\n"
+      "Took  289.618 ms\n"
      ]
     },
     {
@@ -809,7 +810,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x28aecf410>"
+       "<matplotlib.legend.Legend at 0x28223f940>"
       ]
      },
      "execution_count": 18,
@@ -854,16 +855,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Exact: 171.144 us\n",
-      "Smooth, k=5: 161.840 us\n",
-      "Smooth, k=10: 137.627 us\n",
-      "Smooth, k=100: 178.807 us\n"
+      "Exact: 172.240 us\n",
+      "Smooth, k=5: 161.790 us\n",
+      "Smooth, k=10: 150.367 us\n",
+      "Smooth, k=100: 193.054 us\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "a7ef8a815d03434bb3f1f4283d50018b",
+       "model_id": "13ea3acf77af43019375fb4f53395b28",
        "version_major": 2,
        "version_minor": 0
       },
@@ -986,7 +987,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "pybamm",
+   "display_name": "dev",
    "language": "python",
    "name": "python3"
   },
@@ -1000,7 +1001,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.9.16"
   },
   "toc": {
    "base_numbering": 1,
@@ -1017,7 +1018,7 @@
   },
   "vscode": {
    "interpreter": {
-    "hash": "187972e187ab8dfbecfab9e8e194ae6d08262b2d51a54fa40644e3ddb6b5f74c"
+    "hash": "bca2b99bfac80e18288b793d52fa0653ab9b5fe5d22e7b211c44eb982a41c00c"
    }
   }
  },
diff --git a/examples/scripts/MSMR.py b/examples/scripts/MSMR.py
new file mode 100644
index 0000000000..4ceb4fc9f4
--- /dev/null
+++ b/examples/scripts/MSMR.py
@@ -0,0 +1,68 @@
+import pybamm
+
+pybamm.set_logging_level("INFO")
+
+# Use the MSMR model, with 6 negative electrode reactions and 4 positive electrode
+# reactions
+msmr_model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")})
+
+# We can also use a SPM with MSMR thermodynamics, transport and kinetics by changing
+# model options. Note we need to se the "surface form" to "algebraic" or "differential"
+# to use the MSMR, since we cannot explicitly invert the kinetics
+spm_msmr_model = pybamm.lithium_ion.SPM(
+    {
+        "number of MSMR reactions": ("6", "4"),
+        "open-circuit potential": "MSMR",
+        "particle": "MSMR",
+        "intercalation kinetics": "MSMR",
+        "surface form": "algebraic",
+    },
+    name="Single Particle MSMR",
+)
+
+# Load in the example MSMR parameter set
+parameter_values = pybamm.ParameterValues("MSMR_Example")
+
+# Define an experiment
+experiment = pybamm.Experiment(
+    [
+        (
+            "Discharge at 1C for 1 hour or until 3 V",
+            "Rest for 1 hour",
+            "Charge at C/3 until 4.2 V",
+            "Hold at 4.2 V until 10 mA",
+            "Rest for 1 hour",
+        ),
+    ]
+)
+
+# Loop over the models, creating and solving a simulation
+sols = []
+for model in [msmr_model, spm_msmr_model]:
+    sim = pybamm.Simulation(
+        model, parameter_values=parameter_values, experiment=experiment
+    )
+    sol = sim.solve(initial_soc=0.9)
+    sols.append(sol)
+
+# Plot the fractional occupancy x_j of the individual MSMR reactions, along with some
+# other variables of interest
+xns = [
+    f"Average x_n_{i}" for i in range(6)
+]  # negative electrode reactions: x_n_0, x_n_1, ..., x_n_5
+xps = [
+    f"Average x_p_{i}" for i in range(4)
+]  # positive electrode reactions: x_p_0, x_p_1, ..., x_p_3
+pybamm.dynamic_plot(
+    sols,
+    [
+        xns,
+        xps,
+        "Current [A]",
+        "Negative electrode interfacial current density [A.m-2]",
+        "Positive electrode interfacial current density [A.m-2]",
+        "Negative particle surface concentration [mol.m-3]",
+        "Positive particle surface concentration [mol.m-3]",
+        "Voltage [V]",
+    ],
+)
diff --git a/pybamm/CITATIONS.bib b/pybamm/CITATIONS.bib
index 221d643683..21740584b5 100644
--- a/pybamm/CITATIONS.bib
+++ b/pybamm/CITATIONS.bib
@@ -36,6 +36,17 @@ @article{Andersson2019
   doi = {10.1007/s12532-018-0139-4},
 }
 
+@article{Baker2018,
+  title={Multi-species, multi-reaction model for porous intercalation electrodes: Part I. Model formulation and a perturbation solution for low-scan-rate, linear-sweep voltammetry of a spinel lithium manganese oxide electrode},
+  author={Baker, Daniel R and Verbrugge, Mark W},
+  journal={Journal of The Electrochemical Society},
+  volume={165},
+  number={16},
+  pages={A3952},
+  year={2018},
+  publisher={IOP Publishing}
+}
+
 @article{BrosaPlanella2021,
   title = {Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods},
   author = {Brosa Planella, Ferran and Sheikh, Muhammad and Widanage, W. Dhammika},
@@ -502,6 +513,17 @@ @article{Valoen2005
   publisher={IOP Publishing}
 }
 
+@article{Verbrugge2017,
+  title={Thermodynamic model for substitutional materials: application to lithiated graphite, spinel manganese oxide, iron phosphate, and layered nickel-manganese-cobalt oxide},
+  author={Verbrugge, Mark and Baker, Daniel and Koch, Brian and Xiao, Xingcheng and Gu, Wentian},
+  journal={Journal of The Electrochemical Society},
+  volume={164},
+  number={11},
+  pages={E3243},
+  year={2017},
+  publisher={IOP Publishing}
+}
+
 @article{Virtanen2020,
   title = {{SciPy 1.0: fundamental algorithms for scientific computing in Python}},
   author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and Haberland, Matt and Reddy, Tyler and Cournapeau, David and Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and Bright, Jonathan and others},
diff --git a/pybamm/expression_tree/broadcasts.py b/pybamm/expression_tree/broadcasts.py
index 7fb34a57b8..32cf2c002b 100644
--- a/pybamm/expression_tree/broadcasts.py
+++ b/pybamm/expression_tree/broadcasts.py
@@ -45,6 +45,11 @@ def _sympy_operator(self, child):
         """Override :meth:`pybamm.UnaryOperator._sympy_operator`"""
         return child
 
+    def _diff(self, variable):
+        """See :meth:`pybamm.Symbol._diff()`."""
+        # Differentiate the child and broadcast the result in the same way
+        return self._unary_new_copy(self.child.diff(variable))
+
 
 class PrimaryBroadcast(Broadcast):
     """
diff --git a/pybamm/expression_tree/unary_operators.py b/pybamm/expression_tree/unary_operators.py
index d8550bb8ae..7f9c45775c 100644
--- a/pybamm/expression_tree/unary_operators.py
+++ b/pybamm/expression_tree/unary_operators.py
@@ -339,11 +339,6 @@ class with a :class:`Matrix`
     def __init__(self, name, child, domains=None):
         super().__init__(name, child, domains)
 
-    def diff(self, variable):
-        """See :meth:`pybamm.Symbol.diff()`."""
-        # We shouldn't need this
-        raise NotImplementedError
-
 
 class Gradient(SpatialOperator):
     """
diff --git a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py
new file mode 100644
index 0000000000..fce5c7f068
--- /dev/null
+++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py
@@ -0,0 +1,201 @@
+def electrolyte_diffusivity_Nyman2008(c_e, T):
+    """
+    Diffusivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data
+    comes from [1]
+
+    References
+    ----------
+    .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and
+    modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte,"
+    Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    D_c_e = 8.794e-11 * (c_e / 1000) ** 2 - 3.972e-10 * (c_e / 1000) + 4.862e-10
+
+    # Nyman et al. (2008) does not provide temperature dependence
+
+    return D_c_e
+
+
+def electrolyte_conductivity_Nyman2008(c_e, T):
+    """
+    Conductivity of LiPF6 in EC:EMC (3:7) as a function of ion concentration. The data
+    comes from [1].
+
+    References
+    ----------
+    .. [1] A. Nyman, M. Behm, and G. Lindbergh, "Electrochemical characterisation and
+    modelling of the mass transport phenomena in LiPF6-EC-EMC electrolyte,"
+    Electrochim. Acta, vol. 53, no. 22, pp. 6356–6365, 2008.
+
+    Parameters
+    ----------
+    c_e: :class:`pybamm.Symbol`
+        Dimensional electrolyte concentration
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
+
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
+    """
+
+    sigma_e = (
+        0.1297 * (c_e / 1000) ** 3 - 2.51 * (c_e / 1000) ** 1.5 + 3.329 * (c_e / 1000)
+    )
+
+    # Nyman et al. (2008) does not provide temperature dependence
+
+    return sigma_e
+
+
+def get_parameter_values():
+    """
+    Example parameter values for use with MSMR models. The thermodynamic parameters
+    are for Graphite and NMC622, and are taken from Table 1 of the paper
+
+        Mark Verbrugge, Daniel Baker, Brian Koch, Xingcheng Xiao and Wentian Gu.
+        Thermodynamic Model for Substitutional Materials: Application to Lithiated
+        Graphite, Spinel Manganese Oxide, Iron Phosphate, and Layered
+        Nickel-Manganese-Cobalt Oxide. Journal of The Electrochemical Society,
+        164(11):3243-3253, 2017. doi:10.1149/2.0341708jes.
+
+    The remaining value are based on a parameterization of the LG M50 cell, from the
+    paper
+
+        Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W.
+        Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for
+        Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The
+        Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.
+
+    and references therein. Verbrugge et al. (2017) does not provide kinetic parameters
+    so we set the reference exchange current density to 5 A.m-2 for the positive
+    electrode reactions and 2.7 A.m-2 for the negative electrode reactions, which are
+    the values used in the Chen et al. (2020) paper. We also assume that the
+    exchange-current density is symmetric. Note: the 4th reaction in the positive
+    electrode gave unphysical results so we set the reference exchange current density
+    and symmetry factor to 1e6 and 1, respectively. The parameter values are intended
+    to serve as an example set to use with the MSMR model and do not claim to match any
+    experimental cycling data.
+    """
+    return {
+        "chemistry": "lithium_ion",
+        # cell
+        "Negative electrode thickness [m]": 8.52e-05,
+        "Separator thickness [m]": 1.2e-05,
+        "Positive electrode thickness [m]": 7.56e-05,
+        "Electrode height [m]": 0.065,
+        "Electrode width [m]": 1.58,
+        "Nominal cell capacity [A.h]": 5.0,
+        "Current function [A]": 5.0,
+        "Contact resistance [Ohm]": 0,
+        # negative electrode
+        "Number of reactions in negative electrode": 6,
+        "U0_n_0": 0.08843,
+        "X_n_0": 0.43336,
+        "w_n_0": 0.08611,
+        "a_n_0": 0.5,
+        "j0_ref_n_0": 2.7,
+        "U0_n_1": 0.12799,
+        "X_n_1": 0.23963,
+        "w_n_1": 0.08009,
+        "a_n_1": 0.5,
+        "j0_ref_n_1": 2.7,
+        "U0_n_2": 0.14331,
+        "X_n_2": 0.15018,
+        "w_n_2": 0.72469,
+        "a_n_2": 0.5,
+        "j0_ref_n_2": 2.7,
+        "U0_n_3": 0.16984,
+        "X_n_3": 0.05462,
+        "w_n_3": 2.53277,
+        "a_n_3": 0.5,
+        "j0_ref_n_3": 2.7,
+        "U0_n_4": 0.21446,
+        "X_n_4": 0.06744,
+        "w_n_4": 0.09470,
+        "a_n_4": 0.5,
+        "j0_ref_n_4": 2.7,
+        "U0_n_5": 0.36325,
+        "X_n_5": 0.05476,
+        "w_n_5": 5.97354,
+        "a_n_5": 0.5,
+        "j0_ref_n_5": 2.7,
+        "Negative electrode conductivity [S.m-1]": 215.0,
+        "Maximum concentration in negative electrode [mol.m-3]": 33133.0,
+        "Negative electrode diffusivity [m2.s-1]": 3.3e-14,
+        "Negative electrode porosity": 0.25,
+        "Negative electrode active material volume fraction": 0.75,
+        "Negative particle radius [m]": 5.86e-06,
+        "Negative electrode Bruggeman coefficient (electrolyte)": 1.5,
+        "Negative electrode Bruggeman coefficient (electrode)": 0,
+        "Negative electrode OCP entropic change [V.K-1]": 0.0,
+        # positive electrode
+        "Number of reactions in positive electrode": 4,
+        "U0_p_0": 3.62274,
+        "X_p_0": 0.13442,
+        "w_p_0": 0.96710,
+        "a_p_0": 0.5,
+        "j0_ref_p_0": 5,
+        "U0_p_1": 3.72645,
+        "X_p_1": 0.32460,
+        "w_p_1": 1.39712,
+        "a_p_1": 0.5,
+        "j0_ref_p_1": 5,
+        "U0_p_2": 3.90575,
+        "X_p_2": 0.21118,
+        "w_p_2": 3.50500,
+        "a_p_2": 0.5,
+        "j0_ref_p_2": 5,
+        "U0_p_3": 4.22955,
+        "X_p_3": 0.32980,
+        "w_p_3": 5.52757,
+        "a_p_3": 1,
+        "j0_ref_p_3": 1e6,
+        "Positive electrode conductivity [S.m-1]": 0.18,
+        "Maximum concentration in positive electrode [mol.m-3]": 63104.0,
+        "Positive electrode diffusivity [m2.s-1]": 4e-15,
+        "Positive electrode porosity": 0.335,
+        "Positive electrode active material volume fraction": 0.665,
+        "Positive particle radius [m]": 5.22e-06,
+        "Positive electrode Bruggeman coefficient (electrolyte)": 1.5,
+        "Positive electrode Bruggeman coefficient (electrode)": 0,
+        "Positive electrode OCP entropic change [V.K-1]": 0.0,
+        # separator
+        "Separator porosity": 0.47,
+        "Separator Bruggeman coefficient (electrolyte)": 1.5,
+        # electrolyte
+        "Initial concentration in electrolyte [mol.m-3]": 1000.0,
+        "Cation transference number": 0.2594,
+        "Thermodynamic factor": 1.0,
+        "Electrolyte diffusivity [m2.s-1]": electrolyte_diffusivity_Nyman2008,
+        "Electrolyte conductivity [S.m-1]": electrolyte_conductivity_Nyman2008,
+        # experiment
+        "Reference temperature [K]": 298.15,
+        "Total heat transfer coefficient [W.m-2.K-1]": 10.0,
+        "Ambient temperature [K]": 298.15,
+        "Number of electrodes connected in parallel to make a cell": 1.0,
+        "Number of cells connected in series to make a battery": 1.0,
+        "Lower voltage cut-off [V]": 2.8,
+        "Upper voltage cut-off [V]": 4.2,
+        "Open-circuit voltage at 0% SOC [V]": 2.8,
+        "Open-circuit voltage at 100% SOC [V]": 4.2,
+        "Initial temperature [K]": 298.15,
+        "Initial voltage in negative electrode [V]": 0.01,
+        "Initial voltage in positive electrode [V]": 4.19,
+        # citations
+        "citations": ["Verbrugge2017", "Baker2018", "Chen2020"],
+    }
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 64ce55ebda..ad36786381 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -7,6 +7,16 @@
 import warnings
 
 
+def represents_positive_integer(s):
+    """Check if a string represents a positive integer"""
+    try:
+        val = int(s)
+    except ValueError:
+        return False
+    else:
+        return val > 0
+
+
 class BatteryModelOptions(pybamm.FuzzyDict):
     """
     Attributes
@@ -64,10 +74,10 @@ class BatteryModelOptions(pybamm.FuzzyDict):
                 "surface form" cannot be 'false'.
             * "intercalation kinetics" : str
                 Model for intercalation kinetics. Can be "symmetric Butler-Volmer"
-                (default), "asymmetric Butler-Volmer", "linear", "Marcus", or
-                "Marcus-Hush-Chidsey" (which uses the asymptotic form from Zeng 2014).
-                A 2-tuple can be provided for different behaviour in negative and
-                positive electrodes.
+                (default), "asymmetric Butler-Volmer", "linear", "Marcus",
+                "Marcus-Hush-Chidsey" (which uses the asymptotic form from Zeng 2014),
+                or "MSMR" (which uses the form from Baker 2018). A 2-tuple can be
+                provided for different behaviour in negative and positive electrodes.
             * "interface utilisation": str
                 Can be "full" (default), "constant", or "current-driven".
             * "lithium plating" : str
@@ -82,9 +92,17 @@ class BatteryModelOptions(pybamm.FuzzyDict):
                 "stress and reaction-driven".
                 A 2-tuple can be provided for different behaviour in negative and
                 positive electrodes.
+            * "number of MSMR reactions" : str
+                Sets the number of reactions to use in the MSMR model in each electrode.
+                A 2-tuple can be provided to give a different number of reactions in
+                the negative and positive electrodes. Default is "none". Can be any
+                2-tuple of strings of integers. For example, set to ("6", "4") for a
+                negative electrode with 6 reactions and a positive electrode with 4
+                reactions.
             * "open-circuit potential" : str
                 Sets the model for the open circuit potential. Can be "single"
-                (default) or "current sigmoid". A 2-tuple can be provided for different
+                (default), "current sigmoid", or "MSMR". If "MSMR" then the "particle"
+                option must also be "MSMR". A 2-tuple can be provided for different
                 behaviour in negative and positive electrodes.
             * "operating mode" : str
                 Sets the operating mode for the model. This determines how the current
@@ -105,8 +123,9 @@ class BatteryModelOptions(pybamm.FuzzyDict):
             * "particle" : str
                 Sets the submodel to use to describe behaviour within the particle.
                 Can be "Fickian diffusion" (default), "uniform profile",
-                "quadratic profile", or "quartic profile". A 2-tuple can be provided
-                for different behaviour in negative and positive electrodes.
+                "quadratic profile", "quartic profile", or "MSMR". If "MSMR" then the
+                "open-circuit potential" option must also be "MSMR". A 2-tuple can be
+                provided for different behaviour in negative and positive electrodes.
             * "particle mechanics" : str
                 Sets the model to account for mechanical effects such as particle
                 swelling and cracking. Can be "none" (default), "swelling only",
@@ -225,6 +244,7 @@ def __init__(self, extra_options):
                 "linear",
                 "Marcus",
                 "Marcus-Hush-Chidsey",
+                "MSMR",
             ],
             "interface utilisation": ["full", "constant", "current-driven"],
             "lithium plating": [
@@ -241,7 +261,8 @@ def __init__(self, extra_options):
                 "current-driven",
                 "stress and reaction-driven",
             ],
-            "open-circuit potential": ["single", "current sigmoid"],
+            "number of MSMR reactions": ["none"],
+            "open-circuit potential": ["single", "current sigmoid", "MSMR"],
             "operating mode": [
                 "current",
                 "voltage",
@@ -259,6 +280,7 @@ def __init__(self, extra_options):
                 "uniform profile",
                 "quadratic profile",
                 "quartic profile",
+                "MSMR",
             ],
             "particle mechanics": ["none", "swelling only", "swelling and cracking"],
             "particle phases": ["1", "2"],
@@ -396,6 +418,25 @@ def __init__(self, extra_options):
                         )
                     )
 
