diff --git a/CHANGELOG.md b/CHANGELOG.md
index ef861062d7..42ed912528 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -2,6 +2,7 @@
 
 ## Features
 
+-   Stress-induced diffusion is now a separate model option instead of being automatically included when using the particle mechanics submodels ([#1797](https://github.com/pybamm-team/PyBaMM/pull/1797))
 -   `Experiment`s with drive cycles can be solved ([#1793](https://github.com/pybamm-team/PyBaMM/pull/1793))
 -   Added surface area to volume ratio as a factor to the SEI equations ([#1790](https://github.com/pybamm-team/PyBaMM/pull/1790))
 -   Half-cell SPM and SPMe have been implemented ([#1731](https://github.com/pybamm-team/PyBaMM/pull/1731))
diff --git a/docs/source/models/submodels/particle/no_distribution/base_fickian.rst b/docs/source/models/submodels/particle/no_distribution/base_fickian.rst
new file mode 100644
index 0000000000..de07955e8f
--- /dev/null
+++ b/docs/source/models/submodels/particle/no_distribution/base_fickian.rst
@@ -0,0 +1,5 @@
+Base Fickian Diffusion
+======================
+
+.. autoclass:: pybamm.particle.no_distribution.BaseFickian
+    :members:
diff --git a/docs/source/models/submodels/particle/no_distribution/index.rst b/docs/source/models/submodels/particle/no_distribution/index.rst
index 732d55f2dc..8f40e0cfa2 100644
--- a/docs/source/models/submodels/particle/no_distribution/index.rst
+++ b/docs/source/models/submodels/particle/no_distribution/index.rst
@@ -4,6 +4,7 @@ No Particle-Size Distribution
 .. toctree::
   :maxdepth: 1
 
+  base_fickian
   fickian_diffusion
   x_averaged_fickian_diffusion
   polynomial_profile
diff --git a/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb b/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb
index 298561281b..472f4aef2d 100644
--- a/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb
+++ b/examples/notebooks/models/Validating_mechanical_models_Enertech_DFN.ipynb
@@ -17,8 +17,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1.1 is available.\n",
-      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -36,7 +34,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When you load a model in PyBaMM it builds by default. Building the model sets all of the model variables and sets up any variables which are coupled between different submodels: this is the process which couples the submodels together and allows one submodel to access variables from another. If you would like to swap out a submodel in an exisitng battery model you need to load it without building it by passing the keyword `build=False`"
+    "Let's load the DFN and include particle swelling"
    ]
   },
   {
@@ -78,39 +76,41 @@
     }
    ],
    "source": [
+    "# update parameters, making C-rate and input\n",
     "chemistry = pybamm.parameter_sets.Ai2020\n",
     "param = pybamm.ParameterValues(chemistry=chemistry)\n",
     "capacity = param[\"Nominal cell capacity [A.h]\"]\n",
     "param.update({\n",
     "    \"Current function [A]\": capacity * pybamm.InputParameter(\"C-rate\")\n",
     "})\n",
-    "# experiment05C = pybamm.Experiment([\"Discharge at 0.5C until 3 V\"])\n",
-    "# experiment1C = pybamm.Experiment([\"Discharge at 1C until 3 V\"])\n",
-    "# experiment2C = pybamm.Experiment([\"Discharge at 2C until 3 V\"])\n",
+    "\n",
+    "# update the mesh\n",
     "var = pybamm.standard_spatial_vars\n",
     "var_pts = {\n",
     "  var.x_n: 50,\n",
     "  var.x_s: 50,\n",
     "  var.x_p: 50,\n",
-    "  var.r_n: 20,\n",
-    "  var.r_p: 20,\n",
+    "  var.r_n: 21,\n",
+    "  var.r_p: 21,\n",
     "}\n",
     "\n",
+    "# define the simulation\n",
     "sim = pybamm.Simulation(\n",
     "        model,\n",
     "        var_pts=var_pts,\n",
     "        parameter_values=param,\n",
-    "        solver=pybamm.CasadiSolver(dt_max=600)\n",
+    "        solver=pybamm.CasadiSolver(mode=\"fast\")\n",
     "    )\n",
+    "\n",
+    "# solve for different C-rates\n",
     "Crates = [0.5, 1, 2]\n",
     "solutions = []\n",
-    "\n",
     "for Crate in Crates:\n",
     "    print(f\"{Crate} C\")\n",
     "    sol = sim.solve(t_eval=[0, 3600/Crate*1.05], inputs={\"C-rate\": Crate})\n",
     "    solutions.append(sol)\n",
     "\n",
-    "\n",
+    "# unpack solutions\n",
     "solution05C, solution1C, solution2C = solutions"
    ]
   },
@@ -157,7 +157,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1008x288 with 3 Axes>"
       ]
@@ -183,7 +183,9 @@
     "V_n05C = solution05C[\"Terminal voltage [V]\"].entries\n",
     "T_n05C = solution05C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
     "L_x05C = solution05C[\"Cell thickness change [m]\"].entries\n",
+    "\n",
     "f, (ax1, ax2,ax3) = plt.subplots(1, 3 ,figsize=(14,4))\n",
+    "\n",
     "ax1.plot(t_all2C, V_n2C,'r-',label=\"Simulation\")\n",
     "ax1.plot(data_V_2C.values[::30,0]/3600, data_V_2C.values[::30,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
     "ax1.plot(t_all05C, V_n05C,'g-')\n",
@@ -191,7 +193,6 @@
     "ax1.plot(t_all1C, V_n1C,'b-')\n",
     "ax1.plot(data_V_1C.values[::50,0]/3600, data_V_1C.values[::50,1],'bo',markerfacecolor='none')\n",
     "ax1.legend()\n",
-    "#plt.xlim(0, 3600);\n",
     "ax1.set_xlabel(\"Time [h]\")\n",
     "ax1.set_ylabel(\"Terminal voltage [V]\")\n",
     "ax1.text(0.1, 3.2, r'2 C', {'color': 'r', 'fontsize': 14})\n",
@@ -207,7 +208,6 @@
     "ax2.legend()\n",
     "ax2.set_xlabel(\"Time [h]\")\n",
     "ax2.set_ylabel(\"Temperature rise [K]\")\n",
-    "#plt.xlim(0, 3600);\n",
     "ax2.text(0.5, 8, r'2 C', {'color': 'r', 'fontsize': 14})\n",
     "ax2.text(0.8, 4.4, r'1 C', {'color': 'b', 'fontsize': 14})\n",
     "ax2.text(1.5, 2, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
@@ -224,7 +224,7 @@
     "ax3.text(0.1, -0.0001, r'2 C', {'color': 'r', 'fontsize': 14})\n",
     "ax3.text(0.9, -0.0001, r'1 C', {'color': 'b', 'fontsize': 14})\n",
     "ax3.text(1.8, -0.0001, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
-    "#plt.xlim(0, 3600);\n",
+    "\n",
     "f.tight_layout()\n",
     "f.show()\n"
    ]
@@ -243,7 +243,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAD0CAYAAACGjNCJAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABx3UlEQVR4nO2deXgV5dXAfwfCIooboCBBgwICEUVJXVpbQUTQWhCXSkBFRdEqaKuCIn5SrQhqXVGrKChaCaKtgCsuwYW64s6moKKAihBX0LCe748zQ27CTXJvcpfc5PyeZ547884775zJzX3nzJmziKriOI7jOI7jOI5RL90COI7jOI7jOE5NwhVkx3Ecx3Ecx4nAFWTHcRzHcRzHicAVZMdxHMdxHMeJwBVkx3Ecx3Ecx4nAFWTHcRzHcRzHiSDlCrKI9BGRj0VkqYhcHmV/IxF5JNj/pojkROwbFbR/LCK9I9oni8i3IjI/ynjDRWSxiCwQkRuSdmGO4ziO4zhOrSClCrKI1AfuBI4BOgP5ItK5TLchwPeq2g64Bbg+OLYzMADIBfoAdwXjATwQtJU9Xw+gH3CAquYC/0z0NTmO4ziO4zi1i6wUn+9gYKmqfgYgItMwBXZhRJ9+wN+D9ceAO0REgvZpqroe+FxElgbjva6qr0RamiP4CzA+OAZV/bYyAZs3b645OdGGchzHqR7vvPPOGlVtkW45Mgmfkx3HSSblzcupVpBbA8sjtlcAh5TXR1U3iciPQLOg/Y0yx7au5HwdgN+LyFigGLhUVd8u20lEhgJDAfbcc0/mzZsX8wU5juPEioh8kW4ZMo2cnByfkx3HSRrlzcu1PUgvC9gVOBQYAUwPrNGlUNWJqpqnqnktWrhxx3Ecx3Ecpy6TagV5JdAmYjs7aIvaR0SygJ2AohiPLcsK4L9qvAVsAZpXWXrHcRzHcRyn1pNqBfltoL2ItBWRhljQ3awyfWYBg4P1k4BCVdWgfUCQ5aIt0B54q5LzzQB6AIhIB6AhsCYRF+I4juM4juPUTlLqgxz4FA8DZgP1gcmqukBErgHmqeosYBLwUBCE9x2mRBP0m44F9G0CLlDVzQAiUgB0B5qLyApgjKpOAiYDk4P0bxuAwYGy7Ti1ho