+        # If any of "open-circuit potential", "particle" or "intercalation kinetics" is
+        # "MSMR" then all of them must be "MSMR".
+        # Note: this check is currently performed on full cells, but is loosened for
+        # half-cells where you must pass a tuple of options to only set MSMR models in
+        # the working electrode
+        msmr_check_list = [
+            options[opt] == "MSMR"
+            for opt in ["open-circuit potential", "particle", "intercalation kinetics"]
+        ]
+        if (
+            options["working electrode"] == "both"
+            and any(msmr_check_list)
+            and not all(msmr_check_list)
+        ):
+            raise pybamm.OptionError(
+                "If any of 'open-circuit potential', 'particle' or "
+                "'intercalation kinetics' is 'MSMR' then all of them must be 'MSMR'"
+            )
+
         # If "SEI film resistance" is "distributed" then "total interfacial current
         # density as a state" must be "true"
         if options["SEI film resistance"] == "distributed":
@@ -575,6 +616,7 @@ def __init__(self, extra_options):
                                 "intercalation kinetics",
                                 "interface utilisation",
                                 "loss of active material",
+                                "number of MSMR reactions",
                                 "open-circuit potential",
                                 "particle",
                                 "particle mechanics",
@@ -602,7 +644,16 @@ def __init__(self, extra_options):
                         value_list.append(val)
                 for val in value_list:
                     if val not in self.possible_options[option]:
-                        if not (option == "operating mode" and callable(val)):
+                        if option == "operating mode" and callable(val):
+                            # "operating mode" can be a function
+                            pass
+                        elif (
+                            option == "number of MSMR reactions"
+                            and represents_positive_integer(val)
+                        ):
+                            # "number of MSMR reactions" can be a positive integer
+                            pass
+                        else:
                             raise pybamm.OptionError(
                                 f"\n'{val}' is not recognized in option '{option}'. "
                                 f"Possible values are {self.possible_options[option]}"
@@ -879,6 +930,10 @@ def options(self, extra_options):
                 raise pybamm.OptionError("Lead-acid models cannot have SEI formation")
             if options["lithium plating"] != "none":
                 raise pybamm.OptionError("Lead-acid models cannot have lithium plating")
+            if options["open-circuit potential"] == "MSMR":
+                raise pybamm.OptionError(
+                    "Lead-acid models cannot use the MSMR open-circuit potential model"
+                )
 
         if (
             isinstance(self, pybamm.lead_acid.LOQS)
@@ -1003,6 +1058,8 @@ def get_intercalation_kinetics(self, domain):
             return pybamm.kinetics.Marcus
         elif options["intercalation kinetics"] == "Marcus-Hush-Chidsey":
             return pybamm.kinetics.MarcusHushChidsey
+        elif options["intercalation kinetics"] == "MSMR":
+            return pybamm.kinetics.MSMRButlerVolmer
 
     def get_inverse_intercalation_kinetics(self):
         if self.options["intercalation kinetics"] == "symmetric Butler-Volmer":
@@ -1241,17 +1298,10 @@ def set_voltage_variables(self):
                 "Battery voltage [V]": V * num_cells,
             }
         )
-        # Variables for calculating the equivalent circuit model (ECM) resistance
-        # Need to compare OCV to initial value to capture this as an overpotential
-        ocv_init = self.param.ocv_init
-        eta_ocv = ocv_bulk - ocv_init
-        # Current collector current density for working out euiqvalent resistance
-        # based on Ohm's Law
-        i_cc = self.variables["Current collector current density [A.m-2]"]
+
+        # Calculate equivalent resistance of an OCV-R Equivalent Circuit Model
         # ECM overvoltage is OCV minus voltage
         v_ecm = ocv_bulk - V
-        # Current collector area for turning resistivity into resistance
-        A_cc = self.param.A_cc
 
         # Hack to avoid division by zero if i_cc is exactly zero
         # If i_cc is zero, i_cc_not_zero becomes 1. But multiplying by sign(i_cc) makes
@@ -1259,11 +1309,12 @@ def set_voltage_variables(self):
         def x_not_zero(x):
             return ((x > 0) + (x < 0)) * x + (x >= 0) * (x <= 0)
 
+        i_cc = self.variables["Current collector current density [A.m-2]"]
         i_cc_not_zero = x_not_zero(i_cc)
+        A_cc = self.param.A_cc
 
         self.variables.update(
             {
-                "Change in open-circuit voltage [V]": eta_ocv,
                 "Local ECM resistance [Ohm]": pybamm.sign(i_cc)
                 * v_ecm
                 / (i_cc_not_zero * A_cc),
diff --git a/pybamm/models/full_battery_models/lithium_ion/__init__.py b/pybamm/models/full_battery_models/lithium_ion/__init__.py
index 76625858e3..95a5059f5a 100644
--- a/pybamm/models/full_battery_models/lithium_ion/__init__.py
+++ b/pybamm/models/full_battery_models/lithium_ion/__init__.py
@@ -6,6 +6,8 @@
     ElectrodeSOHSolver,
     get_initial_stoichiometries,
     get_min_max_stoichiometries,
+    get_initial_ocps,
+    get_min_max_ocps,
 )
 from .electrode_soh_half_cell import ElectrodeSOHHalfCell
 from .spm import SPM
@@ -18,3 +20,4 @@
 from .basic_dfn_composite import BasicDFNComposite
 from .Yang2017 import Yang2017
 from .mpm import MPM
+from .msmr import MSMR
diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
index cc615dacf7..41e4670cf7 100644
--- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
+++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
@@ -238,6 +238,8 @@ def set_open_circuit_potential_submodel(self):
                     ocp_model = ocp_submodels.SingleOpenCircuitPotential
                 elif ocp_option == "current sigmoid":
                     ocp_model = ocp_submodels.CurrentSigmoidOpenCircuitPotential
+                elif ocp_option == "MSMR":
+                    ocp_model = ocp_submodels.MSMROpenCircuitPotential
                 self.submodels[f"{domain} {phase} open-circuit potential"] = ocp_model(
                     self.param, domain, reaction, self.options, phase
                 )
diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py
index 6fcebc636d..5f0a2cfb3e 100644
--- a/pybamm/models/full_battery_models/lithium_ion/dfn.py
+++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py
@@ -66,6 +66,10 @@ def set_particle_submodel(self):
                     submod = pybamm.particle.PolynomialProfile(
                         self.param, domain, self.options, phase=phase
                     )
+                elif particle == "MSMR":
+                    submod = pybamm.particle.MSMRDiffusion(
+                        self.param, domain, self.options, phase=phase, x_average=False
+                    )
                 self.submodels[f"{domain} {phase} particle"] = submod
                 self.submodels[
                     f"{domain} {phase} total particle concentration"
diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
index 92996675d4..c6a445f316 100644
--- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
+++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh.py
@@ -7,11 +7,80 @@
 import warnings
 
 
-class _ElectrodeSOH(pybamm.BaseModel):
-    """Model to calculate electrode-specific SOH, from :footcite:t:`Mohtat2019`.
-    This model is mainly for internal use, to calculate summary variables in a
-    simulation.
-    Some of the output variables are defined in [2]_.
+class _BaseElectrodeSOH(pybamm.BaseModel):
+    def __init__(self):
+        pybamm.citations.register("Mohtat2019")
+        pybamm.citations.register("Weng2023")
+        name = "ElectrodeSOH model"
+        super().__init__(name)
+
+    def get_100_soc_variables(
+        self, x_100, y_100, Un_100, Up_100, Q_Li, Q_n, Q_p, param
+    ):
+        Acc_cm2 = param.A_cc * 1e4
+        variables = {
+            "x_100": x_100,
+            "y_100": y_100,
+            "Un(x_100)": Un_100,
+            "Up(y_100)": Up_100,
+            "Up(y_100) - Un(x_100)": Up_100 - Un_100,
+            "Q_Li": Q_Li,
+            "n_Li": Q_Li * 3600 / param.F,
+            "Q_n": Q_n,
+            "Q_p": Q_p,
+            "Cyclable lithium capacity [A.h]": Q_Li,
+            "Negative electrode capacity [A.h]": Q_n,
+            "Positive electrode capacity [A.h]": Q_p,
+            "Cyclable lithium capacity [mA.h.cm-2]": Q_Li * 1e3 / Acc_cm2,
+            "Negative electrode capacity [mA.h.cm-2]": Q_n * 1e3 / Acc_cm2,
+            "Positive electrode capacity [mA.h.cm-2]": Q_p * 1e3 / Acc_cm2,
+            # eq 33 of Weng2023
+            "Formation capacity loss [A.h]": Q_p - Q_Li,
+            "Formation capacity loss [mA.h.cm-2]": (Q_p - Q_Li) * 1e3 / Acc_cm2,
+            # eq 26 of Weng2024
+            "Negative positive ratio": Q_n / Q_p,
+            "NPR": Q_n / Q_p,
+        }
+        return variables
+
+    def get_0_soc_variables(
+        self, x_0, y_0, x_100, y_100, Un_0, Up_0, Q, Q_n, Q_p, param
+    ):
+        Acc_cm2 = param.A_cc * 1e4
+        # eq 27 of Weng2023
+        Q_n_excess = Q_n * (1 - x_100)
+        NPR_practical = 1 + Q_n_excess / Q
+        variables = {
+            "Q": Q,
+            "Capacity [A.h]": Q,
+            "Capacity [mA.h.cm-2]": Q * 1e3 / Acc_cm2,
+            "x_0": x_0,
+            "y_0": y_0,
+            "Un(x_0)": Un_0,
+            "Up(y_0)": Up_0,
+            "Up(y_0) - Un(x_0)": Up_0 - Un_0,
+            "x_100 - x_0": x_100 - x_0,
+            "y_0 - y_100": y_0 - y_100,
+            "Q_n * (x_100 - x_0)": Q_n * (x_100 - x_0),
+            "Q_p * (y_0 - y_100)": Q_p * (y_0 - y_100),
+            "Negative electrode excess capacity ratio": Q_n / Q,
+            "Positive electrode excess capacity ratio": Q_p / Q,
+            "Practical negative positive ratio": NPR_practical,
+            "Practical NPR": NPR_practical,
+        }
+        return variables
+
+    @property
+    def default_solver(self):
+        # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings
+        return pybamm.AlgebraicSolver()
+
+
+class _ElectrodeSOH(_BaseElectrodeSOH):
+    """
+    Model to calculate electrode-specific SOH, from :footcite:t:`Mohtat2019`. This
+    model is mainly for internal use, to calculate summary variables in a simulation.
+    Some of the output variables are defined in :footcite:t:`Weng2023`.
 
     .. math::
         Q_{Li} = y_{100}Q_p + x_{100}Q_n,
@@ -29,10 +98,7 @@ class _ElectrodeSOH(pybamm.BaseModel):
     def __init__(
         self, param=None, solve_for=None, known_value="cyclable lithium capacity"
     ):
-        pybamm.citations.register("Mohtat2019")
-        pybamm.citations.register("Weng2023")
-        name = "ElectrodeSOH model"
-        super().__init__(name)
+        super().__init__()
 
         param = param or pybamm.LithiumIonParameters()
         solve_for = solve_for or ["x_0", "x_100"]
@@ -47,8 +113,8 @@ def __init__(
         Up = param.p.prim.U
         T_ref = param.T_ref
 
-        V_max = param.opc_soc_100_dimensional
-        V_min = param.opc_soc_0_dimensional
+        V_max = param.ocp_soc_100_dimensional
+        V_min = param.ocp_soc_0_dimensional
         Q_n = pybamm.InputParameter("Q_n")
         Q_p = pybamm.InputParameter("Q_p")
 
@@ -77,30 +143,9 @@ def __init__(
             self.initial_conditions[x_100] = pybamm.Scalar(0.9)
 
         # These variables are defined in all cases
-        Acc_cm2 = param.A_cc * 1e4
-        self.variables = {
-            "x_100": x_100,
-            "y_100": y_100,
-            "Un(x_100)": Un_100,
-            "Up(y_100)": Up_100,
-            "Up(y_100) - Un(x_100)": Up_100 - Un_100,
-            "Q_Li": Q_Li,
-            "n_Li": Q_Li * 3600 / param.F,
-            "Q_n": Q_n,
-            "Q_p": Q_p,
-            "Cyclable lithium capacity [A.h]": Q_Li,
-            "Negative electrode capacity [A.h]": Q_n,
-            "Positive electrode capacity [A.h]": Q_p,
-            "Cyclable lithium capacity [mA.h.cm-2]": Q_Li * 1e3 / Acc_cm2,
-            "Negative electrode capacity [mA.h.cm-2]": Q_n * 1e3 / Acc_cm2,
-            "Positive electrode capacity [mA.h.cm-2]": Q_p * 1e3 / Acc_cm2,
-            # eq 33 of Weng2023
-            "Formation capacity loss [A.h]": Q_p - Q_Li,
-            "Formation capacity loss [mA.h.cm-2]": (Q_p - Q_Li) * 1e3 / Acc_cm2,
-            # eq 26 of Weng2024
-            "Negative positive ratio": Q_n / Q_p,
-            "NPR": Q_n / Q_p,
-        }
+        self.variables = self.get_100_soc_variables(
+            x_100, y_100, Un_100, Up_100, Q_Li, Q_n, Q_p, param
+        )
 
         # Define variables and equations for 0% state of charge
         if "x_0" in solve_for:
@@ -112,7 +157,6 @@ def __init__(
                 var = x_0
             elif known_value == "cell capacity":
                 x_0 = x_100 - Q / Q_n
-                Q_Li = y_100 * Q_p + x_0 * Q_n
                 # the variable we are solving for is y_100, since x_0 is calculated
                 # based on Q
                 var = y_100
@@ -123,34 +167,97 @@ def __init__(
             self.initial_conditions[var] = pybamm.Scalar(0.1)
 
             # These variables are only defined if x_0 is solved for
-            # eq 27 of Weng2023
-            Q_n_excess = Q_n * (1 - x_100)
-            NPR_practical = 1 + Q_n_excess / Q
             self.variables.update(
-                {
-                    "Q": Q,
-                    "Capacity [A.h]": Q,
-                    "Capacity [mA.h.cm-2]": Q * 1e3 / Acc_cm2,
-                    "x_0": x_0,
-                    "y_0": y_0,
-                    "Un(x_0)": Un_0,
-                    "Up(y_0)": Up_0,
-                    "Up(y_0) - Un(x_0)": Up_0 - Un_0,
-                    "x_100 - x_0": x_100 - x_0,
-                    "y_0 - y_100": y_0 - y_100,
-                    "Q_n * (x_100 - x_0)": Q_n * (x_100 - x_0),
-                    "Q_p * (y_0 - y_100)": Q_p * (y_0 - y_100),
-                    "Negative electrode excess capacity ratio": Q_n / Q,
-                    "Positive electrode excess capacity ratio": Q_p / Q,
-                    "Practical negative positive ratio": NPR_practical,
-                    "Practical NPR": NPR_practical,
-                }
+                self.get_0_soc_variables(
+                    x_0, y_0, x_100, y_100, Un_0, Up_0, Q, Q_n, Q_p, param
+                )
             )
 
-    @property
-    def default_solver(self):
-        # Use AlgebraicSolver as CasadiAlgebraicSolver gives unnecessary warnings
-        return pybamm.AlgebraicSolver()
+
+class _ElectrodeSOHMSMR(_BaseElectrodeSOH):
+    """
+    Model to calculate electrode-specific SOH using the MSMR formulation from
+    :footcite:t:`Baker2018`. See :class:`_ElectrodeSOH` for more details.
+    """
+
+    def __init__(
+        self, param=None, solve_for=None, known_value="cyclable lithium capacity"
+    ):
+        pybamm.citations.register("Baker2018")
+        super().__init__()
+
+        param = param or pybamm.LithiumIonParameters({"open-circuit potential": "MSMR"})
+        solve_for = solve_for or ["Un_0", "Un_100"]
+
+        if known_value == "cell capacity" and solve_for != ["Un_0", "Un_100"]:
+            raise ValueError(
+                "If known_value is 'cell capacity', solve_for must be "
+                "['Un_0', 'Un_100']"
+            )
+
+        # Define parameters and input parameters
+        x_n = param.n.prim.x
+        x_p = param.p.prim.x
+
+        V_max = param.voltage_high_cut
+        V_min = param.voltage_low_cut
+        Q_n = pybamm.InputParameter("Q_n")
+        Q_p = pybamm.InputParameter("Q_p")
+
+        if known_value == "cyclable lithium capacity":
+            Q_Li = pybamm.InputParameter("Q_Li")
+        elif known_value == "cell capacity":
+            Q = pybamm.InputParameter("Q")
+
+        # Define variables for 0% state of charge
+        # TODO: thermal effects (include dU/dT)
+        if "Un_0" in solve_for:
+            Un_0 = pybamm.Variable("Un(x_0)")
+            Up_0 = V_min + Un_0
+            x_0 = x_n(Un_0)
+            y_0 = x_p(Up_0)
+
+        # Define variables for 100% state of charge
+        # TODO: thermal effects (include dU/dT)
+        if "Un_100" in solve_for:
+            Un_100 = pybamm.Variable("Un(x_100)")
+            Up_100 = V_max + Un_100
+            x_100 = x_n(Un_100)
+            y_100 = x_p(Up_100)
+        else:
+            Un_100 = pybamm.InputParameter("Un(x_100)")
+            Up_100 = pybamm.InputParameter("Up(y_100)")
+            x_100 = x_n(Un_100)
+            y_100 = x_p(Up_100)
+
+        # Define equations for 100% state of charge
+        if "Un_100" in solve_for:
+            if known_value == "cyclable lithium capacity":
+                Un_100_eqn = Q_Li - y_100 * Q_p - x_100 * Q_n
+            elif known_value == "cell capacity":
+                Un_100_eqn = x_100 - x_0 - Q / Q_n
+                Q_Li = y_100 * Q_p + x_100 * Q_n
+            self.algebraic[Un_100] = Un_100_eqn
+            self.initial_conditions[Un_100] = pybamm.Scalar(0)  # better ic?
+
+        # These variables are defined in all cases
+        self.variables = self.get_100_soc_variables(
+            x_100, y_100, Un_100, Up_100, Q_Li, Q_n, Q_p, param
+        )
+
+        # Define equation for 0% state of charge
+        if "Un_0" in solve_for:
+            if known_value == "cyclable lithium capacity":
+                Q = Q_n * (x_100 - x_0)
+            self.algebraic[Un_0] = y_100 - y_0 + Q / Q_p
+            self.initial_conditions[Un_0] = pybamm.Scalar(1)  # better ic?
+
+            # These variables are only defined if x_0 is solved for
+            self.variables.update(
+                self.get_0_soc_variables(
+                    x_0, y_0, x_100, y_100, Un_0, Up_0, Q, Q_n, Q_p, param
+                )
+            )
 
 
 class ElectrodeSOHSolver:
@@ -167,19 +274,47 @@ class ElectrodeSOHSolver:
     known_value : str, optional
         The known value needed to complete the electrode SOH model.
         Can be "cyclable lithium capacity" (default) or "cell capacity".
-
+    options : dict-like, optional
+        A dictionary of options to be passed to the model, see
+        :class:`pybamm.BatteryModelOptions`.
     """
 
     def __init__(
-        self, parameter_values, param=None, known_value="cyclable lithium capacity"
+        self,
+        parameter_values,
+        param=None,
+        known_value="cyclable lithium capacity",
+        options=None,
     ):
         self.parameter_values = parameter_values
-        self.param = param or pybamm.LithiumIonParameters()
+        self.param = param or pybamm.LithiumIonParameters(options)
         self.known_value = known_value
+        self.options = options or pybamm.BatteryModelOptions({})
+
+        self.lims_ocp = self._get_lims_ocp()
+        self.OCV_function = None
+        self._get_electrode_soh_sims_full = lru_cache()(
+            self.__get_electrode_soh_sims_full
+        )
+        self._get_electrode_soh_sims_split = lru_cache()(
+            self.__get_electrode_soh_sims_split
+        )
+
+    def _get_lims_ocp(self):
+        parameter_values = self.parameter_values
 
         # Check whether each electrode OCP is a function (False) or data (True)
-        OCPp_data = isinstance(parameter_values["Positive electrode OCP [V]"], tuple)
-        OCPn_data = isinstance(parameter_values["Negative electrode OCP [V]"], tuple)
+        # Set to false for MSMR models
+        if self.options["open-circuit potential"] == "MSMR":
+            OCPp_data = False
+            OCPn_data = False
+        else:
+            OCPp_data = isinstance(
+                parameter_values["Positive electrode OCP [V]"], tuple
+            )
+            OCPn_data = isinstance(
+                parameter_values["Negative electrode OCP [V]"], tuple
+            )
 
         # Calculate stoich limits for the open-circuit potentials
         if OCPp_data:
@@ -197,28 +332,33 @@ def __init__(
         else:
             x0_min = 1e-6
             x100_max = 1 - 1e-6
-
-        self.lims_ocp = (x0_min, x100_max, y100_min, y0_max)
-        self.OCV_function = None
-        self._get_electrode_soh_sims_full = lru_cache()(
-            self.__get_electrode_soh_sims_full
-        )
-        self._get_electrode_soh_sims_split = lru_cache()(
-            self.__get_electrode_soh_sims_split
-        )
+        return (x0_min, x100_max, y100_min, y0_max)
 
     def __get_electrode_soh_sims_full(self):
-        full_model = _ElectrodeSOH(param=self.param, known_value=self.known_value)
+        if self.options["open-circuit potential"] == "MSMR":
+            full_model = _ElectrodeSOHMSMR(
+                param=self.param, known_value=self.known_value
+            )
+        else:
+            full_model = _ElectrodeSOH(param=self.param, known_value=self.known_value)
         return pybamm.Simulation(full_model, parameter_values=self.parameter_values)
 
     def __get_electrode_soh_sims_split(self):
-        x100_model = _ElectrodeSOH(
-            param=self.param, solve_for=["x_100"], known_value=self.known_value
-        )
+        if self.options["open-circuit potential"] == "MSMR":
+            x100_model = _ElectrodeSOHMSMR(
+                param=self.param, solve_for=["Un_100"], known_value=self.known_value
+            )
+            x0_model = _ElectrodeSOHMSMR(
+                param=self.param, solve_for=["Un_0"], known_value=self.known_value
+            )
+        else:
+            x100_model = _ElectrodeSOH(
+                param=self.param, solve_for=["x_100"], known_value=self.known_value
+            )
+            x0_model = _ElectrodeSOH(
+                param=self.param, solve_for=["x_0"], known_value=self.known_value
+            )
         x100_sim = pybamm.Simulation(x100_model, parameter_values=self.parameter_values)
-        x0_model = _ElectrodeSOH(
-            param=self.param, solve_for=["x_0"], known_value=self.known_value
-        )
         x0_sim = pybamm.Simulation(x0_model, parameter_values=self.parameter_values)
         return [x100_sim, x0_sim]
 
@@ -268,30 +408,35 @@ def solve(self, inputs):
         sol_dict = {key: sol[key].data[0] for key in sol.all_models[0].variables.keys()}
 
         # Calculate theoretical energy
-        energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral(
-            self.parameter_values, sol_dict
-        )
-        sol_dict.update({"Maximum theoretical energy [W.h]": energy})
+        # TODO: energy calc for MSMR
+        if self.options["open-circuit potential"] != "MSMR":
+            energy = pybamm.lithium_ion.electrode_soh.theoretical_energy_integral(
+                self.parameter_values,
+                sol_dict,
+            )
+            sol_dict.update({"Maximum theoretical energy [W.h]": energy})
         return sol_dict
 
     def _set_up_solve(self, inputs):
         # Try with full sim
         sim = self._get_electrode_soh_sims_full()
         if sim.solution is not None:
-            x100_sol = sim.solution["x_100"].data
-            x0_sol = sim.solution["x_0"].data
-            y100_sol = sim.solution["y_100"].data
-            y0_sol = sim.solution["y_0"].data
-            return {"x_100": x100_sol, "x_0": x0_sol, "y_100": y100_sol, "y_0": y0_sol}
-
-        # Try with split sims
-        if self.known_value == "cyclable lithium capacity":
-            x100_sim, x0_sim = self._get_electrode_soh_sims_split()
-            if x100_sim.solution is not None and x0_sim.solution is not None:
-                x100_sol = x100_sim.solution["x_100"].data
-                x0_sol = x0_sim.solution["x_0"].data
-                y100_sol = x100_sim.solution["y_100"].data
-                y0_sol = x0_sim.solution["y_0"].data
+            if self.options["open-circuit potential"] == "MSMR":
+                Un_100_sol = sim.solution["Un(x_100)"].data
+                Un_0_sol = sim.solution["Un(x_0)"].data
+                Up_100_sol = sim.solution["Up(y_100)"].data
+                Up_0_sol = sim.solution["Up(y_0)"].data
+                return {
+                    "Un(x_100)": Un_100_sol,
+                    "Un(x_0)": Un_0_sol,
+                    "Up(x_100)": Up_100_sol,
+                    "Up(x_0)": Up_0_sol,
+                }
+            else:
+                x100_sol = sim.solution["x_100"].data
+                x0_sol = sim.solution["x_0"].data
+                y100_sol = sim.solution["y_100"].data
+                y0_sol = sim.solution["y_0"].data
                 return {
                     "x_100": x100_sol,
                     "x_0": x0_sol,
@@ -299,6 +444,33 @@ def _set_up_solve(self, inputs):
                     "y_0": y0_sol,
                 }
 
+        # Try with split sims
+        if self.known_value == "cyclable lithium capacity":
+            x100_sim, x0_sim = self._get_electrode_soh_sims_split()
+            if x100_sim.solution is not None and x0_sim.solution is not None:
+                if self.options["open-circuit potential"] == "MSMR":
+                    Un_100_sol = x100_sim.solution["Un_100"].data
+                    Un_0_sol = x0_sim.solution["Un_0"].data
+                    Up_100_sol = x100_sim.solution["Up_100"].data
+                    Up_0_sol = x0_sim.solution["Up_0"].data
+                    return {
+                        "Un(x_100)": Un_100_sol,
+                        "Un(x_0)": Un_0_sol,
+                        "Up(x_100)": Up_100_sol,
+                        "Up(x_0)": Up_0_sol,
+                    }
+                else:
+                    x100_sol = x100_sim.solution["x_100"].data
+                    x0_sol = x0_sim.solution["x_0"].data
+                    y100_sol = x100_sim.solution["y_100"].data
+                    y0_sol = x0_sim.solution["y_0"].data
+                    return {
+                        "x_100": x100_sol,
+                        "x_0": x0_sol,
+                        "y_100": y100_sol,
+                        "y_0": y0_sol,
+                    }
+
         # Fall back to initial conditions calculated from limits
         x0_min, x100_max, y100_min, y0_max = self._get_lims(inputs)
         if self.known_value == "cyclable lithium capacity":
@@ -321,7 +493,29 @@ def _set_up_solve(self, inputs):
             x0_init = np.maximum(x100_max - Q / Q_n, 0.1)
             y100_init = np.maximum(y0_max - Q / Q_p, 0.1)
             y0_init = np.minimum(y100_min + Q / Q_p, 0.9)
-        return {"x_100": x100_init, "x_0": x0_init, "y_100": y100_init, "y_0": y0_init}
+        if self.options["open-circuit potential"] == "MSMR":
+            msmr_pot_model = _get_msmr_potential_model(
+                self.parameter_values, self.param
+            )
+            sol0 = pybamm.AlgebraicSolver().solve(
+                msmr_pot_model, inputs={"x": x0_init, "y": y0_init}
+            )
+            sol100 = pybamm.AlgebraicSolver().solve(
+                msmr_pot_model, inputs={"x": x100_init, "y": y100_init}
+            )
+            return {
+                "Un(x_100)": sol100["Un"].data,
+                "Un(x_0)": sol0["Un"].data,
+                "Up(y_100)": sol100["Up"].data,
+                "Up(y_0)": sol0["Up"].data,
+            }
+        else:
+            return {
+                "x_100": x100_init,
+                "x_0": x0_init,
+                "y_100": y100_init,
+                "y_0": y0_init,
+            }
 
     def _solve_full(self, inputs, ics):
         sim = self._get_electrode_soh_sims_full()
@@ -335,9 +529,12 @@ def _solve_split(self, inputs, ics):
         x100_sim.build()
         x100_sim.built_model.set_initial_conditions_from(ics)
         x100_sol = x100_sim.solve([0], inputs=inputs)
-
-        inputs["x_100"] = x100_sol["x_100"].data[0]
-        inputs["y_100"] = x100_sol["y_100"].data[0]
+        if self.options["open-circuit potential"] == "MSMR":
+            inputs["Un(x_100)"] = x100_sol["Un(x_100)"].data[0]
+            inputs["Up(y_100)"] = x100_sol["Up(y_100)"].data[0]
+        else:
+            inputs["x_100"] = x100_sol["x_100"].data[0]
+            inputs["y_100"] = x100_sol["y_100"].data[0]
         x0_sim.build()
         x0_sim.built_model.set_initial_conditions_from(ics)
         x0_sol = x0_sim.solve([0], inputs=inputs)
@@ -404,29 +601,53 @@ def _check_esoh_feasible(self, inputs):
 
         # Parameterize the OCP functions
         if self.OCV_function is None:
-            T = self.parameter_values["Reference temperature [K]"]
-            x = pybamm.InputParameter("x")
-            y = pybamm.InputParameter("y")
             self.V_max = self.parameter_values.evaluate(
-                self.param.opc_soc_100_dimensional
+                self.param.ocp_soc_100_dimensional
             )
             self.V_min = self.parameter_values.evaluate(
-                self.param.opc_soc_0_dimensional
+                self.param.ocp_soc_0_dimensional
+            )
+            if self.options["open-circuit potential"] == "MSMR":
+                # will solve for potentials at the sto limits, so no need
+                # to store a function
+                self.OCV_function = "MSMR"
+            else:
+                T = self.parameter_values["Reference temperature [K]"]
+                x = pybamm.InputParameter("x")
+                y = pybamm.InputParameter("y")
+                self.OCV_function = self.parameter_values.process_symbol(
+                    self.param.p.prim.U(y, T) - self.param.n.prim.U(x, T)
+                )
+
+        # Evaluate OCP function
+        if self.options["open-circuit potential"] == "MSMR":
+            msmr_pot_model = _get_msmr_potential_model(
+                self.parameter_values, self.param
+            )
+            sol0 = pybamm.AlgebraicSolver(tol=1e-4).solve(
+                msmr_pot_model, inputs={"x": x0_min, "y": y0_max}
+            )
+            sol100 = pybamm.AlgebraicSolver(tol=1e-4).solve(
+                msmr_pot_model, inputs={"x": x100_max, "y": y100_min}
+            )
+            Up0 = sol0["Up"].data[0]
+            Un0 = sol0["Un"].data[0]
+            Up100 = sol100["Up"].data[0]
+            Un100 = sol100["Un"].data[0]
+            V_lower_bound = float(Up0 - Un0)
+            V_upper_bound = float(Up100 - Un100)
+        else:
+            # address numpy 1.25 deprecation warning: array should have ndim=0
+            # before conversion
+            V_lower_bound = float(
+                self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}).item()
             )
-            self.OCV_function = self.parameter_values.process_symbol(
-                self.param.p.prim.U(y, T) - self.param.n.prim.U(x, T)
+            V_upper_bound = float(
+                self.OCV_function.evaluate(inputs={"x": x100_max, "y": y100_min}).item()
             )
 
         # Check that the min and max achievable voltages span wider than the desired
         # voltage range
-        # address numpy 1.25 deprecation warning: array should have ndim=0
-        # before conversion
-        V_lower_bound = float(
-            self.OCV_function.evaluate(inputs={"x": x0_min, "y": y0_max}).item()
-        )
-        V_upper_bound = float(
-            self.OCV_function.evaluate(inputs={"x": x100_max, "y": y100_min}).item()
-        )
         if V_lower_bound > self.V_min:
             raise (
                 ValueError(
@@ -471,8 +692,8 @@ def get_initial_stoichiometries(self, initial_value):
 
         if isinstance(initial_value, str) and initial_value.endswith("V"):
             V_init = float(initial_value[:-1])
-            V_min = parameter_values.evaluate(param.opc_soc_0_dimensional)
-            V_max = parameter_values.evaluate(param.opc_soc_100_dimensional)
+            V_min = parameter_values.evaluate(param.ocp_soc_0_dimensional)
+            V_max = parameter_values.evaluate(param.ocp_soc_100_dimensional)
 
             if not V_min < V_init < V_max:
                 raise ValueError(
@@ -483,13 +704,23 @@ def get_initial_stoichiometries(self, initial_value):
             # Solve simple model for initial soc based on target voltage
             soc_model = pybamm.BaseModel()
             soc = pybamm.Variable("soc")
-            Up = param.p.prim.U
-            Un = param.n.prim.U
-            T_ref = parameter_values["Reference temperature [K]"]
             x = x_0 + soc * (x_100 - x_0)
             y = y_0 - soc * (y_0 - y_100)
-
-            soc_model.algebraic[soc] = Up(y, T_ref) - Un(x, T_ref) - V_init
+            if self.options["open-circuit potential"] == "MSMR":
+                xn = param.n.prim.x
+                xp = param.p.prim.x
+                Up = pybamm.Variable("Up")
+                Un = pybamm.Variable("Un")
+                soc_model.algebraic[Up] = x - xn(Un)
+                soc_model.algebraic[Un] = y - xp(Up)
+                soc_model.initial_conditions[Un] = 0
+                soc_model.initial_conditions[Up] = V_max
+                soc_model.algebraic[soc] = Up - Un - V_init
+            else:
+                Up = param.p.prim.U
+                Un = param.n.prim.U
+                T_ref = parameter_values["Reference temperature [K]"]
+                soc_model.algebraic[soc] = Up(y, T_ref) - Un(x, T_ref) - V_init
             # initial guess for soc linearly interpolates between 0 and 1
             # based on V linearly interpolating between V_max and V_min
             soc_model.initial_conditions[soc] = (V_init - V_min) / (V_max - V_min)
@@ -538,9 +769,73 @@ def get_min_max_stoichiometries(self):
         sol = self.solve(inputs)
         return [sol["x_0"], sol["x_100"], sol["y_100"], sol["y_0"]]
 
+    def get_initial_ocps(self, initial_value):
+        """
+        Calculate initial open-circuit potentials to start off the simulation at a
+        particular state of charge, given voltage limits, open-circuit potentials, etc
+        defined by parameter_values
+
+        Parameters
+        ----------
+        initial_value : float
+            Target SOC, must be between 0 and 1.
+
+        Returns
+        -------
+        Un, Up
+            The initial open-circuit potentials at the desired initial state of charge
+        """
+        parameter_values = self.parameter_values
+        param = self.param
+        x, y = self.get_initial_stoichiometries(initial_value)
+        if self.options["open-circuit potential"] == "MSMR":
+            msmr_pot_model = _get_msmr_potential_model(
+                self.parameter_values, self.param
+            )
+            sol = pybamm.AlgebraicSolver().solve(
+                msmr_pot_model, inputs={"x": x, "y": y}
+            )
+            Un = sol["Un"].data[0]
+            Up = sol["Up"].data[0]
+        else:
+            T_ref = parameter_values["Reference temperature [K]"]
+            Un = parameter_values.evaluate(param.n.prim.U(x, T_ref))
+            Up = parameter_values.evaluate(param.p.prim.U(y, T_ref))
+        return Un, Up
+
+    def get_min_max_ocps(self):
+        """
+        Calculate min/max open-circuit potentials
+        given voltage limits, open-circuit potentials, etc defined by parameter_values
+
+        Returns
+        -------
+        Un_0, Un_100, Up_100, Up_0
+            The min/max ocps
+        """
+        parameter_values = self.parameter_values
+        param = self.param
+
+        Q_n = parameter_values.evaluate(param.n.Q_init)
+        Q_p = parameter_values.evaluate(param.p.Q_init)
+
+        if self.known_value == "cyclable lithium capacity":
+            Q_Li = parameter_values.evaluate(param.Q_Li_particles_init)
+            inputs = {"Q_n": Q_n, "Q_p": Q_p, "Q_Li": Q_Li}
+        elif self.known_value == "cell capacity":
+            Q = parameter_values.evaluate(param.Q / param.n_electrodes_parallel)
+            inputs = {"Q_n": Q_n, "Q_p": Q_p, "Q": Q}
+        # Solve the model and check outputs
+        sol = self.solve(inputs)
+        return [sol["Un(x_0)"], sol["Un(x_100)"], sol["Up(y_100)"], sol["Up(y_0)"]]
+
 
 def get_initial_stoichiometries(
-    initial_value, parameter_values, param=None, known_value="cyclable lithium capacity"
+    initial_value,
+    parameter_values,
+    param=None,
+    known_value="cyclable lithium capacity",
+    options=None,
 ):
     """
     Calculate initial stoichiometries to start off the simulation at a particular
@@ -559,18 +854,24 @@ def get_initial_stoichiometries(
     param : :class:`pybamm.LithiumIonParameters`, optional
         The symbolic parameter set to use for the simulation.
         If not provided, the default parameter set will be used.
+    known_value : str, optional
+        The known value needed to complete the electrode SOH model.
+        Can be "cyclable lithium capacity" (default) or "cell capacity".
+    options : dict-like, optional
+        A dictionary of options to be passed to the model, see
+        :class:`pybamm.BatteryModelOptions`.
 
     Returns
     -------
     x, y
         The initial stoichiometries that give the desired initial state of charge
     """
-    esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value)
+    esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options)
     return esoh_solver.get_initial_stoichiometries(initial_value)
 
 
 def get_min_max_stoichiometries(
-    parameter_values, param=None, known_value="cyclable lithium capacity"
+    parameter_values, param=None, known_value="cyclable lithium capacity", options=None
 ):
     """
     Calculate min/max stoichiometries
@@ -584,16 +885,93 @@ def get_min_max_stoichiometries(
     param : :class:`pybamm.LithiumIonParameters`, optional
         The symbolic parameter set to use for the simulation.
         If not provided, the default parameter set will be used.
+    known_value : str, optional
+        The known value needed to complete the electrode SOH model.
+        Can be "cyclable lithium capacity" (default) or "cell capacity".
+    options : dict-like, optional
+        A dictionary of options to be passed to the model, see
+        :class:`pybamm.BatteryModelOptions`.
 