0bN7JixQqKi4vTLYoT0LhxY7Kzs2nQoEG6RXEcx3GqgLi+WJq8vDz1gBAnk/j8889p2rQpzZo1I4qLvZNiVJWioiJ+/vln2rZtW2qfiLyjqnlpEi0j8TnZcZxkUt68XNuD9DKHn36Cr74Cf2Bx4qS4uNiV4xqEiNCsWTO36Gc6v/4K778Pb70FKysLd3Ecp7bhCnK6+e47GDAAdtoJWreGdu3ArSVOnLhyXLOoid9HkqqYRh1TRCaJyAci8qGIPCYiOwTtZ4jIahF5P1jOTvJlx8/rr8Ntt0GnTnDggXDIIZCdbZ8PPACbN6dbQsdxUoAryOnkl1/gtNPgkUdsu1Ej+OwzOPZY+Oab9MrmOHHwzTffMGDAAPbZZx+6devGscceyyeffJKy87/00ku89tprKTtfppGMKqaVjPk3VT1AVfcHvgSGRZznEVXtGiz3JeN6q4QqjB8Pv/0t/PWvsOOOMHEiPPYYXHMNrFsHZ54Jhx4K33+fbmkdx0kyriCnk0GD4Omn4ZJLzCpRXAwvvABr1sBf/uLuFk5yKCiA/faD+vXts6CgWsOpKv3796d79+58+umnvPPOO4wbN45Vq1bFdPzmMha5stux4ApypWytYqqqG4Cwimkk/YApwfpjQM+yVUxV9XMgrGJa7piq+hNAcPx2QM2ezNauhQsugFGjoH9/+Ogjc6845xw48UT4v/+ztgcfhA8+gN69YfHidEvtOE4ScQU5XSxaBDNmQG4uXH891Au+ip494dJLbd/996dTQqc2UlAAo0fDhAn2QDZhgm1XQ0meM2cODRo04LzzztvadsABB7B582aOO+64rW3Dhg3jgQceAKw62mWXXcZBBx3Eo48+us32c889x2GHHcZBBx3EySefzNq1a7ceN2bMGA466CC6dOnC4sWLWbZsGXfffTe33HILXbt25dVXX63ytdRiolUxLVuJtFQVUyCyimm0YyscU0TuB74BOgITIvqdGOF6EZnbnohjh4rIPBGZt3r16pgvskoUF5tV+F//gosugv/8xx4c65W5PYrYG7/p0+HTT+Hww2FW2SyljuPUFlxBThd33gkNG8KcOWbJi2T8eOjQAa67DtavT498Tu1k7FiYNAl69IAGDexz0iRrryLz58+nW7ducR/XrFkz3n33XQYMGFBq+6ijjuLaa6/lhRde4N133yUvL4+bb75563HNmzfn3Xff5S9/+Qv//Oc/ycnJ4bzzzuNvf/sb77//Pr///e+rfC1O4lDVM4E9gEXAKUHzE0BO4HrxPCUW67LHpq666bhxsGABPP443HqrKcIVcfzx8Oab0LIl9Otn7hjBA5zjOLUHV5DTwUcfweTJcMopEG3yr1cPbrnFrBT/+U/q5XNqL4sWmeUrksMPt/YUc8opp0TdfuONN1i4cCG/+93v6Nq1K1OmTOGLL77Y2u+EE04AoFu3bixbtixl8mY4yahiWumYQa76acCJwXaRqoZP/fcB8T9ZJZIFC+zh8LTTTPGNlXbt4L334Pzz4fbb4bDDLLjPcZxagyvI6eAvf7GsFePHl9+nTx9o2xbuvTd1cjm1n06dYO7c0m1z51p7FcnNzeWdd97Zpj0rK4stW7Zs3S6b9mz77bePuq2q9OrVi/fff5/333+fhQsXMmnSpK39GjVqBED9+vXZtGlTleWuYySjimnUMcVoB1t9kPsCi4PtVhHn64tZl9PHXXfZm5Rbbon/2AYN7E3gE0/Ajz/CEUeYwvzss7BxY+JldRwnpbiCnGpWr4bXXjMleY89yu9Xr571eekl6+84iWD0aBgyxFx7Nm60zyFDrL2KHHnkkaxfv56JEydubfvwww9RVRYuXMj69ev54YcfePHFF2Ma79BDD+V///sfS5cuBWDdunWVZsRo2rQpP//8c5WvobYT+BSHVUwXAdPDKqYi0jfoNgloFlQxvRi4PDh2ARBWMX2WoIppeWMCAkwRkY+Aj4BWwDXBOS4UkQUi8gFwIXBGki+9fNauhX//G04+GZo1q/o4f/yjBfT9+c8WxHfMMZYebs4ciHhAdDKIDRvgoYfg7bfhv/81V5pRo+DVV+2Nweefp1tCJxWoqi8RS7du3TSpTJmiCqrvvFN537VrVXfYQfWss5Irk5PRLFy4ML4Dpk5Vzc1VrVfPPqdOrbYMK1eu1JNPPln33ntv7dy5sx577LH6ySef6IgRI7Rdu3baq1cv7d+/v95///2qqrrXXnvp6tWrtx5fdvvFF1/UvLw87dKli3bp0kVnzpy5Tb+3335bjzjiCFVV/fjjj7VLly56wAEH6CuvvFLt60kE0b4XYJ7WgHkuk5akzck33WRz8RtvJG7M4mLV6dNVW7SwsVu3Vh05UnX+fNVfflHduDFx53ISyy+/qE6cqFpQoNq1q31/4dK4centPfZQHTPGlpUr0y25U03Km5e91HQZkl7W9M9/tlfaK1dWHgwCZt2bPh1WrYImTZInl5OxLFq0iE7VcJFwkkO078VLTcdP0ubkrl1hu+2i+w4XFJhv8qJFJW/6vvrKXJFGj4b8/IrHXrfOXC/+/W9zudi82d4Ktmpl6eTOOces1jWwoEyd48svre7A3/5W8ra2ZUsLkv/+e3N17NsX/vc/KCoy98hzz4XgDRf77AP/+Af8/vdWUMbJOMqbl7PSIUydZeNGmD3bXunFOjEOGGABfS++CH/6U3LlcxzHqQt8+qnlM77pptLtBQUwciSsWAE5OaYIzZhhdsMHHjAFaMgQ61uRkrz99jZ3DxgA335rxaBWrbKy1VdcYUvbtnaOb7+FNm3MNePMM6Fp0+Rcs7Mtzzxj7hMbN9rD0l13wZ57WuDyTjuV7vuHP5Ssf/KJPfS884652AwcCDvvbIGbOTmpvAInibiCnEpefhl++gkicsNWyhFH2IQ5a5YryI7jOIkgzA50wgkl1uKFCyEryxSd556z9d69TWHu2RNOP92Upi++MEUWKrckA+y2GwwfXrL92mvmn/zWW2a9bN7cFK5nn4XLLrMCUr/9LZxxxra5mJ3EUFhoDz733gv77gtnn2335X32ie14Efv/OOQQe7Pw5pumKP/ud2aJ/utfbb+T0fivL5XMmGFPqUcfHfsxDRuaZeGJJzzgw3EcJxE89hjk5Zl7RVg4p1Mnyyy0erW9cu/Rw6yEjz9uFuUVK6zfzz9bEFdVC+z89rd27MyZZnF86SWzaL/xhinkDz1kVuqdd7ZX+2lIwVhrUTWXxaOOsu8yL88eVi66KHbluCwNG5p7RWEhtG8PI0ZYbm0n40m5giwifUTkYxFZKiKXR9nfSEQeCfa/KSI5EftGBe0fi0jviPbJIvKtiMwv55yXiIiKSPOkXFQsrF0Ljz5qE2C8vsR9+9rrubffTo5sjuM4dYUvv7S59MQTSxfOWbzYLL05OXDVVda3Y0dTUK+6ytp79DBrYevWpmwNHJiQcu2AWSNnzLDKfg8/bPEqzz9vGTFuusneQLqRpGqsXm0uMrvuavUHfvc7S8336qtmwU8EeXn2sDNggP2/nHMOeBrKjCalCrKI1AfuBI4BOgP5ItK5TLchwPeq2g64Bbg+OLYzlmczF+gD3BWMB/BA0BbtnG2Ao4EvE3ox8fLSS+ZrNnRo/Mcec4xV2/Oypo7jONVjxgz7PPHE0oVzwhzh11wDy5aZZbFfP5t7ly0zpWfOHFOw1q+HiRPtVXsCyrWXQsQU7/vus3RihxwCl14K3bvba/xnn03MeeoC331nuaqzs80tZvfd4dpr4emnYccdk3POyZPhkkvs+3NLckaTagvywcBSVf1MVTdgFZb6lenTj5Lyo48BPYNk8/2Aaaq6XlU/B5YG46GqrwDflXPOW4CRQHrTdbzxhk20kY7+sbLrrvYK54knEi+X4zhOXeLll62Caf/+ZpHt0MGU2zBH+OrVZiE+6yxTcHbc0fxJhwwxC3OjRhZ0l5Vl/YYPL/FLTpSSHNKypSnlH38MY8aY5fuYY0y5HzjQ9jnbsnKl5S9u3x6GDYP99zfXlQ8/tO85mYGQ220H//ynfT9XX23K8oYNyTufkzRSrSC3BpZHbK8I2qL2UUtE/yPQLMZjSyEi/YCVqvpB9cROAG++aT/SMtXDYuZPf7IS1Z6g3KmhzJgxAxFh8eLFW9u++uorTjrppJjH+OGHH7jrrrviPndOTg5r1qypsM+IESPo2LEj+++/P/379+eHH37Yum/cuHG0a9eOfffdl9mzZwOwfPlyevToQefOncnNzeW2227b2v+7776jV69etG/fnl69evH999/HLbOTBlTNV/SXX8zy++CDZg2++GJ7HT5wIFx+uQVebb89TJ1qqb0efNBcLCZMsH2bNpVYkhPhl1wR9eqZEv/3v9u5L7wQXnjBDCa9epnbx6BB5jddl9myxfy6e/Uyi/GJJ5o746OP2puBU081f+FUcffdFmh588323TmZR7TkyMlasPKl90VsnwbcUabPfCA7YvtToDlwB3BqRPsk4KSI7RxgfsR2E+BNYKdgexnQvBy5hgLzgHl77rln9bNOl2XzZtUdd1Q977zo+8PCDSKqjRrZZ9kCDp98YgnKb7st8fI5GU3chUKSxJ///Gc9/PDD9aqrrqryGJ9//rnm5ubGfVzZQiPRmD17tm4MCjWMHDlSR44cqaqqCxYs0P3331+Li4v1s88+07333ls3bdqkX331lb4TFPT56aeftH379rpgwQJVVR0xYoSOGzdOVVXHjRu3daxIvFBIDSwU8uWXNo8OH17SNnWqak6OtVdUOCecp8H6Z2erFhbavsJC245lnETx3XdWROq3v7VCFtnZqscfr3rYYapt2tg9Z999Vd9+O7lypJtHHlHt0aOkuEfTpqrXXKP62GOq336bbulUhwyxe/q0aemWxCmH8ublVFuQVwJtIrazg7aofUQkC9gJKIrx2Ej2AdoCH4jIsqD/uyLSsmxHVZ2oqnmqmteiRYu4LigmFi+29G6HHlrSVlBgwR316sHgwfYqKCfHkpPvtRccf3xpa0T79uYj537ITg1k7dq1zJ07l0mTJjFt2rSt7cuWLWO//faLesyNN97Ib37zG/bff3/GjBkDwOWXX86nn35K165dGTFixDbHHH/88XTr1o3c3NxSpa1j4eijjyYrSL106KGHsmLFCgBmzpzJgAEDaNSoEW3btqVdu3a89dZbtGrVioMOOgiwUtadOnVi5cqVW48ZPHgwAIMHD2ZG6Nfq1Gzee88+I99q5OdbmrV69WD+/PJTt+Xn2/6pU81P+KuvzD84ml/y8cfbvF6vXuKC+Mqyyy4WYPi//5kMbdta8YrttjOr8plnmqW8Rw9zFXn+eYuDKcuWLWZZzxRUzcXlssssgP2UU+y6f/nFrLVffgn/939mQU7G/Txebr0VDjvM/h8+/TTd0jhxkOpEfW8D7UWkLabcDgAGlukzCxgMvI5ZnAtVVUVkFjBVRG4G9gDaA2+VdyJV/QjYLdwOlOQ8Va34PWwyePdd+8wLCrWE/m6TJpl/1JAh9lpv5Eh71XfggebXNmmSfYYTdt++Fs3844/bJjF3HLD8m++/n9gxu3a1Sb4CZs6cSZ8+fejQoQPNmjXjnXfeoVu3buX2f+6551iyZAlvvfUWqkrfvn155ZVXGD9+PPPnz+f9cq5h8uTJ7Lrrrvz666/85je/4cQTT6RZs2ZxX9LkyZM55ZRTAFi5ciWHRjy8Zmdnb1WEQ5YtW8Z7773HIYccAsCqVato1aoVAC1btmTVqlVxy+CkgVBBXru2dPvcuWaAiIVwPj7zTNhhB+jc2fySH3zQ2lu3NiV6/HhT2NatM9eNkSPhhhtiy50cL4ceCq+8sm37yJGmKF5xRUlby5bm7nf44Ra0NmqUKcn9+pm/7N57W8xMbq5d3yefWP905fXdtAl++MEyOc2cafmjZ860B5E2bczlZNy4mltpdocdLLVcx46W2SLMse3UeFL6LanqJhEZBswG6gOTVXWBiFyDmbhnYa4TD4nIUizwbkBw7AIRmQ4sBDYBF6jqZgARKQC6A81FZAUwRlUnpfLaKuTDD833ad99bTtaaqERIyzf5rXX2sS1cKEpzwsXmgVi9GjzQ77+evuBnXxyeq/JcSIoKCjgoosuAmDAgAEUFBRUqiA/99xzHHjggYBZoJcsWcKee+5Z4Xluv/12Hn/8ccB8hJcsWRK3gjx27FiysrIYNGhQTP3Xrl3LiSeeyK233sqOUSLfRQTxksGZwXvvWbnnYcNsDj78cFOOhwyxeTlWQiU3zKF81FGmyJ17rrVPmmR5k1euNJ/n0L958GDzF+7cObaS1dVljz0s1/OaNVY58MMP7fP99y3oT9XuS4ccYsVTpkwxhW7tWlNAmzQxBb9jRwta3LABvv7aAsePOcYstO3aVV2+TZvsPPXrWzW7JUvMyvr55ybzt9/afTHS8p2VZYrm3XdnTiGV1q3h9tvtb3jppZUaHJyaQcofY1T1aeDpMm1XRawXA1G1P1UdC2wzi6lqpbOMqubEK2vC+PBDexoPnxqjpRYK822CRb6GUdOTJtkEPGSI1XvfdVd46ilXkJ3opGHi/e677ygsLOSjjz5CRNi8eTMiwo033ljuMarKqFGjODdUKAKWLVtW7jEvvfQSL7zwAq+//jpNmjShe/fuFBcXxyXrAw88wJNPPsmLL764Valt3bo1y5eXxP+uWLGC1q0t/nfjxo2ceOKJDBo0iBNOOGFrn913352vv/6aVq1a8fXXX7PbbrvhZADvvWfVSfv2NcPEokU2B48dG7+yGvYfPtwssEOHmovcqafa/N6hQ0nu5IceMoXvpptSZ1WOpHlzqwbYs2dJ25o19vc47DBTim+5xVKivfGGyfPZZ2agOeQQK8E8cmTpMf/xD/vcfnt7O7rzznYfEzF3h6wsU4CbNLFMIEuXwvLl5grSvr1Zgp95xvq0aWOZQCKzPYjY/a5TJ7Ny77ij5S/u0MH2ZRpnnmmlqW+/3f6+wdsopwYTzTG5Li8JDQgJadlSdfBgW586tXQg3rBhqm3bqg4apNqggepNN9nnoEHWHgZ6FBZa//x81d13t8C/dPLxx6q//ppeGRxVTX+Q3j333KNDhw4t1faHP/xBX3755XKD7mbPnq0HH3yw/vzzz6qqumLFCl21apWuWbNGywuUnTFjhh533HGqqrpo