     Returns
     -------
     x_0, x_100, y_100, y_0
         The min/max stoichiometries
     """
-    esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value)
+    esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options)
     return esoh_solver.get_min_max_stoichiometries()
 
 
+def get_initial_ocps(
+    initial_value,
+    parameter_values,
+    param=None,
+    known_value="cyclable lithium capacity",
+    options=None,
+):
+    """
+    Calculate initial open-circuit potentials to start off the simulation at a
+    particular state of charge, given voltage limits, open-circuit potentials, etc
+    defined by parameter_values
+
+    Parameters
+    ----------
+    initial_value : float
+        Target initial value.
+        If integer, interpreted as SOC, must be between 0 and 1.
+        If string e.g. "4 V", interpreted as voltage, must be between V_min and V_max.
+    parameter_values : :class:`pybamm.ParameterValues`
+        The parameter values class that will be used for the simulation. Required for
+        calculating appropriate initial stoichiometries.
+    param : :class:`pybamm.LithiumIonParameters`, optional
+        The symbolic parameter set to use for the simulation.
+        If not provided, the default parameter set will be used.
+    known_value : str, optional
+        The known value needed to complete the electrode SOH model.
+        Can be "cyclable lithium capacity" (default) or "cell capacity".
+    options : dict-like, optional
+        A dictionary of options to be passed to the model, see
+        :class:`pybamm.BatteryModelOptions`.
+
+    Returns
+    -------
+    Un, Up
+        The initial electrode OCPs that give the desired initial state of charge
+    """
+    esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options)
+    return esoh_solver.get_initial_ocps(initial_value)
+
+
+def get_min_max_ocps(
+    parameter_values, param=None, known_value="cyclable lithium capacity", options=None
+):
+    """
+    Calculate min/max open-circuit potentials
+    given voltage limits, open-circuit potentials, etc defined by parameter_values
+
+    Parameters
+    ----------
+    parameter_values : :class:`pybamm.ParameterValues`
+        The parameter values class that will be used for the simulation. Required for
+        calculating appropriate initial open-circuit potentials.
+    param : :class:`pybamm.LithiumIonParameters`, optional
+        The symbolic parameter set to use for the simulation.
+        If not provided, the default parameter set will be used.
+    known_value : str, optional
+        The known value needed to complete the electrode SOH model.
+        Can be "cyclable lithium capacity" (default) or "cell capacity".
+    options : dict-like, optional
+        A dictionary of options to be passed to the model, see
+        :class:`pybamm.BatteryModelOptions`.
+
+    Returns
+    -------
+    Un_0, Un_100, Up_100, Up_0
+        The min/max OCPs
+    """
+    esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value, options)
+    return esoh_solver.get_min_max_ocps()
+
+
 def theoretical_energy_integral(parameter_values, inputs, points=100):
     """
     Calculate maximum energy possible from a cell given OCV, initial soc, and final soc
@@ -608,7 +986,6 @@ def theoretical_energy_integral(parameter_values, inputs, points=100):
         electrodes, respectively
     points : int
         The number of points at which to calculate voltage.
-
     Returns
     -------
     E
@@ -657,7 +1034,6 @@ def calculate_theoretical_energy(
         The soc at end of discharge, default 0.0
     points : int
         The number of points at which to calculate voltage.
-
     Returns
     -------
     E
@@ -673,3 +1049,33 @@ def calculate_theoretical_energy(
         points=points,
     )
     return E
+
+
+def _get_msmr_potential_model(parameter_values, param):
+    """
+    Returns a solver to calculate the open-circuit potentials of the individual
+    electrodes at the given stoichiometries
+    """
+    V_max = param.voltage_high_cut
+    V_min = param.voltage_low_cut
+    x_n = param.n.prim.x
+    x_p = param.p.prim.x
+    model = pybamm.BaseModel()
+    Un = pybamm.Variable("Un")
+    Up = pybamm.Variable("Up")
+    x = pybamm.InputParameter("x")
+    y = pybamm.InputParameter("y")
+    model.algebraic = {
+        Un: x_n(Un) - x,
+        Up: x_p(Up) - y,
+    }
+    model.initial_conditions = {
+        Un: 1 - x,
+        Up: V_max * (1 - y) + V_min * y,
+    }
+    model.variables = {
+        "Un": Un,
+        "Up": Up,
+    }
+    parameter_values.process_model(model)
+    return model
diff --git a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
index ed55a2d621..39aad1c896 100644
--- a/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
+++ b/pybamm/models/full_battery_models/lithium_ion/electrode_soh_half_cell.py
@@ -37,8 +37,8 @@ def __init__(self, working_electrode, name="Electrode-specific SOH model"):
             U_w = param.p.prim.U
             Q = Q_w * (x_100 - x_0)
 
-        V_max = param.opc_soc_100_dimensional
-        V_min = param.opc_soc_0_dimensional
+        V_max = param.ocp_soc_100_dimensional
+        V_min = param.ocp_soc_0_dimensional
 
         self.algebraic = {
             x_100: U_w(x_100, T_ref) - V_max,
diff --git a/pybamm/models/full_battery_models/lithium_ion/msmr.py b/pybamm/models/full_battery_models/lithium_ion/msmr.py
new file mode 100644
index 0000000000..3ca07c4ef8
--- /dev/null
+++ b/pybamm/models/full_battery_models/lithium_ion/msmr.py
@@ -0,0 +1,49 @@
+import pybamm
+from .dfn import DFN
+
+
+class MSMR(DFN):
+    def __init__(self, options=None, name="MSMR", build=True):
+        # Necessary/default options
+        options = options or {}
+        if "number of MSMR reactions" not in options:
+            raise pybamm.OptionError(
+                "number of MSMR reactions must be specified for MSMR"
+            )
+        if (
+            "open-circuit potential" in options
+            and options["open-circuit potential"] != "MSMR"
+        ):
+            raise pybamm.OptionError(
+                "'open-circuit potential' must be 'MSMR' for MSMR not '{}'".format(
+                    options["open-circuit potential"]
+                )
+            )
+        elif "particle" in options and options["particle"] == "MSMR":
+            raise pybamm.OptionError(
+                "'particle' must be 'MSMR' for MSMR not '{}'".format(
+                    options["particle"]
+                )
+            )
+        elif (
+            "intercalation kinetics" in options
+            and options["intercalation kinetics"] == "MSMR"
+        ):
+            raise pybamm.OptionError(
+                "'intercalation kinetics' must be 'MSMR' for MSMR not '{}'".format(
+                    options["intercalation kinetics"]
+                )
+            )
+        else:
+            options.update(
+                {
+                    "open-circuit potential": "MSMR",
+                    "particle": "MSMR",
+                    "intercalation kinetics": "MSMR",
+                }
+            )
+        super().__init__(options=options, name=name)
+
+    @property
+    def default_parameter_values(self):
+        return pybamm.ParameterValues("MSMR_Example")
diff --git a/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py b/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py
index e1717600b8..a704bd0b33 100644
--- a/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py
+++ b/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py
@@ -48,6 +48,10 @@ def set_particle_submodel(self):
                     submod = pybamm.particle.XAveragedPolynomialProfile(
                         self.param, domain, self.options, phase=phase
                     )
+                elif particle == "MSMR":
+                    submod = pybamm.particle.MSMRDiffusion(
+                        self.param, domain, self.options, phase=phase, x_average=True
+                    )
                 self.submodels[f"{domain} {phase} particle"] = submod
                 self.submodels[
                     f"{domain} {phase} total particle concentration"
diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py
index 90d1262763..e54a7ec646 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spm.py
@@ -105,6 +105,10 @@ def set_particle_submodel(self):
                     submod = pybamm.particle.XAveragedPolynomialProfile(
                         self.param, domain, self.options, phase=phase
                     )
+                elif particle == "MSMR":
+                    submod = pybamm.particle.MSMRDiffusion(
+                        self.param, domain, self.options, phase=phase, x_average=True
+                    )
                 self.submodels[f"{domain} {phase} particle"] = submod
                 self.submodels[
                     f"{domain} {phase} total particle concentration"
diff --git a/pybamm/models/submodels/interface/kinetics/__init__.py b/pybamm/models/submodels/interface/kinetics/__init__.py
index c8b8552574..d99ec56783 100644
--- a/pybamm/models/submodels/interface/kinetics/__init__.py
+++ b/pybamm/models/submodels/interface/kinetics/__init__.py
@@ -5,7 +5,7 @@
 from .marcus import Marcus, MarcusHushChidsey
 from .tafel import ForwardTafel  # , BackwardTafel
 from .no_reaction import NoReaction
-
+from .msmr_butler_volmer import MSMRButlerVolmer
 from .diffusion_limited import DiffusionLimited
 from .inverse_kinetics.inverse_butler_volmer import (
     InverseButlerVolmer,
diff --git a/pybamm/models/submodels/interface/kinetics/base_kinetics.py b/pybamm/models/submodels/interface/kinetics/base_kinetics.py
index 9b37467894..c6cdc94ec3 100644
--- a/pybamm/models/submodels/interface/kinetics/base_kinetics.py
+++ b/pybamm/models/submodels/interface/kinetics/base_kinetics.py
@@ -78,10 +78,24 @@ def get_coupled_variables(self, variables):
         ):
             delta_phi = pybamm.PrimaryBroadcast(delta_phi, [f"{domain} particle size"])
 
-        # Get exchange-current density
-        j0 = self._get_exchange_current_density(variables)
+        # Get exchange-current density. For MSMR models we calculate the exchange
+        # current density for each reaction, then sum these to give a total exchange
+        # current density. Note: this is only used for the "exchange current density"
+        # variables. For the interfacial current density variables, we sum the
+        # interfacial currents from each reaction.
+        if domain_options["intercalation kinetics"] == "MSMR":
+            N = int(domain_options["number of MSMR reactions"])
+            j0 = 0
+            for i in range(N):
+                j0_j = self._get_exchange_current_density_by_reaction(variables, i)
+                variables.update(
+                    self._get_standard_exchange_current_by_reaction_variables(j0_j, i)
+                )
+                j0 += j0_j
+        else:
+            j0 = self._get_exchange_current_density(variables)
 
-        # Get open-circuit potential
+        # Get open-circuit potential variables and reaction overpotential
         if (
             domain_options["particle size"] == "distribution"
             and self.options.electrode_types[domain] == "porous"
@@ -171,7 +185,17 @@ def get_coupled_variables(self, variables):
         # Update j, except in the "distributed SEI resistance" model, where j will be
         # found by solving an algebraic equation.
         # (In the "distributed SEI resistance" model, we have already defined j)
-        j = self._get_kinetics(j0, ne, eta_r, T, u)
+        # For MSMR model we calculate the total current density by summing the current
+        # densities from each reaction
+        if domain_options["intercalation kinetics"] == "MSMR":
+            j = 0
+            for i in range(N):
+                j0_j = self._get_exchange_current_density_by_reaction(variables, i)
+                j_j = self._get_kinetics_by_reaction(j0_j, ne, eta_r, T, u, i)
+                variables.update(self._get_standard_icd_by_reaction_variables(j_j, i))
+                j += j_j
+        else:
+            j = self._get_kinetics(j0, ne, eta_r, T, u)
 
         if j.domain == [f"{domain} particle size"]:
             # If j depends on particle size, get size-dependent "distribution"
diff --git a/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py
new file mode 100644
index 0000000000..6a4b9f5023
--- /dev/null
+++ b/pybamm/models/submodels/interface/kinetics/msmr_butler_volmer.py
@@ -0,0 +1,152 @@
+#
+# Bulter volmer class for the MSMR formulation
+#
+
+import pybamm
+from .base_kinetics import BaseKinetics
+
+
+class MSMRButlerVolmer(BaseKinetics):
+    """
+    Submodel which implements the forward Butler-Volmer equation in the MSMR
+    formulation in which the interfacial current density is summed over all
+    reactions.
+
+    Parameters
+    ----------
+    param : parameter class
+        model parameters
+    domain : str
+        The domain to implement the model, either: 'Negative' or 'Positive'.
+    reaction : str
+        The name of the reaction being implemented
+    options: dict
+        A dictionary of options to be passed to the model.
+        See :class:`pybamm.BaseBatteryModel`
+    phase : str, optional
+        Phase of the particle (default is "primary")
+    """
+
+    def __init__(self, param, domain, reaction, options, phase="primary"):
+        super().__init__(param, domain, reaction, options, phase)
+
+    def _get_exchange_current_density_by_reaction(self, variables, index):
+        """ "
+        A private function to obtain the exchange current density for each reaction
+        in the MSMR formulation.
+
+        Parameters
+        ----------
+        variables: dict
+            The variables in the full model.
+
+        Returns
+        -------
+        j0 : :class: `pybamm.Symbol`
+            The exchange current density.
+        """
+        phase_param = self.phase_param
+        domain, Domain = self.domain_Domain
+
+        c_e = variables[f"{Domain} electrolyte concentration [mol.m-3]"]
+        T = variables[f"{Domain} electrode temperature [K]"]
+
+        if self.reaction == "lithium-ion main":
+            # For "particle-size distribution" submodels, take distribution version
+            # of c_s_surf that depends on particle size.
+            domain_options = getattr(self.options, domain)
+            if domain_options["particle size"] == "distribution":
+                ocp = variables[
+                    f"{Domain} electrode open-circuit potential distribution [V]"
+                ]
+                # If all variables were broadcast (in "x"), take only the orphans,
+                # then re-broadcast c_e
+                if (
+                    isinstance(ocp, pybamm.Broadcast)
+                    and isinstance(c_e, pybamm.Broadcast)
+                    and isinstance(T, pybamm.Broadcast)
+                ):
+                    ocp = ocp.orphans[0]
+                    c_e = c_e.orphans[0]
+                    T = T.orphans[0]
+
+                    # as c_e must now be a scalar, re-broadcast to
+                    # "current collector"
+                    c_e = pybamm.PrimaryBroadcast(c_e, ["current collector"])
+                # broadcast c_e, T onto "particle size"
+                c_e = pybamm.PrimaryBroadcast(c_e, [f"{domain} particle size"])
+                T = pybamm.PrimaryBroadcast(T, [f"{domain} particle size"])
+
+            else:
+                ocp = variables[f"{Domain} electrode open-circuit potential [V]"]
+                # If all variables were broadcast, take only the orphans
+                if (
+                    isinstance(ocp, pybamm.Broadcast)
+                    and isinstance(c_e, pybamm.Broadcast)
+                    and isinstance(T, pybamm.Broadcast)
+                ):
+                    ocp = ocp.orphans[0]
+                    c_e = c_e.orphans[0]
+                    T = T.orphans[0]
+
+            j0 = phase_param.j0_j(c_e, ocp, T, index)
+
+        return j0
+
+    def _get_standard_exchange_current_by_reaction_variables(self, j0, index):
+        domain = self.domain
+        # Size average. For j0 variables that depend on particle size, see
+        # "_get_standard_size_distribution_exchange_current_variables"
+        if j0.domain in [["negative particle size"], ["positive particle size"]]:
+            j0 = pybamm.size_average(j0)
+        # Average, and broadcast if necessary
+        j0_av = pybamm.x_average(j0)
+
+        # X-average, and broadcast if necessary
+        if j0.domain == []:
+            j0 = pybamm.FullBroadcast(j0, f"{domain} electrode", "current collector")
+        elif j0.domain == ["current collector"]:
+            j0 = pybamm.PrimaryBroadcast(j0, f"{domain} electrode")
+
+        d = domain[0]
+        variables = {
+            f"j0_{d}_{index} [A.m-2]": j0,
+            f"X-averaged j0_{d}_{index} [A.m-2]": j0_av,
+        }
+
+        return variables
+
+    def _get_kinetics_by_reaction(self, j0, ne, eta_r, T, u, index):
+        alpha = self.phase_param.alpha_bv_j(index)
+        Feta_RT = self.param.F * eta_r / (self.param.R * T)
+        return (
+            u
+            * j0
+            * (
+                pybamm.exp(ne * (1 - alpha) * Feta_RT)
+                - pybamm.exp(-ne * alpha * Feta_RT)
+            )
+        )
+
+    def _get_standard_icd_by_reaction_variables(self, j, index):
+        domain = self.domain
+        j.print_name = f"j_{domain[0]}"
+
+        # Size average. For j variables that depend on particle size, see
+        # "_get_standard_size_distribution_interfacial_current_variables"
+        if j.domain in [["negative particle size"], ["positive particle size"]]:
+            j = pybamm.size_average(j)
+        # Average, and broadcast if necessary
+        j_av = pybamm.x_average(j)
+        if j.domain == []:
+            j = pybamm.FullBroadcast(j, f"{domain} electrode", "current collector")
+        elif j.domain == ["current collector"]:
+            j = pybamm.PrimaryBroadcast(j, f"{domain} electrode")
+
+        d = domain[0]
+        variables = {
+            f"j_{d}_{index} [A.m-2]": j,
+            f"X-averaged j_{d}_{index} [A.m-2]": j_av,
+        }
+
+        return variables
diff --git a/pybamm/models/submodels/interface/open_circuit_potential/__init__.py b/pybamm/models/submodels/interface/open_circuit_potential/__init__.py
index d644b87e76..5f8a409bba 100644
--- a/pybamm/models/submodels/interface/open_circuit_potential/__init__.py
+++ b/pybamm/models/submodels/interface/open_circuit_potential/__init__.py
@@ -1,3 +1,4 @@
 from .base_ocp import BaseOpenCircuitPotential
 from .single_ocp import SingleOpenCircuitPotential
 from .current_sigmoid_ocp import CurrentSigmoidOpenCircuitPotential
+from .msmr_ocp import MSMROpenCircuitPotential
diff --git a/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py
new file mode 100644
index 0000000000..2ac87279f2
--- /dev/null
+++ b/pybamm/models/submodels/interface/open_circuit_potential/msmr_ocp.py
@@ -0,0 +1,63 @@
+#
+# Open-circuit potential from the Multi-Species Multi-Reaction framework
+#
+import pybamm
+from . import BaseOpenCircuitPotential
+
+
+class MSMROpenCircuitPotential(BaseOpenCircuitPotential):
+    """
+    Class for open-circuit potential within the Multi-Species Multi-Reaction
+    framework :footcite:t:`Baker2018`. The thermodynamic model is presented in
+    :footcite:t:`Verbrugge2017`, along with parameter values for a number of
+    substitutional materials.
+    """
+
+    def get_coupled_variables(self, variables):
+        domain, Domain = self.domain_Domain
+        phase_name = self.phase_name
+
+        if self.reaction == "lithium-ion main":
+            T = variables[f"{Domain} electrode temperature [K]"]
+            # For "particle-size distribution" models, take distribution version
+            # of c_s_surf that depends on particle size.
+            domain_options = getattr(self.options, domain)
+            if domain_options["particle size"] == "distribution":
+                sto_surf = variables[
+                    f"{Domain} {phase_name}particle surface stoichiometry distribution"
+                ]
+                ocp_surf = variables[
+                    f"{Domain} {phase_name}particle surface potential distribution [V]"
+                ]
+                # If variable was broadcast, take only the orphan
+                if (
+                    isinstance(sto_surf, pybamm.Broadcast)
+                    and isinstance(ocp_surf, pybamm.Broadcast)
+                    and isinstance(T, pybamm.Broadcast)
+                ):
+                    sto_surf = sto_surf.orphans[0]
+                    ocp_surf = ocp_surf.orphans[0]
+                    T = T.orphans[0]
+                T = pybamm.PrimaryBroadcast(T, [f"{domain} particle size"])
+            else:
+                sto_surf = variables[
+                    f"{Domain} {phase_name}particle surface stoichiometry"
+                ]
+                ocp_surf = variables[
+                    f"{Domain} {phase_name}particle surface potential [V]"
+                ]
+                # If variable was broadcast, take only the orphan
+                if (
+                    isinstance(sto_surf, pybamm.Broadcast)
+                    and isinstance(ocp_surf, pybamm.Broadcast)
+                    and isinstance(T, pybamm.Broadcast)
+                ):
+                    sto_surf = sto_surf.orphans[0]
+                    ocp_surf = ocp_surf.orphans[0]
+                    T = T.orphans[0]
+
+            ocp_bulk = variables[f"Average {domain} {phase_name}particle potential [V]"]
+            dUdT = self.phase_param.dUdT(sto_surf)
+
+        variables.update(self._get_standard_ocp_variables(ocp_surf, ocp_bulk, dUdT))
+        return variables
diff --git a/pybamm/models/submodels/particle/__init__.py b/pybamm/models/submodels/particle/__init__.py
index 7f3c19953d..237b2c19c8 100644
--- a/pybamm/models/submodels/particle/__init__.py
+++ b/pybamm/models/submodels/particle/__init__.py
@@ -3,3 +3,4 @@
 from .polynomial_profile import PolynomialProfile
 from .x_averaged_polynomial_profile import XAveragedPolynomialProfile
 from .total_particle_concentration import TotalConcentration
+from .msmr_diffusion import MSMRDiffusion
diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
index 01188206c7..ad751c3911 100644
--- a/pybamm/models/submodels/particle/base_particle.py
+++ b/pybamm/models/submodels/particle/base_particle.py
@@ -49,7 +49,7 @@ def _get_effective_diffusivity(self, c, T, current):
             D_delith = phase_param.D(c, T, "delithiation")
             D = m_lith * D_lith + m_delith * D_delith
 
-        # Account for stress-induced difftusion by defining a multiplicative
+        # Account for stress-induced diffusion by defining a multiplicative
         # "stress factor"
         stress_option = getattr(self.options, domain)["stress-induced diffusion"]
 
@@ -71,7 +71,7 @@ def _get_standard_concentration_variables(
         """
         All particle submodels must provide the particle concentration as an argument
         to this method. Some submodels solve for quantities other than the concentration
-        itself, for example the 'XAveragedFickianDiffusion' models solves for the
+        itself, for example the 'XAveragedPolynomialProfile' models solves for the
         x-averaged concentration. In such cases the variables being solved for (set in
         'get_fundamental_variables') must also be passed as keyword arguments. If not
         passed as keyword arguments, the various average concentrations and surface
@@ -98,44 +98,64 @@ def _get_standard_concentration_variables(
             c_s_av = pybamm.r_average(c_s_xav)
 
         variables = {
-            f"{Domain} {phase_name}particle stoichiometry": c_s / c_scale,
-            f"{Domain} {phase_name}particle concentration": c_s / c_scale,
+            # Dimensional concentration
             f"{Domain} {phase_name}particle concentration [mol.m-3]": c_s,
-            f"X-averaged {domain} {phase_name}particle concentration": c_s_xav
-            / c_scale,
             f"X-averaged {domain} {phase_name}particle "
             "concentration [mol.m-3]": c_s_xav,
-            f"R-averaged {domain} {phase_name}particle concentration": c_s_rav
-            / c_scale,
             f"R-averaged {domain} {phase_name}particle "
             "concentration [mol.m-3]": c_s_rav,
-            f"Average {domain} {phase_name}particle concentration": c_s_av / c_scale,
             f"Average {domain} {phase_name}particle concentration [mol.m-3]": c_s_av,
-            f"{Domain} {phase_name}particle surface stoichiometry": c_s_surf / c_scale,
-            f"{Domain} {phase_name}particle surface concentration": c_s_surf / c_scale,
             f"{Domain} {phase_name}particle surface concentration [mol.m-3]": c_s_surf,
             f"X-averaged {domain} {phase_name}particle "
-            "surface concentration": c_s_surf_av / c_scale,
-            f"X-averaged {domain} {phase_name}particle "
             "surface concentration [mol.m-3]": c_s_surf_av,
-            f"{Domain} electrode extent of lithiation": c_s_rav / c_scale,
-            f"X-averaged {domain} electrode extent of lithiation": c_s_av / c_scale,
-            f"Minimum {domain} {phase_name}particle concentration": pybamm.min(c_s)
-            / c_scale,
-            f"Maximum {domain} {phase_name}particle concentration": pybamm.max(c_s)
-            / c_scale,
             f"Minimum {domain} {phase_name}particle concentration [mol.m-3]"
             "": pybamm.min(c_s),
             f"Maximum {domain} {phase_name}particle concentration [mol.m-3]"
             "": pybamm.max(c_s),
             f"Minimum {domain} {phase_name}particle "
+            f"Minimum {domain} {phase_name}particle "
+            "surface concentration [mol.m-3]": pybamm.min(c_s_surf),
+            f"Maximum {domain} {phase_name}particle "
+            "surface concentration [mol.m-3]": pybamm.max(c_s_surf),
+            # Dimensionless concentration
+            f"{Domain} {phase_name}particle concentration": c_s / c_scale,
+            f"X-averaged {domain} {phase_name}particle concentration": c_s_xav
+            / c_scale,
+            f"R-averaged {domain} {phase_name}particle concentration": c_s_rav
+            / c_scale,
+            f"Average {domain} {phase_name}particle concentration": c_s_av / c_scale,
+            f"{Domain} {phase_name}particle surface concentration": c_s_surf / c_scale,
+            f"X-averaged {domain} {phase_name}particle "
+            "surface concentration": c_s_surf_av / c_scale,
+            f"Minimum {domain} {phase_name}particle concentration": pybamm.min(c_s)
+            / c_scale,
+            f"Maximum {domain} {phase_name}particle concentration": pybamm.max(c_s)
+            / c_scale,
+            f"Minimum {domain} {phase_name}particle "
             "surface concentration": pybamm.min(c_s_surf) / c_scale,
             f"Maximum {domain} {phase_name}particle "
             "surface concentration": pybamm.max(c_s_surf) / c_scale,
+            # Stoichiometry (equivalent to dimensionless concentration)
+            f"{Domain} {phase_name}particle stoichiometry": c_s / c_scale,
+            f"X-averaged {domain} {phase_name}particle stoichiometry": c_s_xav
+            / c_scale,
+            f"R-averaged {domain} {phase_name}particle stoichiometry": c_s_rav
+            / c_scale,
+            f"Average {domain} {phase_name}particle stoichiometry": c_s_av / c_scale,
+            f"{Domain} {phase_name}particle surface stoichiometry": c_s_surf / c_scale,
+            f"X-averaged {domain} {phase_name}particle "
+            "surface stoichiometry": c_s_surf_av / c_scale,
+            f"Minimum {domain} {phase_name}particle stoichiometry": pybamm.min(c_s)
+            / c_scale,
+            f"Maximum {domain} {phase_name}particle stoichiometry": pybamm.max(c_s)
+            / c_scale,
             f"Minimum {domain} {phase_name}particle "
-            "surface concentration [mol.m-3]": pybamm.min(c_s_surf),
+            "surface stoichiometry": pybamm.min(c_s_surf) / c_scale,
             f"Maximum {domain} {phase_name}particle "
-            "surface concentration [mol.m-3]": pybamm.max(c_s_surf),
+            "surface stoichiometry": pybamm.max(c_s_surf) / c_scale,
+            # Electrode extent of lithiation
+            f"{Domain} electrode extent of lithiation": c_s_rav / c_scale,
+            f"X-averaged {domain} electrode extent of lithiation": c_s_av / c_scale,
         }
 
         return variables
@@ -302,7 +322,7 @@ def _get_standard_concentration_distribution_variables(self, c_s):
                 c_s_surf_xav_distribution, [f"{domain} {phase_name}particle"]
             )
 
-            # Concentration distribution in all domains.
+            # Concentration distribution in all domains
             c_s_distribution = pybamm.PrimaryBroadcast(
                 c_s_surf_distribution, [f"{domain} {phase_name}particle"]
             )
@@ -328,32 +348,49 @@ def _get_standard_concentration_distribution_variables(self, c_s):
         c_s_av_distribution = pybamm.x_average(c_s_rav_distribution)
 
         variables = {
-            f"Average {domain} {phase_name}particle concentration "
-            "distribution": c_s_av_distribution / c_scale,
-            f"Average {domain} {phase_name}particle concentration "
-            "distribution [mol.m-3]": c_s_av_distribution,
-            f"{Domain} {phase_name}particle concentration "
-            "distribution": c_s_distribution / c_scale,
+            # Dimensional concentration
             f"{Domain} {phase_name}particle concentration distribution "
             "[mol.m-3]": c_s_distribution,
-            f"R-averaged {domain} {phase_name}particle concentration "
-            "distribution": c_s_rav_distribution / c_scale,
-            f"R-averaged {domain} {phase_name}particle concentration distribution "
-            "[mol.m-3]": c_s_rav_distribution,
-            f"X-averaged {domain} {phase_name}particle concentration "
-            "distribution": c_s_xav_distribution / c_scale,
             f"X-averaged {domain} {phase_name}particle concentration distribution "
             "[mol.m-3]": c_s_xav_distribution,
-            f"X-averaged {domain} {phase_name}particle surface concentration"
-            " distribution": c_s_surf_xav_distribution / c_scale,
+            f"R-averaged {domain} {phase_name}particle concentration distribution "
+            "[mol.m-3]": c_s_rav_distribution,
+            f"Average {domain} {phase_name}particle concentration "
+            "distribution [mol.m-3]": c_s_av_distribution,
+            f"{Domain} {phase_name}particle surface concentration"
+            " distribution [mol.m-3]": c_s_surf_distribution,
             f"X-averaged {domain} {phase_name}particle surface concentration "
             "distribution [mol.m-3]": c_s_surf_xav_distribution,
+            # Dimensionless concentration
+            f"{Domain} {phase_name}particle concentration "
+            "distribution": c_s_distribution / c_scale,
+            f"X-averaged {domain} {phase_name}particle concentration "
+            "distribution": c_s_xav_distribution / c_scale,
+            f"R-averaged {domain} {phase_name}particle concentration "
+            "distribution": c_s_rav_distribution / c_scale,
+            f"Average {domain} {phase_name}particle concentration "
+            "distribution": c_s_av_distribution / c_scale,
             f"{Domain} {phase_name}particle surface concentration"
             " distribution": c_s_surf_distribution / c_scale,
+            f"X-averaged {domain} {phase_name}particle surface concentration"
+            " distribution": c_s_surf_xav_distribution / c_scale,
+            # Stoichiometry (equivalent to dimensionless concentration)
+            f"{Domain} {phase_name}particle stoichiometry "
+            "distribution": c_s_distribution / c_scale,
+            f"X-averaged {domain} {phase_name}particle stoichiometry "
+            "distribution": c_s_xav_distribution / c_scale,
+            f"R-averaged {domain} {phase_name}particle stoichiometry "
+            "distribution": c_s_rav_distribution / c_scale,
+            f"Average {domain} {phase_name}particle stoichiometry "
+            "distribution": c_s_av_distribution / c_scale,
             f"{Domain} {phase_name}particle surface stoichiometry"
             " distribution": c_s_surf_distribution / c_scale,
-            f"{Domain} {phase_name}particle surface concentration"
-            " distribution [mol.m-3]": c_s_surf_distribution,
+            f"X-averaged {domain} {phase_name}particle surface stoichiometry"
+            " distribution": c_s_surf_xav_distribution / c_scale,
+            # Electrode extent of lithiation
+            f"{Domain} electrode extent of lithiation": c_s_rav_distribution / c_scale,
+            f"X-averaged {domain} electrode extent of lithiation": c_s_av_distribution
+            / c_scale,
         }
         return variables
 
diff --git a/pybamm/models/submodels/particle/fickian_diffusion.py b/pybamm/models/submodels/particle/fickian_diffusion.py
index 7707284a36..31c5e6be6c 100644
--- a/pybamm/models/submodels/particle/fickian_diffusion.py
+++ b/pybamm/models/submodels/particle/fickian_diffusion.py
@@ -122,6 +122,7 @@ def get_fundamental_variables(self):
             if self.x_average is True:
                 c_s = pybamm.SecondaryBroadcast(c_s, [f"{domain} electrode"])
 
+        # Standard concentration variables (size-independent)
         variables.update(self._get_standard_concentration_variables(c_s))
 
         return variables
@@ -169,7 +170,6 @@ def get_coupled_variables(self, variables):
                     f"{Domain} {phase_name}particle "
                     "concentration distribution [mol.m-3]"
                 ]
-
                 # broadcast T to "particle size" domain then again into "particle"
                 T = pybamm.PrimaryBroadcast(
                     variables[f"{Domain} electrode temperature [K]"],
@@ -185,7 +185,6 @@ def get_coupled_variables(self, variables):
                     f"X-averaged {domain} {phase_name}particle "
                     "concentration distribution [mol.m-3]"
                 ]
-
                 # broadcast to "particle size" domain then again into "particle"
                 T = pybamm.PrimaryBroadcast(
                     variables[f"X-averaged {domain} electrode temperature [K]"],
@@ -207,7 +206,7 @@ def get_coupled_variables(self, variables):
                     1 / (R_broad_nondim**2)
                 )
                 * pybamm.div(N_s),
-                f"{Domain} {phase_name}particle bc [mol.m-2]": -j
+                f"{Domain} {phase_name}particle bc [mol.m-4]": -j
                 * R_nondim
                 / param.F
                 / pybamm.surf(D_eff),
@@ -286,7 +285,7 @@ def set_boundary_conditions(self, variables):
                     "concentration distribution [mol.m-3]"
                 ]
 