0SJt1KiRzpkzR1VjC9J75plntFOnTvptmcCd+fPnlwrSa9u2rW7atEm3bNmip512ml500UXbjHXppZeWCtIbMWLENn08SK+Gzclr1qiC6vXXJ2a8SKZOtfm6sFC1c2ebx0H1wQdtf06OLQ8+aO2FharPPafavLnN91B+gHZNoLhY9a23VIuKLPDt/fdV77lH9R//UD37bAsG3GMPuw6wawqXsG377VX320+1SRPbbt1a9ZxzVC+5RPXEE1VHjlR96CHV119XXbVKddOmdF914vnxR7vuVq1Uly5NtzROQHnzctonv5q2JFxBXrXK/sw33VQyiY4ebZ833WST5vHHl54kwSbZyElywwbVevVUp0yx/UF0fcrYskX13nttEjv99BIZu3ZVffLJ1MrilCLdCnL37t31mWeeKdV222236XnnnVdhVopbb71V99tvP91vv/300EMP1aXBDSM/P19zc3P10ksvLdW/uLhY+/Tpox07dtR+/frpEUccEVVBPuaYY3TlypXbnG+fffbR7OxsPeCAA/SAAw7Qc889d+u+a6+9Vvfee2/t0KGDPv3006qq+uqrryqgXbp02XrMU089paqqa9as0SOPPFLbtWunPXv21KKiom3O5wpyDZuTX3jB5q299rK5NNGKaGQ2ogYNVFu0MCW4sFC3KsuhoqxaoixHGkci7wc1VVkujy1bVH/4QXXjRlNuN2+2ti1bLHtIaFD59VfVr79Or6zpZP581WbNVPfZR3Xt2nRL42j583LaJ7+atiRcQQ4n5RdesMkuTAsUTqb16plSHDkJRvYLCS3I33xj440dm1g5K2LTJkuLFFoCGjZUbd/elrCtTx/V9etTJ5OzlXQryE50XEGuYXPywIE2V/33v2ZwKCws/ZYukUydamnXwpRwrVuXb1Xu3NmUYVUznoTKcufOJduZpiw7FfPSS/a/cMkl6ZbE0fLn5bRPfjVtSbiCfPPN9mf+9ltThjdsKL0/tAxHEvm6bsOGbSfJnBzV3/0usXJGY/581VmzVI86quTH/NZbqt9/X9JnxQrL8wiqBx+s+sUXyZfLKYUryDWTmqYgA32Aj7EqpJdH2d8IeCTY/yaQE7FvVND+MdC7sjGxYOsPgA+xiqg7VHaO8paEzck77WQuDZGEhodkEYtVuV49U4ZVS5TlDRvsM3zT2Lp1SY7l7GxXlGsDZ5+tmpWl6vN32nEFOcYl4Qry4MHmg6xasWW4LGUn1tGjS6weO+9s7cuWJVbWSN58U7dah7fbTvW++yruP2WKTfR/+EPNSM5eh3AFuWZSkxRkLGvQp8DeQMNAee1cps/5wN3B+gDgkWC9c9C/EZZb/tNgvHLHBHaMGPfmUHku7xwVLQmbk0G1e/fSbdEMFMmiPKtyOL+rlijLhYX2ZrGwsHy/Zbcqp57IN7/Z2bZEFviK1lbed/Ttt/bQdswxKb8MpzTlzcsZkiMlg/nwQ8shCZbWZ8gQS+q+caN9Dhli7WUJk9J37gyzZ1v6twYNLCJ6wgRTXWfOTI7Mn34Kxx1nkc833miRzEOGVHzM6afDv/4Fr74K55+fWYnnHaf2czCwVFU/U9UNwDSgX5k+/YApwfpjQE+xVB/9gGmqul5VP8esvwdXNKaq/gQQHL8doJWcI/lkZVkRjUjiyX9cXfLzLYvD1KmW+eGrryz//R//aG0332zpzg480ObbDRssG8ZVV5Vkw/jmG8s+MX68pXALs2G0aZOcYiTOtkW9jj8eHngANm+2EuPNm1v2kmbNLD1eZNtee1nGjmhFY1q0gCuvtEweQXl7p4YRTWuuy0tCLcgbN5q/bmSwUeQTaCxP/+W5ZYC5PiSa1avNt3jXXVUXL47/+DFjTLZbb024aE50Fi5cqFu2bEm3GE4EW7ZsqWkW5JOA+yK2TwPuKNNnPpAdsf0p0By4Azg1on1SMF6FYwL3A6uAOUCTis4RRd6hwDxgXnlZTeJi3Tqbl3beucR1LZk+yLES+aYwDNAOLcq1MRtGJlGexb9t25Iy45FBl+F6ZFukT3k0N5niYgvW69TJY3jSSHnzsluQk8nixWYFWLfOnhzr17d8m6NH29NnRWVNQ8I8yZHMnWtPqy+9BKtXJ07eX36x/KBffmklrcPCJvFw1VXQq5flyIxW1tRJOI0bN6aoqChULJw0o6oUFRXRuHHjdIuSVlT1TKzq6SLglDiPnaiqeaqa1yIR5YLD/NoDB1re4saN7bMq+Y8TSfimcMsWKC62N29TpsCMGZaj+fLLzdLYsqW9cVy2DK65JrpV+c9/treM0SyXbdrYUr9+8kpf1wbKWovXr7fCXJMnW9GXFi1KisAcfrjdK7/4wo4N1yPbHn/c8jy3aGHHT55s4xUX2/jbbWdvkxctsnzYTo3C6x0mk3fesc9ZsyxRfGTVJohtYg7dMspWfRo50mrRT50KQQWzKvPjj/CXv8Dzz8N331lZzN/9rmpj1asH//ynvSa8+WZ7FegklezsbFasWMHqRD4sOdWicePGZGdnp1uMSFYCbSK2s4O2aH1WiEgWsBNQVMmxFY6pqptFZBowErMol3eO5PL55/Y5aJAVw6jJ5OeX3BsKCmyuP/poU4hbtzajyLJlJaWtQ2X5vfdMGbv4YpvHb7ihpNz15s2mfJ97rr3SHzjQqgk2aWKuHp06paayX00l/DuvWGF/yz32sL/jJZfYA8hpp1n7VVdZ6W2we3Fk5c9oVUAXL7a/beQDTWTRmEmT4I47zKXx2mut0t4ee6Tggp2YiGZWrstLQl0shg83F4nnny/dHm/kdNnXcJHZLDp02NYFI15OPdVe++y6q+r06dUbK+Tkk1UbN1b9/PPEjOc4tQDS52KRBXyGBdmFAXW5ZfpcQOkAuunBei6lg/Q+wwL0oo4JCNAuOFaAfwL/rOgcFS0JmZMnTLA57quvqj9WOqhONozQFSN83V9YqDpqlGVQaNlS9S9/KXn1v+uu9vo/GXmiaxpVzVsduku0amVBdi1aWFvz5ubCE9kWFv2K5iYTBohOnWrjgeqOO9buv3kNpbx5Oe0KaU1bEqogH3mk/YljSe1WGWVTvxUWqu62m43/0ENVl/Hf/7Yxxoyp+hjR+OILm4DPOSex4zpOBpMuBdlOzbHAJ5jf7+ig7Rqgb7DeGHgUC8J7C9g74tjRwXEfA8dUMmY94H/AR5jP8cMEWS0qOkd5S0Lm5Isvtgf22uCrH282jPAeFJlvOVyPV2kOszRkmgJdNvvErrtu+/eLx9e7vIwVZdtCn/JoyndhofVt29b2hVmjWrXKnL9rLcEV5BiXhCrIOTn2NBlrareKiJYi7oUX7Ee4//5Vm/jfe89SuB1+uAUUJpoLL7R/sfnzEz+242Qg6VSQM3VJyJzcv79qx47VH6cmUdYCevzxJXmTQ8tlZEBZpIU5XI9XaW7VqnwFuqrpzhL5t4iWfi0y8PHBB+0asrJUL788dmtxdXJQxxLsV1hogXotW1p12mTm5na2wRXkVE7GqiVW4v79t7X8ViVyurxsFiL2Nc6cGd9433xT8mP95pv4jo2V1attIho6NDnjO06G4Qpymubkbt1Ue/eu/jg1lYqyYURTCsN9sSrNkXmZoynQlbkalC2hHWsO4UiLdXn7I63BoRIfzc2hvIeFWKzFiVDuo9U2qFfPztW2reqwYaYgh5ZktyKnDFeQUzkZq6ouXWp/3vvvjz+1WzTKKzLSqZOlZcvNjc0XubhY9ayzVHffXbVJE6uMl0xCP2y3IjuOK8jpmpN3390ql9UlKnIrCBXJWJXm0Je5PFeNeNOdxeK/G7ZFWq+j7Y92DZHnrszdJJHW4ni/m/D7GDbMlORnn1Vt1sweAHJyXElOEa4gp3IyVi3xKXr55cSMF80HObREP/KInWvs2IrH2LLFlGNQbdp0W4U7GaxZo7rDDqoDByb/XI5Tw3EFOQ1z8vr1Nuf9/e/VG6c2UFWlOdKCHE2BrlevROEM1yPbogUNVqZUh+uR5462vzJ5KgtYTGd1wvC+npNT4qPcrJnJ2aKFfWaSr3eGUmMUZKBPEOixlKD8aJn9jYBHgv1vAjkR+0YF7R8DvSPaJwPfAvPLjHUjsBj4EHgc2Lky+RKmIN99t/15ly9PzHiqFVuie/e2oiRlM2aEbN6seuWVJtOVVyZOplgYMcJkXrIkted1nBqGK8hpUJBvu83mPS+eEZ1YlOZIK25lFttoym40K25lSnW4Hmm9jra/Mot2NOt1WT/qZFqLK2Pq1NL/n+efb/KGfss1oaBNLadGKMhBaqBPgb0j0gJ1LtPn/DJpgB4J1juXSTX0KVA/2PcH4KAoCvLRQFawfj1wfWUyJkxBHjnSFNbNmxMzXmV8/bW5WjRqZK9rPvnELMYbN1oEd+jXNGRI6mQK+eork8t9kZ06jivIKVaQp041hQhUn3rKlY1YiRb0VpECHWu6s3iq0MVqQa7MJ7o8/+ealIkj0oUyN1f16qvtb9y6tbVVJbDfiZmaoiAfBsyO2B4FjCrTZzZwWLCeBazBcmmW6hvZL9jOKasglxm3P/BwZTImTEE+6STLUZwsolmTFy+28tOR6WLC9exs1ZtvTr1yHHLOOZZm6dtv03N+x6kBuIKcYgU5N7fkzdmCBdbmykb1qShrREXpziKDBhPlgxxLVo2aogiXR6QLZb16qrNnm4Ftxx0tzqhePfXAveRRUxTkk4D7IrZPA+4o02c+kB2x/SnQHLgDODWifRJwUsR2ZQryE5HHl9k3FJgHzNtzzz0T8xfPy1Pt0qX6wXnRqMgfecsW1VmzVA88UHWvvUyG225Lf/7PBQvs3+0f/0ivHI6TRlxBTrGCXK+e6rhxNvf8+KO1VSUPvVN94lWqY81iUdOswVWlbOBev362np9v/sk5Of72I0mUNy/XiVLTIjIa2IQlrN8GVZ0ITATIy8vThJx06VIr+zlrVtVKTFfE2LFWorJHD9vu0cO2hw+3sf/0J1tqEp07Q+/eVuZ1xAho1CjdEjmOU9vp1AnmzYMddoAdd7S2uXOt3UktkSW0nW0J/z4FBVb2+4MPoFcvePRR+5+9/npo2bLkPu8knXopPt9KoE3EdnbQFrWPiGQBOwFFMR67DSJyBnAcMCh4Ukg+GzfCDz/ACSeY8tqgQYkSO3Zs9cdftMiU7kgOP9zaazJ/+5vVtX/kkXRL4jhOXWD0aHj2WWja1OblOXPMUDF6dLolc5zo5OebnrBsGTz/PGzeDB062L5hw2DBAthvP1OknaSSagX5baC9iLQVkYZYEN6sMn1mAYOD9ZOAwkCxnQUMEJFGItIWaI+VKi0XEekDjMRKqf6SwOuomK+/ts/DDivdnigltlMne6KMJBOsIkcfbZbkW24xz2jHcZxkkp8P7drB999D48ZmfRs71i1wTs0mPx9yc6GwEM47D156CUaOtIe7zp1hwgR7yHMlOamkVEFW1U3AMCzAbhEwXVUXiMg1ItI36DYJaCYiS4GLgcuDYxcA04GFwLPABaq6GUBECoDXgX1FZIWIBL4M3AE0BZ4XkfdF5O6UXOjKwLD9/fel2xOlxI4ebT+UOXMyyyoiAn/9K7z/Prz8crqlcRynLtCwIRxxhFni5s935djJDML7fPfusGULtG4Nd9wBV16Z2DfSTrmk3AdZVZ8Gni7TdlXEejFwcjnHjgW2+Y9Q1agznqq2q5awVWXFCvu84w449NDSPsiJ+IcOJ/jhw80i3alT5lhFTj0VrrjCnoC7d0+3NI7j1HaKikpeUTtOphDez6+5xt64vvkm3Hqrte23n937t2wxK3Im3PszkFS7WNQNQgvymDGmxCbj1V5+vllD/v1v2z711Jrhl1RQYHLUrx9dnu22gzPOsODFL79Mi4iO49Qh1qyBZs3SLYXjxE94n+/QwWKZZswwy/KECeZbn5PjrhZJxBXkZLBypWVpOOcc++dO1qu9MNp1wgQoLk6vX1JBAbRpAwMHwrp18MADcPzxMHgw1KtXWlkeNszcLW6+OfVyOo5Td9iwAX76CZo3T7ckjlN1/v53My699BJcFbxwP/dcuO46d7VIIq4gJ4MVKyA725TAZBKZ7i3RmTJiIbQW16tnivD69fDcczB5MlxyCUycCOPHwx57mNI8cKAp0a+9Bv37w4MPws8/p0ZWx3HqHt99Z59uQXYymfx8GDfO1s88s/Qb6UzIYJWhuIKcDFauNIf6ZJOudG9lrcV77GGK8OrVlsatRw/YfntbWrSwv8fkyaY8Z2WZlXu//SyI8a67kiur4zh1lzVr7NMtyE6mc/759qBXrx48/nhJzuQOHcwXuSa4WNYyXEFOBitXmgU52aQj3Vvo1pGVVWItXrnSFOGcnJLXP19+CV98Yds5OaY0d+9ubarWvv32plhv2JA8eR3HASztpYh8LCJLReTyKPsbicgjwf43RSQnYt+ooP1jEeld2Zgi8nDQPl9EJotIg6C9u4j8GGQVel9EriKZFBXZp1uQndrAtdfa/fOCC+Chh+xNbXGxvY311G8JxxXkRKOaOgtyKtO9he4UAwfaNX7xhSm8PXqUKMbXXGPJzefMMQvzbrvZ9jXX2BhXX22K9cSJ5n5y1VVWUOWiixIvr+M4WxGR+sCdwDFAZyBfRDqX6TYE+D7I/nMLcH1wbGcsZ30u0Ae4S0TqVzLmw0BHoAuwHXB2xHleVdWuwXJN4q82ArcgO7WJ886DY4+1AiKnn26xTjffDKed5qnfkoAryImmqMh8cVOhIIcVd8JMGaefbopyojJalPUxPv54W5840RTdq6+2fqFivHq1XfdZZ5kCvWaNWZZbtjSl+YYbLNl5VpYlO8/ONt/pu++2pOj+5Os4yeJgYKmqfqaqG4BpQL8yffoBU4L1x4CeIiJB+zRVXa+qnwNLg/HKHVNVn9YArKBTCl6pRcEtyE5tY/JkaNLE1j/5pHTwv/sjJxRXkBPNN9/YZ6tWqTlfZLq3Bg3sVUsiMlpEZsjo1MlcIaZONX/jrCxTdG+4wRTfli3NQnP55fDVV+Y6MXWqvQJq1Mgq6J11FmzaZAn7hwyxp90rryyxHg8e7K+HHCd5tAaWR2yvCNqi9gmKOv0INKvg2ErHDFwrTsOKO4UcJiIfiMgzIpJb1QuKidCC7AqyU1vYbTfLBAXw8MMl7e6PnHBcQU40335rn7vvntrzJjKjRUGBRcouW1ZSjGT4cBsPTME94gizVp91linAjRvDlCn24wxT2uXnw/Llpixvv725ZgwdajLNmWPj9eplCvfzz/vrIcepfdwFvKKqrwbb7wJ7qeoBwARgRrSDRGSoiMwTkXmrV6+u+tmLiszatt12VR/DcWoaI0aY8WnYMLuXuj9yUnAFOdGsWmWfu+2W2vMmKqNFaDneuBHWrrUfWuhOcfjhZiEeO9YUXSixFi9fXn6e59DKPXWq+R63bGlybdpkPlUnnQQvvAA77OCvhxwnOawE2kRsZwdtUfuISBawE1BUwbEVjikiY4AWwMVhm6r+pKprg/WngQYiso2DsKpOVNU8Vc1r0aJFfFcaSVGRW4+d2kfz5vDXv8Ivv9i92P2Rk4IryIkmtCCnWkGOzGgR+g43bmzW5FieIssG4e2xh5W27NGjxJ1iwgTo2NEUXBFTeOMpgBLpM71lS4k1uWdP820++ODY5XUcJx7eBtqLSFsRaYgF3c0q02cWMDhYPwkoDHyIZwEDgiwXbYH2mF9xuWOKyNlAbyBfVbeEJxCRloFfMyJyMHYPKkrKFYMFAe+8c9KGd5y0cfHFdo//7W/t/hn6I4f38qOOggUL/H5aHVTVl4ilW7duWi369VMFVRHV3FzVqVOrN16sTJ2q2rat6ujR9nnTTao5OSXb5ckxdapqdrbJnJNjcj/3nGqLFrYUFqquW2f7GzRI3HWVlff3v7dzDB9esbyOk8EA8zRNcxtwLPAJ8CkwOmi7BugbrDcGHsWC8N4C9o44dnRw3MfAMRWNGbRvCtreD5argvZhwALgA+AN4LeVyV2tOfmII2xucZzayN/+plqvnmq7dnavDu+rhYV2H8/J8ftpDJQ3L4vtc0Ly8vJ03rx5VTu4oMCsoo0awddfm0V3yJCSijfJJvQd3rDBskSE6d6uuML8iXNzS9rGjoWFC819Yuedzdk/Kwt69zaLcc+e9tpmp53M7aFBA7j//sReR6S87drB0qVw6aVwzDFmZZ4/P3HncpwagIi8o6p56ZYjk6jWnHzggZYt54knEiuU49QEvv0W2raFrl1N51AtyTIV6h4tW/r9tBLKm5fdxSKRjB0LXbpYDuB0lH7Ozzff4fXrS34Mo0eX5B0+/njzT6qoAl7oTrFpk+VznjAB9tor8cpxWXk/+QT+/Ge45x7Yf3/3RXYcp/q4i4VTmwkzWrz+ulXaW7bMjFxeijohpFxBTlI1p8ki8q2IzC8z1q4i8ryILAk+d0nqxS1aBJs3l/Y/TvU/Z6QvcpjZIivL8hNPnWp5iffaq/wKeGPGmHI8dKg9jUb+0JIt76WXwk8/Qfv2nqrGcZzq8+OP9hbMcWorI0ZYppZ58+wt8YsvlsQGeeq3apFSBTkZ1ZyCYx4I2spyOfCiqrYHXgy2k0enTpbNIVJBTnbp57JEVtcLM0UMGWL7Jk2yVzJffll+BbyXXjIFuipBeImQt2FDu6lNnuypahzHqTqqNpe4BdmpzYQZLR55xIqEhfdTT/1WfaI5JidrAQ4DZkdsjwJGlekzGzgsWM8C1gBStm9kv2A7B5hfZqyPgVbBeivg48pkrFZAyNSpFsR28smqGzaYo3w6HOSnTrVAujDwbupUc+TfsMG2c3Ks34MPWp+bblJt3drawYL2UilzpLy77WafDz1k+woLbZ/j1AJIY5Bepi5VnpN/+snmkhtuqNrxjpMpfPedatOmqvn50e//IX4/jUp583KqXSySUc2pInZX1a+D9W+AqNU7EpaUvm9fs1q88oqlX0m2e0J5RMs73LGjPUGuW2dLRRXwKsppnEx569Uz63aXLjBunL0Wcv8px3Gqwg8/2KdbkJ3azi67mFvk9OmW9i28n3op6mpRZ4L0gqeEqCk7NFFJ6cMcyOPHmy9yst0TKiMy7/CiRaYIDx0KN91UcQW8dNGpE7z2GowaZRk29twzvlzOjuM4IT/+aJ/ug+zUBS66yAxit9xi29WtjeCkXEFORjWnilglIq2CsVoB31ZZ8lgIq+ilusx0RYTW2S1bTBGeMQPOOCN91uKKCP2RP/rIfJF//tlSNF16qftOOY4TH25BduoSbdrYvfy+++D770vup1deaetnnWVGJ7+fxkyqFeRkVHOqiMixBgMzE3AN5ZOuKnqxEirLNcG6HY3Q4n3zzZYb+aefzN3i2mu9bKbjOPExK7i19OnjEfxO3eDSS82F8l//Krmf/vOfFoQ/eTJcd53fT+MgpQpy4FM8DAuwWwRMV9UFInKNiPQNuk0CmonIUuBigswTqroAmA4sBJ4FLlDVzQAiUgC8DuwrIitEJEjbwHigl4gsAY4KtpNHTVeQM4EwN/KGDfb0O3u2uYe475TjOLFSUAAPPGDrH37oEfxO3WD//S0P8u23wy+/bFsbITSK+f00JlLug6yqT6tqB1XdR1XHBm1XqeqsYL1YVU9W1XaqerCqfhZx7NjguH1V9ZmI9nxVbaWqDVQ1W1UnBe1FqtpTVdur6lGq+l1SLy5UkKvjx+yU+E6NGwc77AAnn+y5kR3HiZ2xY60gElggcqqLNjlOuhg92tw9b7jBtiN9kcFzI8dBnQnSSwlFRebb27hxuiXJbELfqfnzLTPIggUWcOO5HB3HiYVFi0re5DVtap9uNXPqAr//PQwYANdfbzFGkbUGPDdyXGSlW4BaRVERNGuWbikyn/A10PDhphw3bAj168MJJ9gDyKRJtq+m+VA7jlMz6NQJFi+29e22s89UF21ynHQxfjz85z/2eeed1hbeT3NyzBc5vH/6/bRc3IKcSIqKYNdd0y1F7SAyN/Jzz9nf9oorbJ9bghzHqYjRo2HmTHubt3mzWc+GDLF2x6nt7LUXnHmmZbRYsaL0/dRzI8eMK8iJxC3IiadTJ/OVGj7cAg9eftktQY7jVEx+PvzmNxaglM6iTY6TLkaNsnvn9deXtHlu5LhwBTmRuIKceEL/qT59zKfwqKPgyCPNJ9l/0I7jlMcee1hu2Jqa1tJxkklOjtU8uPdeq5QLnhs5TtwHOZG4gpx4wpvaueeWZAk54ggYM8Z+6JF9HMdxQtats5gFx6mrXHEF3H+/WZFvu63kXnnmmZZKNcyNnJ8PPXu6L3IZ3IKcKLZsseo1riAnnvx8KxdbWGjlsl9+2YqIeNomx3HKwxVkp67Tti2cfjpMnAhff21tnhs5ZuJWkEVkmog8FCw3JEOojOTHH01J9iC95BAWC7n6ajjwQDjnHMuN7D9op44hIlV6CheRPiLysYgsFZHLo+xvJCKPBPvfFJGciH2jgvaPRaR3ZWOKyMNB+3wRmSwiDYJ2EZHbg/4fishBVbmWmHAF2XHMdWLjRrjxxpI2z40cE1WxIL+uqqep6mnA9ZX2risUFdmnW5CTQ/iDbtgQ/v1v+OEH/0E7dQ4R+Q9wbaCA9ojjuPrAncAxQGcgX0Q6l+k2BPheVdsBtxDM70G/AUAu0Ae4S0TqVzLmw0BHoAuwHXB20H4M0D5YhgL/iuPy42PdOmjSJGnDO05GsM8+cOqpcPfdVkAEPDdyjFRFQe4nIkNEpIOqFiVcokzFFeTkEvmDfucdi7z99Vf429/8B+3UJRar6l9UdRBwUhzHHQwsVdXPVHUDMA3oV6ZPP2BKsP4Y0FNEJGifpqrrVfVzYGkwXrljBhVTVVUVeAvIjjjHg8GuN4CdRaRVfH+CGHELsuMYV1xhCvBdd9l2fr65Jw4fbi4YjRrBzTfDaad51ckIqqIgnwp8BZwgIvcmWJ7MxRXk5FL2B73rrhah/vDD0KWL/6CdukIfEblERI4CfonjuNbA8ojtFUFb1D6qugn4EWhWwbGVjhm4VpwGPBuHHIjIUBGZJyLzVq9eHcPlRcEVZMcxOnSA446Df/3LDEvguZFjoEIFWURyy7ap6leq+oyqjlfVc5InWobhCnLyifxBL1kCTz5pgZHDh/sP2qkr9AEWAb8DWovIlEr6p5u7gFdU9dV4DlLViaqap6p5LVq0qNqZXUF2nBL+9jdYvdpcKiIp648MXmsgoDIL8ta/pIicHblDRNy5KxJXkFNH+IPef3/4v/+DadOgdWv3R3ZqFeUYKFYH7gtXq+pAVR0c43ArgTYR29lBW9Q+IpIF7AQUVXBshWOKyBigBXBxnHIkhl9+cQXZcUK6d4dDDoG//x3Wri1pj3Rf3LjRciT37g0LF9b5+2llCrJErJ9fZl9cFoFaT1ERiFg6Mie5RP6gs7MhKwvWrIFbbnF/ZKc2kUgDxdtAexFpKyINsaC7WWX6zAJChfskoDDwIZ4FDAiyXLTFAuzeqmjMQN7eQL6qbilzjtODbBaHAj+q6tdxXkvlbNlieV632y7hQztORiJi98ivv4bx40vaI90XGzWCG26AkSMtDVwdv59WpiBrxLqU2VelHMopTjXUU0TeFZH3RWSuiLSriswx8cMPsOOOUL9+0k7hBET+oM86C3bf3X7Yjz5qrhbuj+zUDhJmoAh8iocBszEXjemqukBErhGRvkG3SUAzEVmKWX0vD45dAEwHFmK+xBeo6ubyxgzGuhvYHXg9mH+vCtqfBj7DAv3ujXJdiWH9evts1CgpwztORnLYYTBoEPzzn/DllyXtofti584wezZce62Vn67jAXuVVdJrKSJnAB+wrYKs23avmIi0QL2w4Iy3RWSWqi6M6LY11ZCIDMBSDZ1SJtXQHsALItIhOKa8Mf8F9FPVRSJyPnAlcEa8csfEzz+bguykhvx8W+rXh88/h8ceg4EDLXBv9WqzIBUUeFUgJ5NJqIFCVZ/GFNTItqsi1ouBk8s5diywzV0y2phBe9R7S2CRviAuwatCcbF9Nm6c9FM5TkYxbpwZk665Bu67r/S+sN5AJHU4vqeySfbvQDfgViBbRBaKyH9EZCzQvArnS2mqIewGE2qtO2HZNxJPQQH897+wfHmd99lJOaE/sqrlPP3xR5gyxerQ1+FXQ06toKWInCEiB5IAA0WdwhVkx4lOmzZw/vlWgvqTT0rviwzYKygwfaZxY7Mm18F7aWUW5I+Ae4OnfkQkG0v8vj/wShXOFy3FzyHl9VHVTSISmWrojTLHhumByhvzbOBpEfkV+Ak4tAoyV0xBgSliHTrYP9F115l/LLj1MhWE/siqcM89VmFv6FB7Mm7VymvLO5nM3zEDxZkEBgrMlWExVTNQ1B1CFwtXkB1nW0aNgnvvtYC9qVNL2sP76cCB1j5smPkhDxpk+6BO3U8rsyCfDrwTlJc+A9gUpHi7XlVPTb541eZvwLGqmg3cD9wcrVO1cm6OHWs+OvXrm4tFHffZSTmhP/KyZZYfeaedLM/jG2/Yj3vBArfqO5nKR8CFqnqEqjYHjgbuwx72q2KgqDu4Bdlxyme33eDCCy0D1EcflbSH99N//tPuqZMnm9Hv2mvrpF5ToYIcVGw6CLNk7AI8ICKvi8h1IvKHwKc4HlKWakhEWgAHqOqbQfsjwG/Luc6q59wMfXYifZDrsM9OWsjPh9xcePFFWLkS9t4b7rzT2jt3rvORuE7GkukGivThCrLjVMyll0LTpjBmTOn2/HxL97Z+vQXuhRbjOqjXxBTooaqLVfUWVe0DHAnMxYI53qz4yG1IZaqh74GdIgL5emGvJxNL6LPz008lCrIn2U494auhVyIMa9dfbzkd3arvZCBJMFDUHUIF2bNYOE50dt0VLrkEHn8c3n679L6yxUMKCsyNtI7VGqjMB3kbVPVXLGp5m8jlGI7dJCJhWqD6wOQw1RAwT1VnYamGHgpSDX2HKbwE/cJUQ5sIUg0BRBszaD8H+I+IbMEU5rPilblSQsXs++8tKf2cObbtylhqCZ9yhw+Hzz6zgi1FRSUuFosWeWYLJyNR1cWY3/EtIrId0AMzUNwM5KVTthqLW5Adp3L++ld7wzpyJBQWWq5kKNFrJk2CFSvgsssszufBB632QB2Js5Ig/i72A0SmARuxKOpvVHVkMgRLF3l5eTpv3rz4Dnr4YTg1eOOZm2v/XLX8H6dGs99+5kN1xhmmJD/xhPmIDx1qE8DYsf79OGlBRN5R1WoptSLSTFWLEiVTTadKc/Kzz8Ixx8Brr1nuV8dxonPnnRav88QTcNxxJe0FBXavXLDAskJdd13JfXPOHDNGzZ+fFpETTXnzclWKfbyuqqep6ulYjmKnb5Bn/4YbSvvsOOlh9GhLYwOwaZM99Z57rv3A3dXCyWBE5D/AtSLysIj0SLc8NRbPYuE4sTF0qLlPjBhh98uQsHhIvXqWDi4/vyT121FHmeJcy10tqqIg9xORISLSoS5ZMSrk55/t0wuF1AzCSNxVq2y7oMAmAfDMFk6mszjwTR6ExWg40XAXC8eJjQYNzLi3ePG2hUOgxB85TGk7YYK9oakDtQaqoiCfihXcOEFE7k2wPJnJTz/ZpyvINYcws8WMGZbS5t574YorzHfKM1s4mUsfEblERI4Cfkm3MDUWD9JznNjp2xd+/3vLaBEa/EJCf+QrrrBaA1Bn3shWpVzpV0GqofGqek4yhMo4QgW5adP0yuGUZvRo+Nvf7Mf82Wf2GumOO+DII81/6osv4MwzXUl2Mok+WDae3wGtRWRKJf3rJm5BdpzYEbG4nW+/NWtyJJG1Bnr3tntnGMdTy1O/VcWCvBUROUtEuidGlAzGXSxqJuEP+7//te3nnoOuXeGpp8yC/PPPsGGDW5KdjEFVV6vq06p6taoOVNXBlR9VB3EF2XHi4+CD7Z55002WuSKSyFoDYZxVHUj9Vi0FGXgS8HdY7mJRcwkDDTp2tPQ0TzxhT8g9esCbb5q7RS1/TeTUTtxAUQEepOc48XPddbB5M/zf/227L3S1mDMHHnrIcigXF1sQfC11WYxbQRaRSyI2d1HV2QmUJzNZu9Y+t98+vXI45XPVVZbHcdMmmwSeftp+7D16eOCek6m4gaI83AfZceInJwcuugimTIH33y+9L3wjO3w4nH66/bZuvhlOO63WFuOKWUEWkZ1F5H7gJBE5X0QOBy5PnmgZxC9BrIwryDWX/Hy48UbIyoL33oMBA+DYY83dwgP3nAzBDRQxUlxs0fn1vdig48TFFVfALrtY2reydTKipX4LqYX+yDEpyCLyd1X9QVXPxMqevomVev5vEmXLHEIFuUmT9MrhVEx+vr0O2nVX8z+eNs2sx3fc4SWpnRqNGyjipLjYrceOUxV23tmyWbzwghmQohFZijrMjdy4sT2U1iIDU6wW5KtE5PogrdvewGeqer+qPpFE2TKHX3+1T1eQaz75+WYp3mUXq7J33XVw7bUlQQfubuHUMBJloBCRPiLysYgsFZFtlGsRaSQijwT73xSRnIh9o4L2j0Wkd2VjisiwoE1FpHlEe3cR+VFE3g+Wq+K5hpgpLnb/Y8epKuedZ0rw8OElBsBIQn/kK6+09bPOgj33hEsvrVVvYbNi7KdAMTAbOAh4TUQGqOoHSZMsk/jlF3vl0KBBuiVxYmHgQPjzny0/clERhGVsr7zSFORJk0yJriP15p0az1Uish2wK/Au8JaqvhPPACJSH7gT6AWsAN4WkVmqujCi2xDge1VtJyIDsEqpp4hIZ2AAkAvsAbwgIh2CY8ob83+Yj/RLUcR5VVWPi9KeOFxBdpyq07Ah3H03HHEE/OMfMG5c6f3hPfHMMy0T1OTJJaWoe/Y0xboW3DdjtSAvVtUxqvqYql4B9ANuSaJcmcUvv5j1WCTdkjixkpVlrhU77gi33AIXXggXXODuFk5NJNJA0QYzUBwQ5xgHA0tV9TNV3QBMw+bxSPoBYV7lx4CeIiJB+zRVXa+qnwNLg/HKHVNV31PVZXHKmDjWr3cF2XGqwx/+YArwP/9pb1XLkp8PGzfaby1M/Qa1yhc5VgV5jYh0CzdU9ROgRXJEykBCBdnJLAYOhH/9q8Td4sYbSxTiOlRv3qnxJMJA0RpYHrG9ImiL2kdVNwE/As0qODaWMaNxmIh8ICLPiEhutA4iMlRE5onIvNWrV8cwZBncguw41eeGG8yIdN55lu+4LJG+yFDrciPH6mJxITBNRN4BPgL2Bz5PmlSZhivImcvAgZbRYuedYfVqeOMNy5U8aZKlhBs61HyqoFa8MnIykjUi0i10q1DVT0QkUw0U7wJ7qepaETkWmIH5U5dCVScCEwHy8vK07P5KcQXZcapP8+ZmOBoyBB54wHyNIwl9kSdNsuIil11mmS8efNDqDmS4m2JMFuTA17grED4OzAEy84qTwa+/uoKcydSrB7fdZj7kt99ueR2hztSbd2o8FwL/FpF/i8hlIvIw8RsoVmLuGSHZQVvUPiKSBewEFFVwbCxjlkJVf1LVtcH600CDyCC+hOFZLBwnMZxxhrlNjBhhRqRIanlu5JjzIAf+Z0+p6vWqep+qrqvKCVMcSS0iMlZEPhGRRSJyYVVkrpRffoHttkvK0E6KOPNMuPNOW7/mGntSDn/YntnCSSMJMlC8DbQXkbYi0hALuptVps8sICxdfRJQqKoatA8I5ua2mMX3rRjHLIWItAz8mhGRg7F7UFGc11I5bkF2nMRQr54F7P30E4wcue3+8nIj14KsUNUtNR0XEZHUxwCdgfwgQjqSrZHUmJ/d9cGxkZHUfYC7RKR+JWOegVk4OqpqJyyIJPG4i0XtYMgQK0ndpo3lSV61quQVkhcScdJIdQ0UgU/xMCzQbxEwXVUXiMg1ItI36DYJaCYiS4GLCfIsq+oCYDqwEHgWuEBVN5c3JoCIXCgiKzCr8ocicl9wjpOA+SLyAXA7MCBQwhPLhg0Wie84TvXJzbUUbg88AC+/HL1P2dzIteHeqaopW4DDgNkR26OAUWX6zAYOC9azgDWAlO0b9qtoTMzK0S4eGbt166Zxc/DBqr17x3+cU/OYOlU1O1t1xx1VGzVSHTtWtW1ba1dVLSxUzc1Nr4xOxgLM0xTOubVhqdKc3LWrat++8R/nOE501q1TzclR7dRJdf36bfdPnWr3ysJC1c6dVW+6KWPuneXNyym1IJP6SOp9sDye84KI6W2CQSABEdPug1x7yM+3yN1dd7X0NePGWR5I8MwWjpMpbNxoqRwdx0kMTZpYGtRFi6L7FUf6Iy9cWNr/OBX3zrCiX/36CXPpSLWCnGoaAcWqmgfcC0yO1klVJ6pqnqrmtWhRheBw90GuXeTnw+efQ6tWsHYtTJ9ur4cmTIBnn4WcnMx8XeQ4dYVNm7xwk+Mkmj/+EU491arPvvTStvtDf+TcXFOmITX3ztClY8IEiz9IkEtHqhXkVEdSr6CkHOvjWHq6xOM+yLWTm26Cpk1h1iw45RRrO/dcOO44S2UzcGDGBh84Tq1m0ya3IDtOMrjrLmjfHk4+GT77LHqf0P/4iivgnnusLZlZocaOtXF79LAH4wRl0Ei1gpzqSOoZQI9g/Qjgk6RclSvItZP8/JLMFuPHwznn2BP0U0/BxIlWOTFTgw8cpzbjLhaOkxxCo9HmzfCnP8GPP27bJ3S3WLYMevc2t4tkZoVatMhS0UWSgIp+KVWQNcWR1MB44EQR+QgYB5ydlAtzBbn2ctppsO++5pMsArNn25NpVha0bm0//C++sDRxriQ7Ts3AXSwcJ3l06AD/+Y+ldRswwH5vZcnPN1eLF180twtIfGaL0O94yxaTKXKsuXMts0Z1iBa5V5eXuCOmN25UBdVrronvOCdzmDpVtVUr1aZN7bu+7jrVFi1sKSy06F6R0hG7FbFli+qvv9rnli3Jl9+pMeBZLJI/J6vab/O88+I/znGc2Ln3XrsnDh8efX8yM1tEjv3gg3aPbtnS1gsLY78fa/nzsr+Dqi6//mqfHqRXewkTn191leVHvuIKe8303/+ar9OcOWZN1sAveexYK7nZoQN8+SW8+aaV4Vy1yl45rV0La9ZYntZmzSzg4YwzLNG64zjVxy3IjpN8zj7b3BhuvtlqCJx/fun94b2zvMwWixaZ9begIP5y1JF+x2Bvda+4wir65eba/mqWuHYFubr88ot9uotF7SY/35YpU+zH/vPPcMEF9qro2WftR77vvtZ31Sr7kYY0bGjRuzvuCL/7nVX4ys62h6tXX7VXTnfeCbfeCr//fTquznFqFx6k5zip4YYbzNXiwgsteK9Xr9L7w3vnfvuZW8U335hrxaRJ9jsdOtS2w76xUFBgfsw9e9o9ePRoO/akk+z+Grp0VBOfQaqLK8h1i8GDTeE9/XSbFJYuNd/kNkEilSZN7En6o48seGHvveHKK81HORqqMG2alfD8wx8sGPCyy1J3PY5TG/EgPcdJDfXrw9SpFhR38smW/q1r1237hf7HqhbkDiVZoZ58suTta6jslkeY0i0nx8bJyrJxAVq2rL7fcQT+Tre6FBfbZ+PG6ZXDSR35+fDgg9C2LbzwQskPfu1aOOssWLkSHn3UFOf77rNCI+UFIojYeB9/bKnkRo2Ct95K7fU4Tm3DXSwcJ3U0bQpPPGFvSXv0KD9HctnMFpFZoQDWrTNFuU2bbe+ZYUDewIF2zz3uOFOwwVLJXXGFKcqhNToBuIJcXTZssM9GjdIrh5NaIqsGbdlir4nGjjV/5DDLRefOsedjbNLEJolWrWysaFHBjuNUjqq7WDhOqtlzT3jlFdh9d3N9GD/e7o2RlM1sEd4vX37ZHmgnT4bnnjPD4+DBFpfTpo3F6gwcaAq0iN0rn3rKFOzhw6FPH1O8E+B3HIkryNUlVJAbNkyvHE7qCasGTZ1qP9qWLS3oYNMme5Lt0SO+Eps77mg+Wh98ALfdlpprcJzaxubN9ukWZMdJLTk58Pbb5moxahT06wfff1+6T+hqMWdOyf3yhhvMzbBHD/NRXrPGFOw99rDf808/weWXmwKdlWUK9aRJNsb8+fYmNzc3ocoxuIJcfUIF2Sfjuks0a3L46ijeEpv9+9uro7//Hb76KhXSO07tYuNG+3QLsuOknqZN7T43YYLVDejY0TI1rQwKHIf3y2HD7H55+ummJI8ZY/uvusrul8OH2zEPP2z9Zs0yBXrkSFOoN22yzBhz5iTctSLEFeTq4hZkB7a1Jj/5ZOkSm7GWpxYx6/HGjXDppamT33FqC6F7kivIjpMeREwBfv116NYN/u//zAXj6KPtvlZQUGIAKiqCvfYyv+U5c8xV4pprrNAHWPBfx44lVfHGjCnJfqFaUqUvwdZj8CwW1Se0VriC7EDJj3TgQAtE6Ny5dCBC7972ZB1G3Ub7Ue+9t71OuvpqK28d5nl0HKdyQgXZ3+o5Tnrp1g2efhqWLLHA9kcfNT/lvfaylGxHHAGrV1se5aOPNstx69bWNmaMpUOdO9ferC5ZYgr0pk12vIgZpJKgGIeIFRFxQvLy8nTevHmxH/Dkk1aP/O23IS8veYI5mUWY8zH0Q54wwdpPPx122smehhs0gPvvj/4D//VX86nabjt4/32/2dcSROQdVfWJIg7inpO//dYChe64w3KVO45T8ykoMEvwwoX29mfkSKstcNllZinu3x+eecYszNnZ5maRIOW4vHnZXSyqi/sgO9GIFohwyimwfr0pyz//bP875fklb7eduVosXAi3317xucL0N/XqWbrBevUqduNw6iQi0kdEPhaRpSJyeZT9jUTkkWD/myKSE7FvVND+sYj0rmxMERkWtKmINI9oFxG5Pdj3oYgclPALdQuy42QeoZvili1WkGvGDKswW7++Kcz33APbb29W4+XLk2o5DnEFubq4i4UTjWiBe40awSOPmFX5zTdLl6eOptD+6U/bBuyVVYZFLB1O+/b2euq66+z1U/v2JWlyXFmu84hIfeBO4BigM5AvIp3LdBsCfK+q7YBbgOuDYzsDA4BcoA9wl4jUr2TM/wFHAV+UOccxQPtgGQr8K5HXCXiQnuNkOqGyvHmzKcPLl9v6/PkpUYxDXEGuLh6k55RH2cC9r76CQw4xq3JoTS6bIL1ZM8v7WL++fc6bZwVI2rSJrgw3aAB//rP5OA8cCBdfDIMG2fb48VZV6PjjXVl2DgaWqupnqroBmAb0K9OnHzAlWH8M6CkiErRPU9X1qvo5sDQYr9wxVfU9VV0WRY5+wINqvAHsLCKtEnqlHqTnOE4CcAW5uriLhVMZoTW5QQPYYQezKofW5MgE6aNGWb7HTZss88XmzeaL3KSJWaHr199WGd68Gd57zz4ff9zO9/jjNsbw4ebeMXVqibI8YUJs6eac2kZrYHnE9oqgLWofVd0E/Ag0q+DYWMasihyIyFARmSci81avXl3JkGVwFwvHcRJAyhXkVPrBRey/XUTWJuWC3ILsxEJ+vgXk5eSYkvrVV9smSJ850xTdqVNNYX74YdhlF2je3Mpab94M775bWhkO099EpsFZvNiU4blz7f9y0iRTlhcvjr2yn+OkEVWdqKp5qprXokWL+A52FwvHcRJAShXkNPjBISJ5wC5Juyj3QXZiJZpfcmSC9FCxPfxwc784/HD48kvzv7rlFutTVhnu398UgQMPtM+bbzZL84EHWpDghg02zty5VpUonsp+Tm1iJdAmYjs7aIvaR0SygJ2AogqOjWXMqshRPdyC7DhOAki1BTmlfnCB8nwjMDJpV+QWZCceyvolRyZIr1/fFN65c80FY+5cS66+117Qty+ElrSuXUuU4YcftjzL06fbw9oVV9jn9OnmhhG6VURm0Iinsp9TW3gbaC8ibUWkIWZsmFWmzyxgcLB+ElColgd0FjAgeLvXFguweyvGMcsyCzg9yGZxKPCjqn6diAvciluQHcdJAKlWkFPtBzcMmFXZBFwtfzf3QXaqQmhN3rTJEqSfdRacfba5VgwcaNuDBlkd+3XrzIK8ZYsd+8gjJcrwF19YAvUpUywjRnGxfYZpchYtsqIjYMo0mH/zdde5q0UdIphLhwGzgUXAdFVdICLXiEjfoNskoJmILAUuBi4Pjl0ATAcWAs8CF6jq5vLGBBCRC0VkBWYh/lBE7gvO8TTwGWbguBc4P+EX60F6juMkgFo7g4jIHsDJQPfK+qrqRGAiWFL6uE7kCrJTVfLzbQkTpN9zj7lBQMl606ZWj/6KK8wCXL++VSd6883YxgYbP7KyX6gUDxtmeZb328+sySlMn+OkHlV9GlNQI9uuilgvxubMaMeOBbZ5moo2ZtB+O7BNAu/AIp3c6h3uYuE4TgJItQU5lX5wBwLtgKUisgxoElhGEsvGjWapqOcJQZwqUl7Ox3B9y5YSy/CIEZb67fPP4xs/NxdefNHOAyWFTDp39swWTu3CXSwcx0kAqdbqUuYHp6pPqWpLVc1R1RzglyDwL7Fs2OCWCid1DBtmVuRbb43vuMjKftdea+PccQdceaVntnBqF25BdhwnAaT0EVtVN4lI6LNWH5gc+sEB81R1FuYH91Bg7f0OU3gJ+oV+cJsI/ODAypqWHTNlF7VhgwfoOamjdWuzCE+aZBX2dokxQUvoPjF8uLlVRCrE++1nvspbtpgV2V0tnEzGLciO4ySAlPsFqOrTqtpBVfcJ/NpQ1asC5RhVLVbVk1W1naoerKqfRRw7NjhuX1V9pqIxo5x3h6Rc0MaNriA7qeXiiy1w75574jsudOXIzTXrMZhl2TNbxEdY7rt+fa9MWBPxID3HcRKAzyDVxS3ITqo54ADLZXz77aYsx/v/F7pbqJaUug4zW7RsaVZmtyJHp6DA/n6TJpXklx4yxPb536xm4C4WjuMkAFeQq4v7IDvp4NJLoU8fy6d8xhnxHRsqcmUzW+Tn2xuRsAiJsy1jx5py3KOHbYf+2/5QUXM4/nhYuxYaN063JI7jZDCeeqG6uAXZSQdHH20FQ66+2tK/xUvZzBZhurkOHcwX2V0HorNokVmOIzn8cH+oqEnUrw/bb2+fjuM4VcQV5OriPshOOhCB8eNh2bL4fZFDIjNbPPQQXHKJpZN78EFP/RaNggJ7W9SoUekHiLlzrWKh4ziOU2twF4vq4hZkJ10cfTQceST84x8weDDstFN8x0dmtliwwIL0rruupN1dB0oIfY8vvdTcWs46y4q3LFhg254iz3Ecp1YhlmLYCcnLy9N58+bFfsDKlaYkt22bPKEcpzzefRd+8xsYOhT+9a+qj1O/vlmPGzQoqewXpn6bOtWV5P32M6t6jx6l/z4NGsD998f89xGRd1Q1L8nS1irinpMdx3HioLx52V0sqkvr1q4cO+njoIPgr3+Fu++GV1+t+jidOpmrQGgp9dRvpYn0PQ7T5RUXm4tVXX94cBzHqYW4guw4mc4115gie8458MsvVRsj9Ee+4ooSn+Yw9ZtX2St5gIjEfY8dx3FqLa4gO06ms/32cN998PHHFmhXFfLzTQletsxSvw0fXqIUDxtmvrZ1ObNFZEDjxo32OWSItTuO4zi1DleQHac20LMnjBxprhb/+