-        rbc = variables[f"{Domain} {phase_name}particle bc [mol.m-2]"]
+        rbc = variables[f"{Domain} {phase_name}particle bc [mol.m-4]"]
         self.boundary_conditions = {
             c_s: {"left": (pybamm.Scalar(0), "Neumann"), "right": (rbc, "Neumann")}
         }
diff --git a/pybamm/models/submodels/particle/msmr_diffusion.py b/pybamm/models/submodels/particle/msmr_diffusion.py
new file mode 100644
index 0000000000..65ab913e97
--- /dev/null
+++ b/pybamm/models/submodels/particle/msmr_diffusion.py
@@ -0,0 +1,665 @@
+#
+# Class for particles using the MSMR model
+#
+import pybamm
+from .base_particle import BaseParticle
+
+
+class MSMRDiffusion(BaseParticle):
+    """
+    Class for molar conservation in particles within the Multi-Species Multi-Reaction
+    framework :footcite:t:`Baker2018`. The thermodynamic model is presented in
+    :footcite:t:`Verbrugge2017`, along with parameter values for a number of
+    substitutional materials.
+
+    Parameters
+    ----------
+    param : parameter class
+        The parameters to use for this submodel
+    domain : str
+        The domain of the model either 'Negative' or 'Positive'
+    options: dict
+        A dictionary of options to be passed to the model.
+        See :class:`pybamm.BaseBatteryModel`
+    phase : str, optional
+        Phase of the particle (default is "primary")
+    x_average : bool
+        Whether the particle concentration is averaged over the x-direction
+    """
+
+    def __init__(self, param, domain, options, phase="primary", x_average=False):
+        super().__init__(param, domain, options, phase)
+        self.x_average = x_average
+
+        pybamm.citations.register("Baker2018")
+        pybamm.citations.register("Verbrugge2017")
+
+    def get_fundamental_variables(self):
+        domain, Domain = self.domain_Domain
+        phase_name = self.phase_name
+
+        variables = {}
+
+        # Define "particle" potential variables. In the MSMR model, we solve for the
+        # potential as a function of position within the electrode and particles (and
+        # particle-size distribution, if applicable). The potential is then used to
+        # calculate the stoichiometry, which is used to calculate the particle
+        # concentration.
+        c_max = self.phase_param.c_max
+        if self.size_distribution is False:
+            if self.x_average is False:
+                U = pybamm.Variable(
+                    f"{Domain} {phase_name}particle potential [V]",
+                    f"{domain} {phase_name}particle",
+                    auxiliary_domains={
+                        "secondary": f"{domain} electrode",
+                        "tertiary": "current collector",
+                    },
+                )
+                U.print_name = f"U_{domain[0]}"
+            else:
+                U_xav = pybamm.Variable(
+                    f"X-averaged {domain} {phase_name}particle " "potential [V]",
+                    f"{domain} {phase_name}particle",
+                    auxiliary_domains={"secondary": "current collector"},
+                )
+                U_xav.print_name = f"U_{domain[0]}_xav"
+                U = pybamm.SecondaryBroadcast(U_xav, f"{domain} electrode")
+        else:
+            if self.x_average is False:
+                U_distribution = pybamm.Variable(
+                    f"{Domain} {phase_name}particle " "potential distribution [V]",
+                    domain=f"{domain} {phase_name}particle",
+                    auxiliary_domains={
+                        "secondary": f"{domain} {phase_name}particle size",
+                        "tertiary": f"{domain} electrode",
+                        "quaternary": "current collector",
+                    },
+                )
+                R = pybamm.SpatialVariable(
+                    f"R_{domain[0]}",
+                    domain=[f"{domain} {phase_name}particle size"],
+                    auxiliary_domains={
+                        "secondary": f"{domain} electrode",
+                        "tertiary": "current collector",
+                    },
+                    coord_sys="cartesian",
+                )
+                variables = self._get_distribution_variables(R)
+                f_v_dist = variables[
+                    f"{Domain} volume-weighted {phase_name}"
+                    "particle-size distribution [m-1]"
+                ]
+            else:
+                U_distribution = pybamm.Variable(
+                    f"X-averaged {domain} {phase_name}particle "
+                    "potential distribution [V]",
+                    domain=f"{domain} {phase_name}particle",
+                    auxiliary_domains={
+                        "secondary": f"{domain} {phase_name}particle size",
+                        "tertiary": "current collector",
+                    },
+                )
+                R = pybamm.SpatialVariable(
+                    f"R_{domain[0]}",
+                    domain=[f"{domain} {phase_name}particle size"],
+                    auxiliary_domains={"secondary": "current collector"},
+                    coord_sys="cartesian",
+                )
+                variables = self._get_distribution_variables(R)
+                f_v_dist = variables[
+                    f"X-averaged {domain} volume-weighted {phase_name}"
+                    "particle-size distribution [m-1]"
+                ]
+
+            # Standard potential distribution_variables
+            variables.update(
+                self._get_standard_potential_distribution_variables(U_distribution)
+            )
+
+            # Calculate the stoichiometry distribution from the potential distribution
+            x_distribution = self.phase_param.x(U_distribution)
+            dxdU_distribution = self.phase_param.dxdU(U_distribution)
+
+            # Standard stoichiometry and concentration distribution variables
+            # (size-dependent)
+            c_s_distribution = x_distribution * c_max
+            variables.update(
+                self._get_standard_concentration_distribution_variables(
+                    c_s_distribution
+                )
+            )
+            variables.update(
+                self._get_standard_differential_stoichiometry_distribution_variables(
+                    dxdU_distribution
+                )
+            )
+
+            # Standard size-averaged variables. Average potentials using
+            # the volume-weighted distribution since they are volume-based
+            # quantities. Necessary for output variables "Total lithium in
+            # negative electrode [mol]", etc, to be calculated correctly
+            U = pybamm.Integral(f_v_dist * U_distribution, R)
+            if self.x_average is True:
+                U = pybamm.SecondaryBroadcast(U, [f"{domain} electrode"])
+
+        # Standard potential variables
+        variables.update(self._get_standard_potential_variables(U))
+
+        # Standard fractional occupancy variables (these are indexed by reaction number)
+        variables.update(self._get_standard_fractional_occupancy_variables(U))
+        variables.update(
+            self._get_standard_differential_fractional_occupancy_variables(U)
+        )
+
+        # Calculate the (total) stoichiometry from the potential
+        x = self.phase_param.x(U)
+        dxdU = self.phase_param.dxdU(U)
+
+        # Standard (total) stoichiometry and concentration variables (size-independent)
+        c_s = x * c_max
+        variables.update(self._get_standard_concentration_variables(c_s))
+        variables.update(self._get_standard_differential_stoichiometry_variables(dxdU))
+
+        return variables
+
+    def get_coupled_variables(self, variables):
+        domain, Domain = self.domain_Domain
+        phase_name = self.phase_name
+        param = self.param
+
+        if self.size_distribution is False:
+            if self.x_average is False:
+                x = variables[f"{Domain} {phase_name}particle stoichiometry"]
+                dxdU = variables[
+                    f"{Domain} {phase_name}particle differential stoichiometry [V-1]"
+                ]
+                U = variables[f"{Domain} {phase_name}particle potential [V]"]
+                T = pybamm.PrimaryBroadcast(
+                    variables[f"{Domain} electrode temperature [K]"],
+                    [f"{domain} {phase_name}particle"],
+                )
+                R_nondim = variables[f"{Domain} {phase_name}particle radius"]
+                j = variables[
+                    f"{Domain} electrode {phase_name}"
+                    "interfacial current density [A.m-2]"
+                ]
+            else:
+                x = variables[f"X-averaged {domain} {phase_name}particle stoichiometry"]
+                dxdU = variables[
+                    f"X-averaged {domain} {phase_name}particle differential "
+                    "stoichiometry [V-1]"
+                ]
+                U = variables[
+                    f"X-averaged {domain} {phase_name}particle " "potential [V]"
+                ]
+                T = pybamm.PrimaryBroadcast(
+                    variables[f"X-averaged {domain} electrode temperature [K]"],
+                    [f"{domain} {phase_name}particle"],
+                )
+                R_nondim = 1
+                j = variables[
+                    f"X-averaged {domain} electrode {phase_name}"
+                    "interfacial current density [A.m-2]"
+                ]
+            R_broad_nondim = R_nondim
+        else:
+            R_nondim = variables[f"{Domain} {phase_name}particle sizes"]
+            R_broad_nondim = pybamm.PrimaryBroadcast(
+                R_nondim, [f"{domain} {phase_name}particle"]
+            )
+            if self.x_average is False:
+                x = variables[
+                    f"{Domain} {phase_name}particle stoichiometry distribution"
+                ]
+                dxdU = variables[
+                    f"{Domain} {phase_name}particle differential stoichiometry "
+                    "distribution [V-1]"
+                ]
+                U = variables[
+                    f"{Domain} {phase_name}particle potential " "distribution [V]"
+                ]
+                # broadcast T to "particle size" domain then again into "particle"
+                T = pybamm.PrimaryBroadcast(
+                    variables[f"{Domain} electrode temperature [K]"],
+                    [f"{domain} {phase_name}particle size"],
+                )
+                T = pybamm.PrimaryBroadcast(T, [f"{domain} {phase_name}particle"])
+                j = variables[
+                    f"{Domain} electrode {phase_name}interfacial "
+                    "current density distribution [A.m-2]"
+                ]
+            else:
+                x = variables[
+                    f"X-averaged {domain} {phase_name}particle "
+                    "stoichiometry distribution"
+                ]
+                dxdU = variables[
+                    f"X-averaged {domain} {phase_name}particle "
+                    "differential stoichiometry distribution [V-1]"
+                ]
+                U = variables[
+                    f"X-averaged {domain} {phase_name}particle "
+                    "potential distribution [V]"
+                ]
+                # broadcast to "particle size" domain then again into "particle"
+                T = pybamm.PrimaryBroadcast(
+                    variables[f"X-averaged {domain} electrode temperature [K]"],
+                    [f"{domain} {phase_name}particle size"],
+                )
+                T = pybamm.PrimaryBroadcast(T, [f"{domain} {phase_name}particle"])
+                j = variables[
+                    f"X-averaged {domain} electrode {phase_name}interfacial "
+                    "current density distribution [A.m-2]"
+                ]
+
+        # Note: diffusivity is given as a function of concentration here,
+        # not stoichiometry
+        c_max = self.phase_param.c_max
+        current = variables["Total current density [A.m-2]"]
+        D_eff = self._get_effective_diffusivity(x * c_max, T, current)
+        f = self.param.F / (self.param.R * T)
+        N_s = c_max * x * (1 - x) * f * D_eff * pybamm.grad(U)
+        variables.update(
+            {
+                f"{Domain} {phase_name}particle rhs [V.s-1]": -(
+                    1 / (R_broad_nondim**2)
+                )
+                * pybamm.div(N_s)
+                / c_max
+                / dxdU,
+                f"{Domain} {phase_name}particle bc [V.m-1]": j
+                * R_nondim
+                / param.F
+                / pybamm.surf(c_max * x * (1 - x) * f * D_eff),
+            }
+        )
+
+        if self.size_distribution is True:
+            # Size-dependent flux variables
+            variables.update(
+                self._get_standard_diffusivity_distribution_variables(D_eff)
+            )
+            variables.update(self._get_standard_flux_distribution_variables(N_s))
+            # Size-averaged flux variables
+            R = variables[f"{Domain} {phase_name}particle sizes [m]"]
+            f_a_dist = self.phase_param.f_a_dist(R)
+            D_eff = pybamm.Integral(f_a_dist * D_eff, R)
+            N_s = pybamm.Integral(f_a_dist * N_s, R)
+
+        if self.x_average is True:
+            D_eff = pybamm.SecondaryBroadcast(D_eff, [f"{domain} electrode"])
+            N_s = pybamm.SecondaryBroadcast(N_s, [f"{domain} electrode"])
+
+        variables.update(self._get_standard_diffusivity_variables(D_eff))
+        variables.update(self._get_standard_flux_variables(N_s))
+
+        return variables
+
+    def set_rhs(self, variables):
+        domain, Domain = self.domain_Domain
+        phase_name = self.phase_name
+
+        if self.size_distribution is False:
+            if self.x_average is False:
+                U = variables[f"{Domain} {phase_name}particle potential [V]"]
+            else:
+                U = variables[
+                    f"X-averaged {domain} {phase_name}particle " "potential [V]"
+                ]
+        else:
+            if self.x_average is False:
+                U = variables[
+                    f"{Domain} {phase_name}particle " "potential distribution [V]"
+                ]
+            else:
+                U = variables[
+                    f"X-averaged {domain} {phase_name}particle "
+                    "potential distribution [V]"
+                ]
+        self.rhs = {U: variables[f"{Domain} {phase_name}particle rhs [V.s-1]"]}
+
+    def set_boundary_conditions(self, variables):
+        domain, Domain = self.domain_Domain
+        phase_name = self.phase_name
+
+        if self.size_distribution is False:
+            if self.x_average is False:
+                U = variables[f"{Domain} {phase_name}particle potential [V]"]
+            else:
+                U = variables[
+                    f"X-averaged {domain} {phase_name}particle " "potential [V]"
+                ]
+        else:
+            if self.x_average is False:
+                U = variables[
+                    f"{Domain} {phase_name}particle " "potential distribution [V]"
+                ]
+            else:
+                U = variables[
+                    f"X-averaged {domain} {phase_name}particle "
+                    "potential distribution [V]"
+                ]
+
+        rbc = variables[f"{Domain} {phase_name}particle bc [V.m-1]"]
+        self.boundary_conditions = {
+            U: {"left": (pybamm.Scalar(0), "Neumann"), "right": (rbc, "Neumann")}
+        }
+
+    def set_initial_conditions(self, variables):
+        domain, Domain = self.domain_Domain
+        phase_name = self.phase_name
+
+        U_init = self.phase_param.U_init
+        if self.size_distribution is False:
+            if self.x_average is False:
+                U = variables[f"{Domain} {phase_name}particle potential [V]"]
+            else:
+                U = variables[
+                    f"X-averaged {domain} {phase_name}particle " "potential [V]"
+                ]
+        else:
+            if self.x_average is False:
+                U = variables[
+                    f"{Domain} {phase_name}particle " "potential distribution [V]"
+                ]
+            else:
+                U = variables[
+                    f"X-averaged {domain} {phase_name}particle "
+                    "potential distribution [V]"
+                ]
+        self.initial_conditions = {U: U_init}
+
+    def _get_standard_potential_variables(self, U):
+        """
+        A private function to obtain the standard variables which can be derived from
+        the potential.
+        """
+        domain, Domain = self.domain_Domain
+        phase_name = self.phase_name
+        U_surf = pybamm.surf(U)
+        U_surf_av = pybamm.x_average(U_surf)
+        U_xav = pybamm.x_average(U)
+        U_rav = pybamm.r_average(U)
+        U_av = pybamm.r_average(U_xav)
+        variables = {
+            f"{Domain} {phase_name}particle potential [V]": U,
+            f"X-averaged {domain} {phase_name}particle " "potential [V]": U_xav,
+            f"R-averaged {domain} {phase_name}particle " "potential [V]": U_rav,
+            f"Average {domain} {phase_name}particle potential [V]": U_av,
+            f"{Domain} {phase_name}particle surface potential [V]": U_surf,
+            f"X-averaged {domain} {phase_name}particle "
+            "surface potential [V]": U_surf_av,
+            f"Minimum {domain} {phase_name}particle potential [V]" "": pybamm.min(U),
+            f"Maximum {domain} {phase_name}particle potential [V]" "": pybamm.max(U),
+            f"Minimum {domain} {phase_name}particle "
+            "surface potential [V]": pybamm.min(U_surf),
+            f"Maximum {domain} {phase_name}particle "
+            "surface potential [V]": pybamm.max(U_surf),
+        }
+        return variables
+
+    def _get_standard_potential_distribution_variables(self, U):
+        """
+        A private function to obtain the standard variables which can be derived from
+        the potential distribution in particle size.
+        """
+        domain, Domain = self.domain_Domain
+        phase_name = self.phase_name
+
+        # Broadcast and x-average when necessary
+        if U.domain == [f"{domain} {phase_name}particle size"] and U.domains[
+            "secondary"
+        ] != [f"{domain} electrode"]:
+            # X-avg potential distribution
+            U_xav_distribution = pybamm.PrimaryBroadcast(
+                U, [f"{domain} {phase_name}particle"]
+            )
+
+            # Surface potential distribution variables
+            U_surf_xav_distribution = U
+            U_surf_distribution = pybamm.SecondaryBroadcast(
+                U_surf_xav_distribution, [f"{domain} electrode"]
+            )
+
+            # potential distribution in all domains.
+            U_distribution = pybamm.PrimaryBroadcast(
+                U_surf_distribution, [f"{domain} {phase_name}particle"]
+            )
+        elif U.domain == [f"{domain} {phase_name}particle"] and (
+            U.domains["tertiary"] != [f"{domain} electrode"]
+        ):
+            # X-avg potential distribution
+            U_xav_distribution = U
+
+            # Surface potential distribution variables
+            U_surf_xav_distribution = pybamm.surf(U_xav_distribution)
+            U_surf_distribution = pybamm.SecondaryBroadcast(
+                U_surf_xav_distribution, [f"{domain} electrode"]
+            )
+
+            # potential distribution in all domains
+            U_distribution = pybamm.TertiaryBroadcast(
+                U_xav_distribution, [f"{domain} electrode"]
+            )
+        elif U.domain == [f"{domain} {phase_name}particle size"] and U.domains[
+            "secondary"
+        ] == [f"{domain} electrode"]:
+            # Surface potential distribution variables
+            U_surf_distribution = U
+            U_surf_xav_distribution = pybamm.x_average(U)
+
+            # X-avg potential distribution
+            U_xav_distribution = pybamm.PrimaryBroadcast(
+                U_surf_xav_distribution, [f"{domain} {phase_name}particle"]
+            )
+
+            # potential distribution in all domains
+            U_distribution = pybamm.PrimaryBroadcast(
+                U_surf_distribution, [f"{domain} {phase_name}particle"]
+            )
+        else:
+            U_distribution = U
+
+            # x-average the *tertiary* domain.
+            # NOTE: not yet implemented. Make 0.5 everywhere
+            U_xav_distribution = pybamm.FullBroadcast(
+                0.5,
+                [f"{domain} {phase_name}particle"],
+                {
+                    "secondary": f"{domain} {phase_name}particle size",
+                    "tertiary": "current collector",
+                },
+            )
+
+            # Surface potential distribution variables
+            U_surf_distribution = pybamm.surf(U)
+            U_surf_xav_distribution = pybamm.x_average(U_surf_distribution)
+
+        U_rav_distribution = pybamm.r_average(U_distribution)
+        U_av_distribution = pybamm.x_average(U_rav_distribution)
+
+        variables = {
+            f"{Domain} {phase_name}particle potential distribution [V]": U_distribution,
+            f"X-averaged {domain} {phase_name}particle potential "
+            "distribution [V]": U_xav_distribution,
+            f"R-averaged {domain} {phase_name}particle potential "
+            "distribution [V]": U_rav_distribution,
+            f"Average {domain} {phase_name}particle potential "
+            "distribution [V]": U_av_distribution,
+            f"{Domain} {phase_name}particle surface potential"
+            " distribution [V]": U_surf_distribution,
+            f"X-averaged {domain} {phase_name}particle surface potential "
+            "distribution [V]": U_surf_xav_distribution,
+        }
+        return variables
+
+    def _get_standard_fractional_occupancy_variables(self, U):
+        options = self.options
+        domain = self.domain
+        d = domain[0]
+        variables = {}
+        # Loop over all reactions
+        N = int(getattr(options, domain)["number of MSMR reactions"])
+        for i in range(N):
+            x = self.phase_param.x_j(U, i)
+            x_surf = pybamm.surf(x)
+            x_surf_av = pybamm.x_average(x_surf)
+            x_xav = pybamm.x_average(x)
+            x_rav = pybamm.r_average(x)
+            x_av = pybamm.r_average(x_xav)
+            variables.update(
+                {
+                    f"x_{d}_{i}": x,
+                    f"X-averaged x_{d}_{i}": x_xav,
+                    f"R-averaged x_{d}_{i}": x_rav,
+                    f"Average x_{d}_{i}": x_av,
+                    f"Surface x_{d}_{i}": x_surf,
+                    f"X-averaged surface x_{d}_{i}": x_surf_av,
+                }
+            )
+        return variables
+
+    def _get_standard_differential_fractional_occupancy_variables(self, U):
+        options = self.options
+        domain = self.domain
+        d = domain[0]
+        variables = {}
+        # Loop over all reactions
+        N = int(getattr(options, domain)["number of MSMR reactions"])
+        for i in range(N):
+            dxdU = self.phase_param.dxdU_j(U, i)
+            dxdU_surf = pybamm.surf(dxdU)
+            dxdU_surf_av = pybamm.x_average(dxdU_surf)
+            dxdU_xav = pybamm.x_average(dxdU)
+            dxdU_rav = pybamm.r_average(dxdU)
+            dxdU_av = pybamm.r_average(dxdU_xav)
+            variables.update(
+                {
+                    f"dxdU_{d}_{i}": dxdU,
+                    f"X-averaged dxdU_{d}_{i}": dxdU_xav,
+                    f"R-averaged dxdU_{d}_{i}": dxdU_rav,
+                    f"Average dxdU_{d}_{i}": dxdU_av,
+                    f"Surface dxdU_{d}_{i}": dxdU_surf,
+                    f"X-averaged surface dxdU_{d}_{i}": dxdU_surf_av,
+                }
+            )
+        return variables
+
+    def _get_standard_differential_stoichiometry_variables(self, dxdU):
+        domain, Domain = self.domain_Domain
+        phase_name = self.phase_name
+
+        dxdU_surf = pybamm.surf(dxdU)
+        dxdU_surf_av = pybamm.x_average(dxdU_surf)
+        dxdU_xav = pybamm.x_average(dxdU)
+        dxdU_rav = pybamm.r_average(dxdU)
+        dxdU_av = pybamm.r_average(dxdU_xav)
+
+        variables = {
+            f"{Domain} {phase_name}particle differential stoichiometry [V-1]": dxdU,
+            f"X-averaged {domain} {phase_name}particle "
+            "differential stoichiometry [V-1]": dxdU_xav,
+            f"R-averaged {domain} {phase_name}particle "
+            "differential stoichiometry [V-1]": dxdU_rav,
+            f"Average {domain} {phase_name}particle differential "
+            "stoichiometry [V-1]": dxdU_av,
+            f"{Domain} {phase_name}particle surface differential "
+            "stoichiometry [V-1]": dxdU_surf,
+            f"X-averaged {domain} {phase_name}particle "
+            "surface differential stoichiometry [V-1]": dxdU_surf_av,
+        }
+
+        return variables
+
+    def _get_standard_differential_stoichiometry_distribution_variables(self, dxdU):
+        domain, Domain = self.domain_Domain
+        phase_name = self.phase_name
+
+        # Broadcast and x-average when necessary
+        if dxdU.domain == [f"{domain} {phase_name}particle size"] and dxdU.domains[
+            "secondary"
+        ] != [f"{domain} electrode"]:
+            # X-avg differential stoichiometry distribution
+            dxdU_xav_distribution = pybamm.PrimaryBroadcast(
+                dxdU, [f"{domain} {phase_name}particle"]
+            )
+
+            # Surface differential stoichiometry distribution variables
+            dxdU_surf_xav_distribution = dxdU
+            dxdU_surf_distribution = pybamm.SecondaryBroadcast(
+                dxdU_surf_xav_distribution, [f"{domain} electrode"]
+            )
+
+            # Differential stoichiometry distribution in all domains.
+            dxdU_distribution = pybamm.PrimaryBroadcast(
+                dxdU_surf_distribution, [f"{domain} {phase_name}particle"]
+            )
+        elif dxdU.domain == [f"{domain} {phase_name}particle"] and (
+            dxdU.domains["tertiary"] != [f"{domain} electrode"]
+        ):
+            # X-avg differential stoichiometry distribution
+            dxdU_xav_distribution = dxdU
+
+            # Surface differential stoichiometry distribution variables
+            dxdU_surf_xav_distribution = pybamm.surf(dxdU_xav_distribution)
+            dxdU_surf_distribution = pybamm.SecondaryBroadcast(
+                dxdU_surf_xav_distribution, [f"{domain} electrode"]
+            )
+
+            # Differential stoichiometry distribution in all domains.
+            dxdU_distribution = pybamm.TertiaryBroadcast(
+                dxdU_xav_distribution, [f"{domain} electrode"]
+            )
+        elif dxdU.domain == [f"{domain} {phase_name}particle size"] and dxdU.domains[
+            "secondary"
+        ] == [f"{domain} electrode"]:
+            # Surface differential stoichiometry distribution variables
+            dxdU_surf_distribution = dxdU
+            dxdU_surf_xav_distribution = pybamm.x_average(dxdU)
+
+            # X-avg differential stoichiometry distribution
+            dxdU_xav_distribution = pybamm.PrimaryBroadcast(
+                dxdU_surf_xav_distribution, [f"{domain} {phase_name}particle"]
+            )
+
+            # Differential stoichiometry distribution in all domains
+            dxdU_distribution = pybamm.PrimaryBroadcast(
+                dxdU_surf_distribution, [f"{domain} {phase_name}particle"]
+            )
+        else:
+            dxdU_distribution = dxdU
+
+            # x-average the *tertiary* domain.
+            # NOTE: not yet implemented. Make 0.5 everywhere
+            dxdU_xav_distribution = pybamm.FullBroadcast(
+                0.5,
+                [f"{domain} {phase_name}particle"],
+                {
+                    "secondary": f"{domain} {phase_name}particle size",
+                    "tertiary": "current collector",
+                },
+            )
+
+            # Surface differential stoichiometry distribution variables
+            dxdU_surf_distribution = pybamm.surf(dxdU)
+            dxdU_surf_xav_distribution = pybamm.x_average(dxdU_surf_distribution)
+
+        dxdU_rav_distribution = pybamm.r_average(dxdU_distribution)
+        dxdU_av_distribution = pybamm.x_average(dxdU_rav_distribution)
+
+        variables = {
+            f"{Domain} {phase_name}particle differential stoichiometry distribution "
+            "[V-1]": dxdU_distribution,
+            f"X-averaged {domain} {phase_name}particle differential stoichiometry "
+            "distribution [V-1]": dxdU_xav_distribution,
+            f"R-averaged {domain} {phase_name}particle differential stoichiometry "
+            "distribution [V-1]": dxdU_rav_distribution,
+            f"Average {domain} {phase_name}particle differential stoichiometry "
+            "distribution [V-1]": dxdU_av_distribution,
+            f"{Domain} {phase_name}particle surface differential stoichiometry"
+            " distribution [V-1]": dxdU_surf_distribution,
+            f"X-averaged {domain} {phase_name}particle surface differential "
+            "stoichiometry distribution [V-1]": dxdU_surf_xav_distribution,
+        }
+        return variables
diff --git a/pybamm/parameters/electrical_parameters.py b/pybamm/parameters/electrical_parameters.py
index e4d574daed..946c47f53b 100644
--- a/pybamm/parameters/electrical_parameters.py
+++ b/pybamm/parameters/electrical_parameters.py
@@ -30,10 +30,10 @@ def _set_parameters(self):
         )
         self.voltage_low_cut = pybamm.Parameter("Lower voltage cut-off [V]")
         self.voltage_high_cut = pybamm.Parameter("Upper voltage cut-off [V]")
-        self.opc_soc_0_dimensional = pybamm.Parameter(
+        self.ocp_soc_0_dimensional = pybamm.Parameter(
             "Open-circuit voltage at 0% SOC [V]"
         )
-        self.opc_soc_100_dimensional = pybamm.Parameter(
+        self.ocp_soc_100_dimensional = pybamm.Parameter(
             "Open-circuit voltage at 100% SOC [V]"
         )
         # Current as a function of time
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index a8558d55dc..05ce9b8084 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -13,18 +13,8 @@ class LithiumIonParameters(BaseParameters):
     ----------
 
     options : dict, optional
-        A dictionary of options to be passed to the parameters. The options that
-        can be set are listed below.
-
-            * "particle shape" : str, optional
-                Sets the model shape of the electrode particles. This is used to
-                calculate the surface area to volume ratio. Can be "spherical"
-                (default). TODO: implement "cylindrical" and "platelet".
-            * "working electrode": str
-                Which electrode(s) intercalates and which is counter. If "both"
-                (default), the model is a standard battery. Otherwise can be "negative"
-                or "positive" to indicate a half-cell model.
-
+        A dictionary of options to be passed to the parameters, see
+        :class:`pybamm.BatteryModelOptions`.
     """
 
     def __init__(self, options=None):
@@ -84,8 +74,8 @@ def _set_parameters(self):
         self.n_cells = self.elec.n_cells
         self.voltage_low_cut = self.elec.voltage_low_cut
         self.voltage_high_cut = self.elec.voltage_high_cut
-        self.opc_soc_0_dimensional = self.elec.opc_soc_0_dimensional
-        self.opc_soc_100_dimensional = self.elec.opc_soc_100_dimensional
+        self.ocp_soc_0_dimensional = self.elec.ocp_soc_0_dimensional
+        self.ocp_soc_100_dimensional = self.elec.ocp_soc_100_dimensional
 
         # Domain parameters
         for domain in self.domain_params.values():
@@ -330,7 +320,7 @@ def _set_parameters(self):
             f"{Domain} electrode reaction-driven LAM factor [m3.mol-1]"
         )
 
-        # utilisation parameters
+        # Utilisation parameters
         self.u_init = pybamm.Parameter(
             f"Initial {domain} electrode interface utilisation"
         )
@@ -386,6 +376,7 @@ def __init__(self, phase, domain_param):
             self.geo = domain_param.geo.prim
         elif self.phase == "secondary":
             self.geo = domain_param.geo.sec
+        self.options = getattr(self.main_param.options, self.domain)
 
     def _set_parameters(self):
         main = self.main_param
@@ -469,6 +460,8 @@ def _set_parameters(self):
             self.U_init = pybamm.Scalar(0)
             return
 
+        # Spatial variables for parameters that depend on position within the cell
+        # and/or particle
         x = pybamm.SpatialVariable(
             f"x_{domain[0]}",
             domain=[f"{domain} electrode"],
@@ -485,53 +478,59 @@ def _set_parameters(self):
             coord_sys="spherical polar",
         )
 
-        # Macroscale geometry
+        # Microscale geometry
         # Note: the surface area to volume ratio is defined later with the function
         # parameters. The particle size as a function of through-cell position is
         # already defined in geometric_parameters.py
         self.R = self.geo.R
         self.R_typ = self.geo.R_typ
-
-        # Particle properties
-        self.c_max = pybamm.Parameter(
-            f"{pref}Maximum concentration in {domain} electrode [mol.m-3]"
-        )
-
         # Particle-size distribution parameters
         self.R_min = self.geo.R_min
         self.R_max = self.geo.R_max
         self.f_a_dist = self.geo.f_a_dist
 
+        # Particle properties
         self.epsilon_s = pybamm.FunctionParameter(
             f"{pref}{Domain} electrode active material volume fraction",
             {"Through-cell distance (x) [m]": x},
         )
-        self.c_init = pybamm.FunctionParameter(
-            f"{pref}Initial concentration in {domain} electrode [mol.m-3]",
-            {
-                "Radial distance (r) [m]": r,
-                "Through-cell distance (x) [m]": pybamm.PrimaryBroadcast(
-                    x, f"{domain} {phase_name}particle"
-                ),
-            },
+        self.epsilon_s_av = pybamm.xyz_average(self.epsilon_s)
+        self.c_max = pybamm.Parameter(
+            f"{pref}Maximum concentration in {domain} electrode [mol.m-3]"
         )
+        if self.options["open-circuit potential"] == "MSMR":
+            self.U_init = pybamm.Parameter(
+                f"{pref}Initial voltage in {domain} electrode [V]",
+            )
+            self.c_init = self.x(self.U_init) * self.c_max
+        else:
+            self.c_init = pybamm.FunctionParameter(
+                f"{pref}Initial concentration in {domain} electrode [mol.m-3]",
+                {
+                    "Radial distance (r) [m]": r,
+                    "Through-cell distance (x) [m]": pybamm.PrimaryBroadcast(
+                        x, f"{domain} {phase_name}particle"
+                    ),
+                },
+            )
         self.c_init_av = pybamm.xyz_average(pybamm.r_average(self.c_init))
         self.sto_init_av = self.c_init_av / self.c_max
         eps_c_init_av = pybamm.xyz_average(
             self.epsilon_s * pybamm.r_average(self.c_init)
         )
-        self.n_Li_init = eps_c_init_av * self.domain_param.L * main.A_cc
-        self.Q_Li_init = self.n_Li_init * main.F / 3600
 
-        self.epsilon_s_av = pybamm.xyz_average(self.epsilon_s)
+        if self.options["open-circuit potential"] != "MSMR":
+            self.U_init = self.U(self.sto_init_av, main.T_init)
+
+        # Electrode loading and capacity
         self.elec_loading = (
             self.epsilon_s_av * self.domain_param.L * self.c_max * main.F / 3600
         )
+        self.n_Li_init = eps_c_init_av * self.domain_param.L * main.A_cc
+        self.Q_Li_init = self.n_Li_init * main.F / 3600
         self.Q_init = self.elec_loading * main.A_cc
 
-        self.U_init = self.U(self.sto_init_av, main.T_init)
-
-        if main.options["particle shape"] == "spherical":
+        if self.options["particle shape"] == "spherical":
             self.a_typ = 3 * pybamm.xyz_average(self.epsilon_s) / self.R_typ
 
     def D(self, c_s, T, lithiation=None):
@@ -629,6 +628,114 @@ def dUdT(self, sto):
             inputs,
         )
 
+    def X_j(self, index):
+        "Available host sites indexed by reaction j"
+        domain = self.domain
+        d = domain[0]
+        Xj = pybamm.Parameter(f"X_{d}_{index}")
+        return Xj
+
+    def U0_j(self, index):
+        "Equilibrium potential indexed by reaction j"
+        domain = self.domain
+        d = domain[0]
+        U0j = pybamm.Parameter(f"U0_{d}_{index}")
+        return U0j
+
+    def w_j(self, index):
+        "Order parameter indexed by reaction j"
+        domain = self.domain
+        d = domain[0]
+        wj = pybamm.Parameter(f"w_{d}_{index}")
+        return wj
+
+    def alpha_bv_j(self, index):
+        "Dimensional Butler-Volmer exchange-current density indexed by reaction j"
+        domain = self.domain
+        d = domain[0]
+        alpha_bv_j = pybamm.Parameter(f"a_{d}_{index}")
+        return alpha_bv_j
+
+    def x_j(self, U, index):
+        "Fractional occupancy of site j as a function of potential"
+        T = self.main_param.T_ref
+        f = self.main_param.F / (self.main_param.R * T)
+        U0j = self.U0_j(index)
+        wj = self.w_j(index)
+        Xj = self.X_j(index)
+        # Equation 5, Baker et al 2018
+        xj = Xj / (1 + pybamm.exp(f * (U - U0j) / wj))
+        return xj
+
+    def dxdU_j(self, U, index):
+        "Derivative of fractional occupancy of site j as a function of potential [V-1]"
+        T = self.main_param.T_ref
+        f = self.main_param.F / (self.main_param.R * T)
+        U0j = self.U0_j(index)
+        wj = self.w_j(index)
+        Xj = self.X_j(index)
+        e = pybamm.exp(f * (U - U0j) / wj)
+        # Equation 25, Baker et al 2018
+        dxjdU = -(f / wj) * (Xj * e) / (1 + e) ** 2
+        return dxjdU
+
+    def j0_j(self, c_e, U, T, index):
+        "Exchange-current density index by reaction j [A.m-2]"
+        domain = self.domain
+        d = domain[0]
+
+        tol = pybamm.settings.tolerances["j0__c_e"]
+        c_e = pybamm.maximum(c_e, tol)
+        c_e_ref = self.main_param.c_e_init
+        xj = self.x_j(U, index)
+        # xj = pybamm.maximum(pybamm.minimum(xj, (1 - tol)), tol)
+
+        f = self.main_param.F / (self.main_param.R * T)
+        wj = self.w_j(index)
+        self.X_j(index)
+        aj = self.alpha_bv_j(index)
+        j0_ref_j = pybamm.FunctionParameter(
+            f"j0_ref_{d}_{index}", {"Temperature [K]": T}
+        )
+
+        # Equation 16, Baker et al 2018. The original formulation would be implemented
+        # as:
+        # j0_j = (
+        #    j0_ref_j
+        #    * xj ** (wj * aj)
+        #    * (Xj - xj) ** (wj * (1 - aj))
+        #    * (c_e / c_e_ref) ** (1 - aj)
+        # )
+        # However, we reformulate in terms of potential to avoid singularity as x_j
+        # approaches X_j
+        j0_j = (
+            j0_ref_j
+            * xj**wj
+            * pybamm.exp(f * (1 - aj) * (U - self.U0_j(index)))
+            * (c_e / c_e_ref) ** (1 - aj)
+        )
+        return j0_j
+
+    def x(self, U):
+        "Stoichiometry as a function of potential (for use with MSMR models)"
+        N = int(self.options["number of MSMR reactions"])
+        # Equation 6, Baker et al 2018
+        x = 0
+        for i in range(N):
+            x += self.x_j(U, i)
+        return x
+
+    def dxdU(self, U):
+        """
+        Differential stoichiometry as a function of potential (for use with MSMR models)
+        """
+        N = int(self.options["number of MSMR reactions"])
+        # Equation 25, Baker et al 2018
+        dxdU = 0
+        for i in range(N):
+            dxdU += self.dxdU_j(U, i)
+        return dxdU
+
     def t_change(self, sto):
         """
         Volume change for the electrode; sto should be R-averaged
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index c78cb70c91..136d9737aa 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -259,14 +259,15 @@ def set_initial_stoichiometries(
         param=None,
         known_value="cyclable lithium capacity",
         inplace=True,
+        options=None,
     ):
         """
         Set the initial stoichiometry of each electrode, based on the initial
         SOC or voltage
         """
-        param = param or pybamm.LithiumIonParameters()
+        param = param or pybamm.LithiumIonParameters(options)
         x, y = pybamm.lithium_ion.get_initial_stoichiometries(
-            initial_value, self, param=param, known_value=known_value
+            initial_value, self, param=param, known_value=known_value, options=options
         )
         if inplace:
             parameter_values = self
@@ -282,6 +283,34 @@ def set_initial_stoichiometries(
         )
         return parameter_values
 
+    def set_initial_ocps(
+        self,
+        initial_value,
+        param=None,
+        known_value="cyclable lithium capacity",
+        inplace=True,
+        options=None,
+    ):
+        """
+        Set the initial OCP of each electrode, based on the initial
+        SOC or voltage
+        """
+        param = param or pybamm.LithiumIonParameters(options)
+        Un, Up = pybamm.lithium_ion.get_initial_ocps(
+            initial_value, self, param=param, known_value=known_value, options=options
+        )
+        if inplace:
+            parameter_values = self
+        else:
+            parameter_values = self.copy()
+        parameter_values.update(
+            {
+                "Initial voltage in negative electrode [V]": Un,
+                "Initial voltage in positive electrode [V]": Up,
+            }
+        )
+        return parameter_values
+
     def check_parameter_values(self, values):
         for param in values:
             if "propotional term" in param:
diff --git a/pybamm/simulation.py b/pybamm/simulation.py
index 52c1922545..b72322e835 100644
--- a/pybamm/simulation.py
+++ b/pybamm/simulation.py
@@ -371,12 +371,19 @@ def set_initial_soc(self, initial_soc):
             self.op_conds_to_built_models = None
             self.op_conds_to_built_solvers = None
 
+        options = self.model.options
         param = self.model.param
-        self.parameter_values = (
-            self._unprocessed_parameter_values.set_initial_stoichiometries(
-                initial_soc, param=param, inplace=False
+        if options["open-circuit potential"] == "MSMR":
+            self.parameter_values = self._unprocessed_parameter_values.set_initial_ocps(
+                initial_soc, param=param, inplace=False, options=options
             )
-        )
+        else:
+            self.parameter_values = (
+                self._unprocessed_parameter_values.set_initial_stoichiometries(
+                    initial_soc, param=param, inplace=False, options=options
+                )
+            )
+
         # Save solved initial SOC in case we need to re-build the model
         self._built_initial_soc = initial_soc
 
@@ -948,7 +955,7 @@ def _get_esoh_solver(self, calc_esoh):
             return None
 
         return pybamm.lithium_ion.ElectrodeSOHSolver(
-            self.parameter_values, self.model.param
+            self.parameter_values, self.model.param, options=self.model.options
         )
 
     def plot(self, output_variables=None, **kwargs):
diff --git a/setup.py b/setup.py
index dfdd455a16..df55a24325 100644
--- a/setup.py
+++ b/setup.py
@@ -300,6 +300,7 @@ def compile_KLU():
             "Ramadass2004 = pybamm.input.parameters.lithium_ion.Ramadass2004:get_parameter_values",  # noqa: E501
             "Xu2019 = pybamm.input.parameters.lithium_ion.Xu2019:get_parameter_values",  # noqa: E501
             "ECM_Example = pybamm.input.parameters.ecm.example_set:get_parameter_values",  # noqa: E501
+            "MSMR_Example = pybamm.input.parameters.lithium_ion.MSMR_example_set:get_parameter_values",  # noqa: E501
         ],
     },
 )
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 01fb8b8d4d..6c787cea0b 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -308,3 +308,15 @@ def test_composite_graphite_silicon_sei(self):
             {f"Primary: {name}": (1 - x) * 0.75, f"Secondary: {name}": x * 0.75}
         )
         self.run_basic_processing_test(options, parameter_values=parameter_values)
+
+    def test_basic_processing_msmr(self):
+        options = {
+            "open-circuit potential": "MSMR",
+            "particle": "MSMR",
+            "intercalation kinetics": "MSMR",
+            "number of MSMR reactions": ("6", "4"),
+        }
+        parameter_values = pybamm.ParameterValues("MSMR_Example")
+        model = self.model(options)
+        modeltest = tests.StandardModelTest(model, parameter_values=parameter_values)
+        modeltest.test_all(skip_output_tests=True)
diff --git a/tests/unit/test_citations.py b/tests/unit/test_citations.py
index bfa5820382..5fde193af3 100644
--- a/tests/unit/test_citations.py
+++ b/tests/unit/test_citations.py
@@ -338,6 +338,18 @@ def test_sripad_2020(self):
         self.assertIn("Sripad2020", citations._papers_to_cite)
         self.assertIn("Sripad2020", citations._citation_tags.keys())
 
+    def test_msmr(self):
+        citations = pybamm.citations
+
+        citations._reset()
+        self.assertNotIn("Baker2018", citations._papers_to_cite)
+        self.assertNotIn("Verbrugge2017", citations._papers_to_cite)
+        pybamm.particle.MSMRDiffusion(None, "negative", None, None, None)
+        self.assertIn("Baker2018", citations._papers_to_cite)
+        self.assertIn("Baker2018", citations._citation_tags.keys())
+        self.assertIn("Verbrugge2017", citations._papers_to_cite)
+        self.assertIn("Verbrugge2017", citations._citation_tags.keys())
+
     def test_parameter_citations(self):
         citations = pybamm.citations
 
@@ -379,6 +391,13 @@ def test_parameter_citations(self):
         self.assertIn("ORegan2022", citations._papers_to_cite)
         self.assertIn("ORegan2022", citations._citation_tags.keys())
 
+        citations._reset()
+        pybamm.ParameterValues("MSMR_Example")
+        self.assertIn("Baker2018", citations._papers_to_cite)
+        self.assertIn("Baker2018", citations._citation_tags.keys())
+        self.assertIn("Verbrugge2017", citations._papers_to_cite)
+        self.assertIn("Verbrugge2017", citations._citation_tags.keys())
+
     def test_solver_citations(self):
         # Test that solving each solver adds the right citations
         citations = pybamm.citations
diff --git a/tests/unit/test_expression_tree/test_broadcasts.py b/tests/unit/test_expression_tree/test_broadcasts.py
index f9500a6f90..81d1210229 100644
--- a/tests/unit/test_expression_tree/test_broadcasts.py
+++ b/tests/unit/test_expression_tree/test_broadcasts.py
@@ -332,6 +332,24 @@ def test_to_equation(self):
         a = pybamm.PrimaryBroadcast(0, "test").to_equation()
         self.assertEqual(a, 0)
 