U/Vxiib+g1KFMPOnetmZouwat6pp5pifPrpll83fIBw9wrHcZxaiSvIjlNb+Mc/LGDv7LPhiy+qNkakpfTaa816fMcdcOWVJUUx6oq7RaQ/dpj+rkED+Pe/S3JHO47jOLUSV5Adp7bQsCFMm2aZJ8JqYvESuloMHw4LF5YoxGEhkbrkbhFZNa9Bg7r3gOA4jlOH8TzIjlOb2HtveOQROO44GDAAHn88/lLo+fm2lE1tNnq0KciTJln7kCEl/Wsjtaxqnoj0AW4D6gP3qer4MvsbAQ8C3YAi4BRVXRbsGwUMATYDF6rq7IrGFJG2wDSgGfAOcJqqbhCRM4AbgZXBae9Q1fuSdc2Okw42btzIihUrKC4uTrcoTgSNGzcmOzubBjHeE11BdpzaRp8+5hbxl7/An/9sVuVGjeIfJ3S3mDSptLvF2LEl1tTaXEgkzFzRo0dJW4ZmrhCR+sCdQC9gBfC2iMxS1YUR3YYA36tqOxEZAFwPnCIinYEBQC6wB/CCiHQIjilvzOuBW1R1mojcHYwdJup+RFWHJfWCHSeNrFixgqZNm5KTk4OIpFscB1BVioqKWLFiBW1jTM2bchcLEekjIh+LyFIRuTzK/kYi8kiw/00RyYnYNypo/1hEelc2pog8HLTPF5HJIhKnKc1xMpTzzoPbb4cZM6BfP/jpp/jHKM/dAsy6fNRR5m5RW10talfmioOBpar6mapuwKy7/cr06QdMCdYfA3qK3d37AdNUdb2qfg4sDcaLOmZwzJHBGARjHp+8S3OcmkVxcTHNmjVz5bgGISI0a9YsLqt+ShXkCCvGMUBnID+wTkSy1YoB3IJZIihjxegD3CUi9SsZ82GgI9AF2A44O4mX5zg1i+HDLf3bCy/AoYfCJ5/EP0ZYFCM316zHUDcKiYTV8pYtg2OOMQt8ZmeuaA0sj9heEbRF7aOqm4AfMReJ8o4tr70Z8EMwRrRznSgiH4rIYyLSpjoX5Tg1FVeOax7xfieptiCnzIoBoKpPawDwFpCd5OtznJrFkCGmIK9eDQcfDDNnVm2c8gqJHHccqMLAgbUncC8ye8X69fDMMyUPApmpHNckngByVHV/4HlK5vpSiMhQEZknIvNWr16dUgEdx3Eg9QpyKq0YWwlcK04Dnq32FThOptG9O7z9NrRrZ9ktzj8//op70QqJ/PGP8NRTMHEiiNSePMm1M3vFSiDSWptNSaDcNn1EJAvYCQvWK+/Y8tqLgJ2DMUqdS1WLVHV90H4fFhC4Dao6UVXzVDWvRYsWcVym42QgYb71+vUTZmj45ptvGDBgAPvssw/dunXj2GOP5ZOqvEWsIi+99BKvvfZays6XDOpKmre7gFdU9dVoO91a4dR6cnLgtdfg0kvhX/+CvDx4/vn4xihbSGTOHFMcs7KgdWtTmr/4As48M7OV5FqWvSLgbaC9iLQVkYaYu9qsMn1mAYOD9ZOAwuDt2yxgQBAf0hZoj72RizpmcMycYAyCMWcCiEiriPP1BTL6j+o41aZsvvUEGBpUlf79+9O9e3c+/fRT3nnnHcaNG8eqVatiOn7z5s0VbseCK8jxk0orBsEYY4AWwMXlCeXWCqdO0LAh3HgjzJ5tE/HRR1vGi8JCc5OIhcjAtUWLYNMmOOUUc0WYMAF+/tnWBw+GevUy0+0izF4RSYZmrwgJ3sYNA2ZjSul0VV0gIteISN+g2ySgmYgsxebLy4NjFwDTgYXYW7gLVHVzeWMGY10GXByM1SwYG+BCEVkgIh8AFwJnJPO6HafGk4Q3VnPmzKFBgwacd955W9sOOOAANm/ezHHHHbe1bdiwYTzwwAMA5OTkcNlll3HQQQfx6KOPbrP93HPPcdhhh3HQQQdx8sknszbIs5+Tk8OYMWM46KCD6NKlC4sXL2bZsmXcfffd3HLLLXTt2pVXX41qm6z5qGrKFiyt3GdAW6Ah8AGQW6bPBcDdwfoAbNIFC877AGgUHP8Zlnuz3DGxoLzXgO1ilbFbt27qOLWe4mLVG29UbdFCFVT331/1rrtUv/uu8mOnTlXNzbXjcnJUs7NVCwtt3+jRqg0aqN50k2rnztbetq0dU9MJr0vErmH0aNUNGxJ6DcA8TeGcWxsWn5OdTGPhwoWxd65Xz+aZSDZssPYqctttt+lf//rXbdrnzJmjf/zjH7duX3DBBXr//ferqupee+2l119//dZ9kdurV6/W3//+97p27VpVVR0/frxeffXVW/vdfvvtqqp655136pAhQ1RVdcyYMXrjjTdW+RqSRbTvprx5OaUWZE29FeNuYHfgdRF5X0SuSsmFOk5Np1Ejc7f48kuzVoD5JrdqZRbhZ5+F8l6rhZktpk413+OvvoJDDjGr8g03wMiR5m6xaFHmuF2UDcwbOdKuJfOzVziOU5OpQW+sTjnllKjbb7zxBgsXLuR3v/sdXbt2ZcqUKXzxxRdb+51wwgkAdOvWjWXLlqVM3mST8kIhqvo08HSZtqsi1ouBk8s5diywzXuHaGMG7V4IxXEqonFjOOssU2Dfew8eeAAefhimT4fddoP+/eGkkyzQL6vMzylUGM88E3bYATp3NpeLMWPg6qut/4QJpjxvv725XQwaZP7KYIp1p06pyQ7x0Udw882WeePQQ0vvKyiwa9iwwZTh0aOtMErPnrY9f35yZXMcp+4SWZDp8MNNOR4ypFouFrm5uTz22GPbtGdlZbFly5at22VzAm+//fZRt1WVXr16UVCOkaNRUIiqfv36bNq0KWqfTKSuBOk5jlMRInDQQVZc5Kuv4LHHTCl+6CHo1QtatrRJ+5lnTJEMyc+H+++3IMAJE0zhnTChxJLcowdcd5351o0fD3vsYZbpTZtMWV23zlLEtWmTeAvz5s0mb+/esP/+pvQvDArHhVHj9eqZ4r5hA6xdWzpAJvMD8xzHqelEFmRq3Dghb6yOPPJI1q9fz8SJE7e2ffjhh6gqCxcuZP369fzwww+8+OKLMY136KGH8r///Y+lS5cCsG7dukozYjRt2pSff/65ytdQI4jmd1GXF/d3c5wI1q1T/e9/VQcNUm3aVBVUd9pJ9bTTVGfOVP31V+tX1n9XxI4tLCztzwvWFvoqFxaqPvecavPmtg2qjRrZ8bm58fv9rlih+vDDqj16qGZl2Xj16qnuuGPJ2FAiU+fO5i8dbquaTLm5JZ8JBPdB9jnZqfXE5YOcJFauXKknn3yy7r333tq5c2c99thj9ZNPPtERI0Zou3bttFevXtq/f/9SPsirV6/eenzZ7RdffFHz8vK0S5cu2qVLF505c+Y2/d5++2094ogjVFX1448/1i5duugBBxygr7zySmouOgbi8UFO++RX0xafjB2nHIqLVZ94QvWMM1R33tmmjx12UD32WNVx41TnzrU+U6eWVnJFSoLdwNY7d7Z2VdUHH7T2QYNKAvxyclSPP35bpTk725Z69VT32KMkyLBePfuMXJo0Ue3dW7VlS1PqW7QoUYYHDbLAu1C2SIV93TprT0JwoSvIPic7tZ+aoCA70amxQXqO42QwjRpZ5bz774dVqyyQb9Ag+OwzGDXKXBJ22MFeDx50EOy4o7lY7LEHXHQRnHqquWrMnm2uC2EQylVXmYvGe++Z68XFF9vrxSeftOPr14ejjrLzf/ONLVlZ5goS5i1v0sTS2DVubP1HjrTjCwvNNWSXXcwP+uKLzfXivffM569hQ/P5GzPGzj18ODRtau0emOc4jlNnEVOenZC8vDydN29eusVwnMxi9Wr43//grbcsIO6jj2D5cogICNmGxo1Ncf36a2jfHpYsMcV0111NCS5LvXqm/OblwZtvwtlnw4wZdsyXX8Lee1u/L7+Ejh1hwQLr37EjLF5sduUtW6zYyaJFlq2iUSNTzocNM4X5jjtKAmSSoByLyDuqmpfwgWsxPic7mcaiRYvolMF502sz0b6b8uZltyA7jlN9WrSwMtbXXQdPPGElqTduhG+/NWV59GgLxBOBnXYy5bi4GH76yZRUVdvXvj386U82ZsuW1rb33vDjj9a2aRO8/LIpunfcAWvWwMqV1u/LLy2lXMeOJcF14fqee8Jee1lb//4lGTY6dbIgwcsvL0lL55Zjx3GcOo8ryI7jJId69Uxx3m8/S5v25Zem2P7wA/z6q+VRzsmxDBJffAH9+sEvv5hS26CBpVnLyjJldccdS5TcuXNNqZ47t7TiG66HCvCcOTZm/frw/feWMePmmy2N3R//WKIUz5gBU6aYbPPnu3LsOI7jpD4PsuM4DmCKaKiMFhSY5XbZMrjiCrM+T59uiuzUqeZ2sXatWZAHDrTczYMGmULdsKEpvuH+SZPM/eKss2y8XXc1H+WVK23sDRvMH3nKFFeGHcdxnKi4BdlxnPQTVufbssVcL1RNgV2ypERpLiqyIMCsLLjnHrMMN21qbhbR9m+/vSnXRUUl/tDFxW4pdhzHcSrFFWTHcWomZZXmLVtM0V2+3DJRhOvl7Xcl2HGcOsyMGTMQERYvXry17auvvuKkk06KeYwffviBu+66K+5z5+TksGbNmgr7jBgxgo4dO7L//vvTv39/fvjhh637xo0bR7t27dh3332ZPXs2AMuXL6dHjx507tyZ3Nxcbrvttq39v/vuO3r16kX79u3p1asX33//fdwyl8UVZMdxHMdxnFpGQUEBhx9+eKkS0XvssUfUMtTlUVUFORZ69erF/Pnz+fDDD+nQoQPjxo0DYOHChUybNo0FCxbw7LPPcv7557N582aysrK46aabWLhwIW+88QZ33nknC4PqqOPHj6dnz54sWbKEnj17Mn78+GrL5z7IjuM4juM4yeCvf4X330/smF27wq23Vthl7dq1zJ07lzlz5vCnP/2Jq6++GoBly5Zx3HHHMX/+/G2OufHGG5k+fTrr16+nf//+XH311Vx++eV8+umndO3alV69enHjjTeWOub4449n+fLlFBcXc9FFFzF06NCYL+Poo4/eun7ooYduVdxnzpzJgAEDaNSoEW3btqVdu3a89dZbHHbYYbRq1QqwUtadOnVi5cqVdO7cmZkzZ/LSSy8BMHjwYLp37871118fsyzRcAXZcRzHcRynFjFz5kz69OlDhw4daNasGe+88w7dunUrt/9zzz3HkiVLeOutt1BV+vbtyyuvvML48eOZP38+75ej5E+ePJldd92VX3/9ld/85jeceOKJNGvWLG55J0+ezCmnnALAypUrOfTQQ7fuy87OZuXKlaX6L1u2jPfee49DDjkEgFWrVm1Vnlu2bMmqVavilqEsriA7juM4juMkg0osvcmioKCAiy66CIABAwZQUFBQqYL83HPPceCBBwJmgV6yZAl77rlnhee5/fbbefzxxwHzEV6yZEncCvLYsWPJyspi0KBBMfVfu3YtJ554Irfeeis77rjjNvtFBBGJS4ZouILsOI7jOI5TS/juu+8oLCzko48+QkTYvHkzIrKNe0QkqsqoUaM499xzS7UvW7as3GNeeuklXnjhBV5//XWaNGlC9+7dKS4ujkvWBx54gCeffJIXX3xxq1LbunVrli9fvrXPihUraN26NQAbN27kxBNPZNCgQZxwwglb++y+++58/fXXtGrViq+//prddtstLjmikfIgPRHpIyIfi8hSEbk8yv5GIvJIsP9NEcmJ2DcqaP9YRHpXNqaItA3GWBqM2TChF1NQYEUQ6te3zwhHeMdxnJpETZh7KzpHQvA52XF47LHHOO200/jiiy9YtmwZy5cvp23btrz66qvlHtO7d28mT57M2rVrAXNz+Pbbb2natCk///xz1GN+/PFHdtllF5o0acLixYt544034pLz2Wef5YYbbmDWrFk0adJka3vfvn2ZNm0a69ev5/PPP2fJkiUcfPDBqCpDhgyhU6dOXHzxxaXG6tu3L1OmTAFgypQp9OvXLy5ZoqKqKVuA+sCnwN5AQ+ADoHOZPucDdwfrA4BHgvXOQf9GQNtgnPoVjQlMBwYE63cDf6lMxm7dumlMTJ2q2ratamGh6oYN9tm2rbU7juNEAZinKZxzw6WmzL3lnaOixedkJ9NYuHBhWs/fvXt3feaZZ0q13XbbbXreeefp559/rrm5uVGPu/XWW3W//fbT/fbbTw899FBdunSpqqrm5+drbm6uXnrppaX6FxcXa58+fbRjx47ar18/PeKII3TOnDmqqrrXXnvp6tWrVVX1mGOO0ZUrV25zvn322Uezs7P1gAMO0AMOOEDPPffcrfuuvfZa3XvvvbVDhw769NNPq6rqq6++qoB26dJl6zFPPfWUqqquWbNGjzzySG3Xrp327NlTi4qKol5jtO+mvHk51ZP0YcDsiO1RwKgyfWYDhwXrWcAaQMr2DfuVN2ZwzBogK9q5y1tinoxzc20CjqSw0Nodx3GikEYFuUbMveWdoyLZfU52Mo10K8hO+cSjIKfaxaI1sDxie0XQFrWPqm4CfgSaVXBsee3NgB+CMco7FwAiMlRE5onIvNWrV8d2JYsWweGHl247/HBrdxzHqVnUlLm3vHOUwudkx3HSjRcKAVR1oqrmqWpeixYtYjuoUyeYO7d029y51u44juNUGZ+THcdJN6lWkFcCbSK2s4O2qH1EJAvYCSiq4Njy2ouAnYMxyjtX1Rk9GoYMgTlzYONG+xwyxNodx3FqFjVl7i3vHNXH52SnBmFv7p2aRLzfSarTvL0NtBeRtthEOQAYWKbPLGAw8DpwElCoqiois4CpInIzsAfQHngL83fbZszgmDnBGNOCMWcm7Ery8+1z+HB7hdepE4wdW9LuOI5Tc6gpc2/UcyTkCn1OdmoIjRs3pqioiGbNmiUkH69TfVSVoqIiGjduHPMxKVWQVXWTiAzDAjXqA5NVdYGIXIM5Sc8CJgEPichS4Dts0iXoNx1YCGwCLlDVzQDRxgxOeRkwTUSuBd4Lxk4c+fk++TqOU+OpQXNv1HMkDJ+TnRpAdnY2K1asIGb/eSclNG7cmOzs7Jj7i78GKE1eXp7Omzcv3WI4jlMLEZF3VDUv3XJkEj4nO46TTMqblz1Iz3Ecx3Ecx3EicAXZcRzHcRzHcSJwBdlxHMdxHMdxInAf5DKIyGrgizgPa45VhMpUMl1+yPxryHT5IfOvIRXy76WqMSb2daDOzsmQ+deQ6fJD5l9DpssPaZyXXUFOACIyL5MDbzJdfsj8a8h0+SHzryHT5XdKqA3fZaZfQ6bLD5l/DZkuP6T3GtzFwnEcx3Ecx3EicAXZcRzHcRzHcSJwBTkxTEy3ANUk0+WHzL+GTJcfMv8aMl1+p4Ta8F1m+jVkuvyQ+deQ6fJDGq/BfZAdx3Ecx3EcJwK3IDuO4ziO4zhOBK4gO47jOI7jOE4EriDHgYj0EZGPRWSpiFweZX8jEXkk2P+miOSkQcxyiUH+P4jIuyKySUROSoeMlRHDNVwsIgtF5EMReVFE9kqHnOURg/znichHIvK+iMwVkc7pkLMiKruGiH4nioiKSI1KMxTDd3CGiKwOvoP3ReTsdMjpVI7PyenH5+T043NyklBVX2JYgPrAp8DeQEPgA6BzmT7nA3cH6wOAR9Itd5zy5wD7Aw8CJ6Vb5ipeQw+gSbD+lwz8DnaMWO8LPJtuueO9hqBfU+AV4A0gL91yx/kdnAHckW5ZfUnId+lzcvqvwefkNF9D0M/n5DgXtyDHzsHAUlX9TFU3ANOAfmX69AOmBOuPAT1FRFIoY0VUKr+qLlPVD4Et6RAwBmK5hjmq+kuw+QaQnWIZKyIW+X+K2NweqGlRtLH8DgD+AVwPFKdSuBiIVX6n5uNzcvrxOTn9+JycJFxBjp3WwPKI7RVBW9Q+qroJ+BFolhLpKicW+Ws68V7DEOCZpEoUHzHJLyIXiMinwA3AhSmSLVYqvQYROQhoo6pPpVKwGIn1f+jE4JXwYyLSJjWiOXHic3L68Tk5/ficnCRcQXZqJSJyKpAH3JhuWeJFVe9U1X2Ay4Ar0y1PPIhIPeBm4JJ0y1INngByVHV/4HlKLJCO41QRn5PTg8/JVccV5NhZCUQ+tWQHbVH7iEgWsBNQlBLpKicW+Ws6MV2DiBwFjAb6qur6FMkWC/F+B9OA45MpUBWo7BqaAvsBL4nIMuBQYFYNCgqp9DtQ1aKI/5v7gG4pks2JD5+T04/PyenH5+Qk4Qpy7LwNtBeRtiLSEAv4mFWmzyxgcLB+ElCogYd5DSAW+Ws6lV6DiBwI3INNxN+mQcaKiEX+9hGbfwSWpFC+WKjwGlT1R1Vtrqo5qpqD+Rz2VdV56RF3G2L5DlpFbPYFFqVQPid2fE5OPz4npx+fk5NFOqIWM3UBjgU+wSIuRwdt12D/bACNgUeBpcBbwN7pljlO+X+D+f+sw6wsC9ItcxWu4QVgFfB+sMxKt8xxyn8bsCCQfQ6Qm26Z472GMn1fogZFTMf4HYwLvoMPgu+gY7pl9qXK36XPyem/Bp+T03wNZfr6nBzj4qWmHcdxHMdxHCcCd7FwHMdxHMdxnAhcQXYcx3Ecx3GcCFxBdhzHcRzHcZwIXEF2HMdxHMdxnAhcQXYcx3Ecx3GcCFxBduoMItJMRN4Plm9EZGWwvlZE7krC+R4Qkc9F5LyI7ZOi9NsnlCPRMjiO49RkfF52aipZ6RbAcVKFqhYBXQFE5O/AWlX9Z5JPO0JVH6tErk+Brj4RO45T1/B52ampuAXZqfOISHcReTJY/7uITBGRV0XkCxE5QURuEJGPRORZEWkQ9OsmIi+LyDsiMrtMpZ+K+IOIvCYin0WzWjiO4zg+LzvpxxVkx9mWfYAjsZKW/wbmqGoX4Ffgj8FkPAE4SVW7AZOBsTGO3Qo4HDgOGJ9owR3HcWopPi87KcVdLBxnW55R1Y0i8hFQH3g2aP8IyAH2BfYDnhcRgj5fxzj2DFXdAiwUkd0TKrXjOE7txedlJ6W4guw427IeQFW3iMhGLanHvgX7zQiwQFUPq+rYAVI9MR3HceoMPi87KcVdLBwnfj4GWojIYQAi0kBEctMsk+M4Tl3G52UnobiC7DhxoqobgJOA60XkA+B94LdpFcpxHKcO4/Oyk2ik5C2F4ziJREQeAJ6sLJ1QRP+1qrpDcqVyHMepu/i87MSKW5AdJ3n8CPwjTEhfHmFCemBVSqRyHMepu/i87MSEW5Adx3Ecx3EcJwK3IDuO4ziO4zhOBK4gO47jOI7jOE4EriA7juM4juM4TgSuIDuO4ziO4zhOBK4gO47jOI7jOE4E/w8jsbkFn/WX3QAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 720x252 with 2 Axes>"
       ]
@@ -264,10 +264,10 @@
     "data_st_p_2C=pd.read_csv (path + \"stp_2C.txt\", delimiter= ',',header=3)\n",
     "\n",
     "f, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,3.5))\n",
+    "\n",
     "ax1.plot(t_all2C, st_surf_n[-1,:],'ro',markerfacecolor='none',label=\"Current\")\n",
     "ax1.plot(data_st_n_2C.values[:,0]/3600, data_st_n_2C.values[:,1],'r-',label=\"Ai et al. 2020\")\n",
     "ax1.legend()\n",
-    "#plt.xlim(0, 3600);\n",
     "ax1.set_xlabel(\"Time [h]\")\n",
     "ax1.set_ylabel(\"$\\sigma_{t,n}/E_n$\")\n",
     "\n",
@@ -276,9 +276,8 @@
     "ax2.legend()\n",
     "ax2.set_xlabel(\"Time [h]\")\n",
     "ax2.set_ylabel(\"$\\sigma_{t,p}/E_p$\")\n",
-    "#plt.xlim(0, 3600);\n",
+    "\n",
     "f.tight_layout()\n",
-    "#f.set_title(\"particle surface tangential stress close to the separator\")\n",
     "f.show()"
    ]
   },
@@ -305,8 +304,8 @@
       "[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\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] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
-      "[7] Robert Timms, Scott G. Marquis, Valentin Sulzer, Colin P. Please, and S. Jon Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. Submitted for publication, 2020. arXiv:2005.05127.\n",
+      "[6] 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",
+      "[7] Robert Timms, Scott G Marquis, Valentin Sulzer, Colin P. Please, and S Jonathan Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. SIAM Journal on Applied Mathematics, 81(3):765–788, 2021. doi:10.1137/20M1336898.\n",
       "\n"
      ]
     }
@@ -339,7 +338,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.12"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/examples/notebooks/models/compare-particle-diffusion-models.ipynb b/examples/notebooks/models/compare-particle-diffusion-models.ipynb
index 37379deb2d..f5fb780005 100644
--- a/examples/notebooks/models/compare-particle-diffusion-models.ipynb
+++ b/examples/notebooks/models/compare-particle-diffusion-models.ipynb
@@ -105,13 +105,13 @@
      "output_type": "stream",
      "text": [
       "Particle model: Fickian diffusion\n",
-      "Solve time: 544.547 mss\n",
+      "Solve time: 344.798 mss\n",
       "Particle model: uniform profile\n",
-      "Solve time: 238.542 mss\n",
+      "Solve time: 207.218 mss\n",
       "Particle model: quadratic profile\n",
-      "Solve time: 253.457 mss\n",
+      "Solve time: 220.810 mss\n",
       "Particle model: quartic profile\n",
-      "Solve time: 272.754 mss\n"
+      "Solve time: 284.064 mss\n"
      ]
     }
    ],
@@ -138,7 +138,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1080x1080 with 1 Axes>"
       ]
@@ -189,7 +189,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1080x1080 with 1 Axes>"
       ]
@@ -239,7 +239,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1080x1080 with 1 Axes>"
       ]
@@ -279,7 +279,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d9803cc24df446a0841979e41fe452bd",
+       "model_id": "44dc456f1b0646f4a32b1780a04a8bbf",
        "version_major": 2,
        "version_minor": 0
       },
@@ -318,7 +318,7 @@
       "[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] Venkat R. Subramanian, Vinten D. Diwakar, and Deepak Tapriyal. Efficient macro-micro scale coupled modeling of batteries. Journal of The Electrochemical Society, 152(10):A2002, 2005. doi:10.1149/1.2032427.\n",
-      "[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n",
+      "[6] 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",
       "\n"
      ]
     }
@@ -326,6 +326,13 @@
    "source": [
     "pybamm.print_citations()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -344,7 +351,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.12"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb b/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb
index b50d7c9ab7..d79736f897 100644
--- a/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb
+++ b/examples/notebooks/models/submodel_cracking_DFN_or_SPM.ipynb
@@ -14,11 +14,9 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1.2 is available.\n",
-      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -88,16 +86,18 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "display_data",
      "data": {
-      "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…",
       "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52f025d6be294882b5d4f9eedb474c34",
        "version_major": 2,
-       "version_minor": 0,
-       "model_id": "653353cedea84635a916a217d9826e3c"
-      }
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -125,16 +125,18 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "display_data",
      "data": {
-      "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=3600.0, step=10.0), Output()), _dom_classes=…",
       "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a4c62376c474449863e4837e971c34a",
        "version_major": 2,
-       "version_minor": 0,
-       "model_id": "a5ed452d11ce494d83f30efec6b6abb4"
-      }