+    def test_diff(self):
+        a = pybamm.StateVector(slice(0, 1))
+        b = pybamm.PrimaryBroadcast(a, "separator")
+        y = np.array([5])
+        # diff of broadcast is broadcast of diff
+        d = b.diff(a)
+        self.assertIsInstance(d, pybamm.PrimaryBroadcast)
+        self.assertEqual(d.child.evaluate(y=y), 1)
+        # diff of broadcast w.r.t. itself is 1
+        d = b.diff(b)
+        self.assertIsInstance(d, pybamm.Scalar)
+        self.assertEqual(d.evaluate(y=y), 1)
+        # diff of broadcast of a constant is 0
+        c = pybamm.PrimaryBroadcast(pybamm.Scalar(4), "separator")
+        d = c.diff(a)
+        self.assertIsInstance(d, pybamm.Scalar)
+        self.assertEqual(d.evaluate(y=y), 0)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
index 28277af9e2..60eed9d6fb 100644
--- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
+++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py
@@ -26,14 +26,15 @@
 'electrolyte conductivity': 'default' (possible: ['default', 'full', 'leading order', 'composite', 'integrated'])
 'exchange-current density': 'single' (possible: ['single', 'current sigmoid'])
 'hydrolysis': 'false' (possible: ['false', 'true'])
-'intercalation kinetics': 'symmetric Butler-Volmer' (possible: ['symmetric Butler-Volmer', 'asymmetric Butler-Volmer', 'linear', 'Marcus', 'Marcus-Hush-Chidsey'])
+'intercalation kinetics': 'symmetric Butler-Volmer' (possible: ['symmetric Butler-Volmer', 'asymmetric Butler-Volmer', 'linear', 'Marcus', 'Marcus-Hush-Chidsey', 'MSMR'])
 'interface utilisation': 'full' (possible: ['full', 'constant', 'current-driven'])
 'lithium plating': 'none' (possible: ['none', 'reversible', 'partially reversible', 'irreversible'])
 'lithium plating porosity change': 'false' (possible: ['false', 'true'])
 'loss of active material': 'stress-driven' (possible: ['none', 'stress-driven', 'reaction-driven', 'current-driven', 'stress and reaction-driven'])
-'open-circuit potential': 'single' (possible: ['single', 'current sigmoid'])
+'number of MSMR reactions': 'none' (possible: ['none'])
+'open-circuit potential': 'single' (possible: ['single', 'current sigmoid', 'MSMR'])
 'operating mode': 'current' (possible: ['current', 'voltage', 'power', 'differential power', 'explicit power', 'resistance', 'differential resistance', 'explicit resistance', 'CCCV'])
-'particle': 'Fickian diffusion' (possible: ['Fickian diffusion', 'fast diffusion', 'uniform profile', 'quadratic profile', 'quartic profile'])
+'particle': 'Fickian diffusion' (possible: ['Fickian diffusion', 'fast diffusion', 'uniform profile', 'quadratic profile', 'quartic profile', 'MSMR'])
 'particle mechanics': 'swelling only' (possible: ['none', 'swelling only', 'swelling and cracking'])
 'particle phases': '1' (possible: ['1', '2'])
 'particle shape': 'spherical' (possible: ['spherical', 'no particles'])
@@ -368,6 +369,35 @@ def test_options(self):
         with self.assertRaisesRegex(pybamm.OptionError, "multiple particle phases"):
             pybamm.BaseBatteryModel({"particle phases": "2", "surface form": "false"})
 
+        # msmr
+        with self.assertRaisesRegex(pybamm.OptionError, "MSMR"):
+            pybamm.BaseBatteryModel({"open-circuit potential": "MSMR"})
+        with self.assertRaisesRegex(pybamm.OptionError, "MSMR"):
+            pybamm.BaseBatteryModel({"particle": "MSMR"})
+        with self.assertRaisesRegex(pybamm.OptionError, "MSMR"):
+            pybamm.BaseBatteryModel({"intercalation kinetics": "MSMR"})
+        with self.assertRaisesRegex(pybamm.OptionError, "MSMR"):
+            pybamm.BaseBatteryModel(
+                {"open-circuit potential": "MSMR", "particle": "MSMR"}
+            )
+        with self.assertRaisesRegex(pybamm.OptionError, "MSMR"):
+            pybamm.BaseBatteryModel(
+                {"open-circuit potential": "MSMR", "intercalation kinetics": "MSMR"}
+            )
+        with self.assertRaisesRegex(pybamm.OptionError, "MSMR"):
+            pybamm.BaseBatteryModel(
+                {"particle": "MSMR", "intercalation kinetics": "MSMR"}
+            )
+        with self.assertRaisesRegex(pybamm.OptionError, "MSMR"):
+            pybamm.BaseBatteryModel(
+                {
+                    "open-circuit potential": "MSMR",
+                    "particle": "MSMR",
+                    "intercalation kinetics": "MSMR",
+                    "number of MSMR reactions": "1.5",
+                }
+            )
+
     def test_build_twice(self):
         model = pybamm.lithium_ion.SPM()  # need to pick a model to set vars and build
         with self.assertRaisesRegex(pybamm.ModelError, "Model already built"):
@@ -416,6 +446,7 @@ def test_print_options(self):
         with io.StringIO() as buffer, redirect_stdout(buffer):
             BatteryModelOptions(OPTIONS_DICT).print_options()
             output = buffer.getvalue()
+
         self.assertEqual(output, PRINT_OPTIONS_OUTPUT)
 
     def test_option_phases(self):
diff --git a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py b/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py
index aa62179e05..ec280cdd1f 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lead_acid/test_base_lead_acid_model.py
@@ -27,6 +27,13 @@ def test_incompatible_options(self):
             pybamm.lead_acid.BaseModel({"SEI": "constant"})
         with self.assertRaisesRegex(pybamm.OptionError, "lithium plating"):
             pybamm.lead_acid.BaseModel({"lithium plating": "reversible"})
+        with self.assertRaisesRegex(pybamm.OptionError, "MSMR"):
+            pybamm.lead_acid.BaseModel(
+                {
+                    "open-circuit potential": "MSMR",
+                    "particle": "MSMR",
+                }
+            )
 
 
 if __name__ == "__main__":
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
index 5cc0e72f1d..6815698588 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py
@@ -368,6 +368,16 @@ def test_well_posed_current_sigmoid_ocp(self):
         options = {"open-circuit potential": "current sigmoid"}
         self.check_well_posedness(options)
 
+    def test_well_posed_msmr(self):
+        options = {
+            "open-circuit potential": "MSMR",
+            "particle": "MSMR",
+            "number of MSMR reactions": ("6", "4"),
+            "intercalation kinetics": "MSMR",
+            "surface form": "differential",
+        }
+        self.check_well_posedness(options)
+
     def test_well_posed_current_sigmoid_exchange_current(self):
         options = {"exchange-current density": "current sigmoid"}
         self.check_well_posedness(options)
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
index 51a9b88d69..d7e95247e0 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
@@ -43,6 +43,16 @@ def test_well_posed_external_circuit_explicit_resistance(self):
         options = {"operating mode": "explicit resistance"}
         self.check_well_posedness(options)
 
+    def test_well_posed_msmr_with_psd(self):
+        options = {
+            "open-circuit potential": "MSMR",
+            "particle": "MSMR",
+            "particle size": "distribution",
+            "number of MSMR reactions": ("6", "4"),
+            "intercalation kinetics": "MSMR",
+        }
+        self.check_well_posedness(options)
+
 
 if __name__ == "__main__":
     print("Add -v for more debug output")
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
index b1c3209096..c305b21fee 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py
@@ -156,6 +156,92 @@ def test_error(self):
             esoh_solver.solve(inputs)
 
 
+class TestElectrodeSOHMSMR(TestCase):
+    def test_known_solution(self):
+        options = {
+            "open-circuit potential": "MSMR",
+            "particle": "MSMR",
+            "number of MSMR reactions": ("6", "4"),
+            "intercalation kinetics": "MSMR",
+        }
+        param = pybamm.LithiumIonParameters(options=options)
+        parameter_values = pybamm.ParameterValues("MSMR_Example")
+
+        esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(
+            parameter_values, param, options=options
+        )
+
+        Vmin = 2.8
+        Vmax = 4.2
+        Q_n = parameter_values.evaluate(param.n.Q_init)
+        Q_p = parameter_values.evaluate(param.p.Q_init)
+        Q_Li = parameter_values.evaluate(param.Q_Li_particles_init)
+
+        inputs = {"Q_Li": Q_Li, "Q_n": Q_n, "Q_p": Q_p}
+
+        # Solve the model and check outputs
+        sol = esoh_solver.solve(inputs)
+
+        self.assertAlmostEqual(sol["Up(y_100) - Un(x_100)"], Vmax, places=5)
+        self.assertAlmostEqual(sol["Up(y_0) - Un(x_0)"], Vmin, places=5)
+        self.assertAlmostEqual(sol["Q_Li"], Q_Li, places=5)
+
+        # Solve with split esoh and check outputs
+        ics = esoh_solver._set_up_solve(inputs)
+        sol_split = esoh_solver._solve_split(inputs, ics)
+        for key in sol:
+            if key != "Maximum theoretical energy [W.h]":
+                self.assertAlmostEqual(sol[key], sol_split[key].data[0], places=5)
+
+        # Check feasibility checks can be performed successfully
+        esoh_solver._check_esoh_feasible(inputs)
+
+    def test_known_solution_cell_capacity(self):
+        options = {
+            "open-circuit potential": "MSMR",
+            "particle": "MSMR",
+            "number of MSMR reactions": ("6", "4"),
+            "intercalation kinetics": "MSMR",
+        }
+        param = pybamm.LithiumIonParameters(options)
+        parameter_values = pybamm.ParameterValues("MSMR_Example")
+
+        esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(
+            parameter_values, param, known_value="cell capacity", options=options
+        )
+
+        Vmin = 2.8
+        Vmax = 4.2
+        Q_n = parameter_values.evaluate(param.n.Q_init)
+        Q_p = parameter_values.evaluate(param.p.Q_init)
+        Q = parameter_values.evaluate(param.Q)
+
+        inputs = {"Q": Q, "Q_n": Q_n, "Q_p": Q_p}
+
+        # Solve the model and check outputs
+        sol = esoh_solver.solve(inputs)
+
+        self.assertAlmostEqual(sol["Up(y_100) - Un(x_100)"], Vmax, places=5)
+        self.assertAlmostEqual(sol["Up(y_0) - Un(x_0)"], Vmin, places=5)
+        self.assertAlmostEqual(sol["Q"], Q, places=5)
+
+    def test_error(self):
+        options = {
+            "open-circuit potential": "MSMR",
+            "particle": "MSMR",
+            "number of MSMR reactions": ("6", "4"),
+            "intercalation kinetics": "MSMR",
+        }
+        param = pybamm.LithiumIonParameters(options)
+        parameter_values = pybamm.ParameterValues("MSMR_Example")
+
+        esoh_solver = pybamm.lithium_ion.ElectrodeSOHSolver(
+            parameter_values, param, known_value="cell capacity", options=options
+        )
+        with self.assertRaisesRegex(ValueError, "solve_for must be "):
+            esoh_solver._get_electrode_soh_sims_split()
+
+
 class TestElectrodeSOHHalfCell(TestCase):
     def test_known_solution(self):
         model = pybamm.lithium_ion.ElectrodeSOHHalfCell("positive")
@@ -187,7 +273,7 @@ def test_efficiency(self):
             )
         )
         # Real energy should be less than discharge energy,
-        #  and both should be greater than 0
+        # and both should be greater than 0
         self.assertLess(discharge_energy, theoretical_energy)
         self.assertLess(0, discharge_energy)
         self.assertLess(0, theoretical_energy)
@@ -237,6 +323,16 @@ def test_min_max_stoich(self):
         V = parameter_values.evaluate(param.p.prim.U(y0, T) - param.n.prim.U(x0, T))
         self.assertAlmostEqual(V, 2.8)
 
+        x0, x100, y100, y0 = pybamm.lithium_ion.get_min_max_stoichiometries(
+            parameter_values,
+            param,
+            known_value="cell capacity",
+        )
+        V = parameter_values.evaluate(param.p.prim.U(y100, T) - param.n.prim.U(x100, T))
+        self.assertAlmostEqual(V, 4.2)
+        V = parameter_values.evaluate(param.p.prim.U(y0, T) - param.n.prim.U(x0, T))
+        self.assertAlmostEqual(V, 2.8)
+
     def test_initial_soc_cell_capacity(self):
         param = pybamm.LithiumIonParameters()
         parameter_values = pybamm.ParameterValues("Mohtat2020")
@@ -263,6 +359,74 @@ def test_error(self):
             pybamm.lithium_ion.get_initial_stoichiometries("5 A", parameter_values)
 
 
+class TestGetInitialOCP(TestCase):
+    def test_get_initial_ocp(self):
+        param = pybamm.LithiumIonParameters()
+        parameter_values = pybamm.ParameterValues("Mohtat2020")
+        Un, Up = pybamm.lithium_ion.get_initial_ocps(1, parameter_values, param)
+        self.assertAlmostEqual(Up - Un, 4.2)
+        Un, Up = pybamm.lithium_ion.get_initial_ocps(0, parameter_values, param)
+        self.assertAlmostEqual(Up - Un, 2.8)
+        Un, Up = pybamm.lithium_ion.get_initial_ocps("4 V", parameter_values, param)
+        self.assertAlmostEqual(Up - Un, 4)
+
+    def test_min_max_ocp(self):
+        param = pybamm.LithiumIonParameters()
+        parameter_values = pybamm.ParameterValues("Mohtat2020")
+
+        Un_0, Un_100, Up_100, Up_0 = pybamm.lithium_ion.get_min_max_ocps(
+            parameter_values, param
+        )
+        self.assertAlmostEqual(Up_100 - Un_100, 4.2)
+        self.assertAlmostEqual(Up_0 - Un_0, 2.8)
+
+
+class TestGetInitialOCPMSMR(TestCase):
+    def test_get_initial_ocp(self):
+        options = {
+            "open-circuit potential": "MSMR",
+            "particle": "MSMR",
+            "number of MSMR reactions": ("6", "4"),
+            "intercalation kinetics": "MSMR",
+        }
+        param = pybamm.LithiumIonParameters(options)
+        parameter_values = pybamm.ParameterValues("MSMR_Example")
+        Un, Up = pybamm.lithium_ion.get_initial_ocps(
+            1, parameter_values, param, options=options
+        )
+        self.assertAlmostEqual(Up - Un, 4.2, places=5)
+        Un, Up = pybamm.lithium_ion.get_initial_ocps(
+            0, parameter_values, param, options=options
+        )
+        self.assertAlmostEqual(Up - Un, 2.8, places=5)
+        Un, Up = pybamm.lithium_ion.get_initial_ocps(
+            "4 V", parameter_values, param, options=options
+        )
+        self.assertAlmostEqual(Up - Un, 4)
+
+    def test_min_max_ocp(self):
+        options = {
+            "open-circuit potential": "MSMR",
+            "particle": "MSMR",
+            "number of MSMR reactions": ("6", "4"),
+            "intercalation kinetics": "MSMR",
+        }
+        param = pybamm.LithiumIonParameters(options)
+        parameter_values = pybamm.ParameterValues("MSMR_Example")
+
+        Un_0, Un_100, Up_100, Up_0 = pybamm.lithium_ion.get_min_max_ocps(
+            parameter_values, param, options=options
+        )
+        self.assertAlmostEqual(Up_100 - Un_100, 4.2)
+        self.assertAlmostEqual(Up_0 - Un_0, 2.8)
+
+        Un_0, Un_100, Up_100, Up_0 = pybamm.lithium_ion.get_min_max_ocps(
+            parameter_values, param, known_value="cell capacity", options=options
+        )
+        self.assertAlmostEqual(Up_100 - Un_100, 4.2)
+        self.assertAlmostEqual(Up_0 - Un_0, 2.8)
+
+
 if __name__ == "__main__":
     print("Add -v for more debug output")
     import sys
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py
index 7dac0694a5..442817e354 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_mpm.py
@@ -108,6 +108,16 @@ def test_stress_induced_diffusion_not_implemented(self):
         with self.assertRaises(NotImplementedError):
             pybamm.lithium_ion.MPM(options)
 
+    def test_msmr(self):
+        options = {
+            "open-circuit potential": "MSMR",
+            "particle": "MSMR",
+            "number of MSMR reactions": ("6", "4"),
+            "intercalation kinetics": "MSMR",
+        }
+        model = pybamm.lithium_ion.MPM(options)
+        model.check_well_posedness()
+
 
 class TestMPMExternalCircuits(TestCase):
     def test_well_posed_voltage(self):
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_msmr.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_msmr.py
new file mode 100644
index 0000000000..96369fbac2
--- /dev/null
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_msmr.py
@@ -0,0 +1,22 @@
+#
+# Tests for the lithium-ion MSMR model
+#
+from tests import TestCase
+import pybamm
+import unittest
+
+
+class TestMSMR(TestCase):
+    def test_well_posed(self):
+        model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")})
+        model.check_well_posedness()
+
+
+if __name__ == "__main__":
+    print("Add -v for more debug output")
+    import sys
+
+    if "-v" in sys.argv:
+        debug = True
+    pybamm.settings.debug_mode = True
+    unittest.main()
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index def4c339f3..c6a4831e86 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -119,6 +119,25 @@ def test_set_initial_stoichiometries(self):
         y_100 = param_100["Initial concentration in positive electrode [mol.m-3]"]
         self.assertAlmostEqual(y, y_0 - 0.4 * (y_0 - y_100))
 
+    def test_set_initial_ocps(self):
+        options = {
+            "open-circuit potential": "MSMR",
+            "particle": "MSMR",
+            "number of MSMR reactions": ("6", "4"),
+            "intercalation kinetics": "MSMR",
+        }
+        param_100 = pybamm.ParameterValues("MSMR_Example")
+        param_100.set_initial_ocps(1, inplace=True, options=options)
+        param_0 = param_100.set_initial_ocps(0, inplace=False, options=options)
+
+        Un_0 = param_0["Initial voltage in negative electrode [V]"]
+        Up_0 = param_0["Initial voltage in positive electrode [V]"]
+        self.assertAlmostEqual(Up_0 - Un_0, 2.8)
+
+        Un_100 = param_100["Initial voltage in negative electrode [V]"]
+        Up_100 = param_100["Initial voltage in positive electrode [V]"]
+        self.assertAlmostEqual(Up_100 - Un_100, 4.2)
+
     def test_check_parameter_values(self):
         with self.assertRaisesRegex(ValueError, "propotional term"):
             pybamm.ParameterValues(
diff --git a/tests/unit/test_simulation.py b/tests/unit/test_simulation.py
index 83ec42ef6c..d0926e5c94 100644
--- a/tests/unit/test_simulation.py
+++ b/tests/unit/test_simulation.py
@@ -203,6 +203,13 @@ def test_solve_with_initial_soc(self):
         sim.build(initial_soc=0.5)
         self.assertEqual(sim._built_initial_soc, 0.5)
 
+        # test with MSMR
+        model = pybamm.lithium_ion.MSMR({"number of MSMR reactions": ("6", "4")})
+        param = pybamm.ParameterValues("MSMR_Example")
+        sim = pybamm.Simulation(model, parameter_values=param)
+        sim.build(initial_soc=0.5)
+        self.assertEqual(sim._built_initial_soc, 0.5)
+
     def test_solve_with_inputs(self):
         model = pybamm.lithium_ion.SPM()
         param = model.default_parameter_values