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=3600.0, step=10.0), Output()), _dom_classes=…"
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -190,16 +192,18 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "display_data",
      "data": {
-      "text/plain": "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…",
       "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70f30cf8e80d4a7a9c671844decddcdb",
        "version_major": 2,
-       "version_minor": 0,
-       "model_id": "dbe2842d65ad4cbd9d0efcaf97a50b57"
-      }
+       "version_minor": 0
+      },
+      "text/plain": [
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
+      ]
      },
-     "metadata": {}
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -229,16 +233,29 @@
    "metadata": {},
    "outputs": [
     {
-     "output_type": "stream",
      "name": "stdout",
+     "output_type": "stream",
      "text": [
-      "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n[2] 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[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\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] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n\n"
+      "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n",
+      "[2] 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",
+      "[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\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] 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",
+      "\n"
      ]
     }
    ],
    "source": [
     "pybamm.print_citations()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -257,7 +274,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.12"
   },
   "toc": {
    "base_numbering": 1,
@@ -275,4 +292,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
\ No newline at end of file
+}
diff --git a/examples/notebooks/models/using-submodels.ipynb b/examples/notebooks/models/using-submodels.ipynb
index db73379159..4c52a11866 100644
--- a/examples/notebooks/models/using-submodels.ipynb
+++ b/examples/notebooks/models/using-submodels.ipynb
@@ -27,8 +27,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[33mWARNING: You are using pip version 21.1.3; however, version 21.2.4 is available.\n",
-      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/venv/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
       "Note: you may need to restart the kernel to use updated packages.\n"
      ]
     }
@@ -70,30 +68,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "external circuit <pybamm.models.submodels.external_circuit.current_control_external_circuit.CurrentControl object at 0x14c02e520>\n",
-      "porosity <pybamm.models.submodels.porosity.constant_porosity.Constant object at 0x14c0b6f70>\n",
-      "negative active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x14c0a4970>\n",
-      "positive active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x14c09bd90>\n",
-      "electrolyte tortuosity <pybamm.models.submodels.tortuosity.bruggeman_tortuosity.Bruggeman object at 0x14c0b6eb0>\n",
-      "electrode tortuosity <pybamm.models.submodels.tortuosity.bruggeman_tortuosity.Bruggeman object at 0x14c0b1d00>\n",
-      "through-cell convection <pybamm.models.submodels.convection.through_cell.no_convection.NoConvection object at 0x14c0ca250>\n",
-      "transverse convection <pybamm.models.submodels.convection.transverse.no_convection.NoConvection object at 0x14c0ca310>\n",
-      "negative interface <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x14c0ca3d0>\n",
-      "positive interface <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x14c0ca490>\n",
-      "negative interface current <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x14c0ca550>\n",
-      "positive interface current <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x14c0ca610>\n",
-      "negative oxygen interface <pybamm.models.submodels.interface.kinetics.no_reaction.NoReaction object at 0x14c0ca6d0>\n",
-      "positive oxygen interface <pybamm.models.submodels.interface.kinetics.no_reaction.NoReaction object at 0x14c0ca790>\n",
-      "negative particle <pybamm.models.submodels.particle.no_distribution.x_averaged_fickian_diffusion.XAveragedFickianDiffusion object at 0x14c0bfe80>\n",
-      "positive particle <pybamm.models.submodels.particle.no_distribution.x_averaged_fickian_diffusion.XAveragedFickianDiffusion object at 0x14c0ca820>\n",
-      "negative electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x14c0bfd90>\n",
-      "leading-order electrolyte conductivity <pybamm.models.submodels.electrolyte_conductivity.leading_order_conductivity.LeadingOrder object at 0x14c0ca970>\n",
-      "electrolyte diffusion <pybamm.models.submodels.electrolyte_diffusion.constant_concentration.ConstantConcentration object at 0x14c0caa30>\n",
-      "positive electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x14c0caaf0>\n",
-      "thermal <pybamm.models.submodels.thermal.isothermal.Isothermal object at 0x14c0cabb0>\n",
-      "current collector <pybamm.models.submodels.current_collector.homogeneous_current_collector.Uniform object at 0x14c0cac70>\n",
-      "negative sei <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x14c0cad30>\n",
-      "negative lithium plating <pybamm.models.submodels.interface.lithium_plating.no_plating.NoPlating object at 0x14c0cadf0>\n"
+      "external circuit <pybamm.models.submodels.external_circuit.current_control_external_circuit.CurrentControl object at 0x7ff71938da30>\n",
+      "porosity <pybamm.models.submodels.porosity.constant_porosity.Constant object at 0x7ff7192ac670>\n",
+      "negative active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x7ff7192ac7c0>\n",
+      "positive active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x7ff7192ac8e0>\n",
+      "electrolyte tortuosity <pybamm.models.submodels.tortuosity.bruggeman_tortuosity.Bruggeman object at 0x7ff7192ac790>\n",
+      "electrode tortuosity <pybamm.models.submodels.tortuosity.bruggeman_tortuosity.Bruggeman object at 0x7ff7192acaf0>\n",
+      "through-cell convection <pybamm.models.submodels.convection.through_cell.no_convection.NoConvection object at 0x7ff7192acc10>\n",
+      "transverse convection <pybamm.models.submodels.convection.transverse.no_convection.NoConvection object at 0x7ff7192acd30>\n",
+      "negative interface <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x7ff7192ace50>\n",
+      "positive interface <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x7ff7192a4490>\n",
+      "negative interface current <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x7ff719315c10>\n",
+      "positive interface current <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x7ff7192ac3a0>\n",
+      "negative oxygen interface <pybamm.models.submodels.interface.kinetics.no_reaction.NoReaction object at 0x7ff7192acee0>\n",
+      "positive oxygen interface <pybamm.models.submodels.interface.kinetics.no_reaction.NoReaction object at 0x7ff7192be040>\n",
+      "negative particle <pybamm.models.submodels.particle.no_distribution.x_averaged_fickian_diffusion.XAveragedFickianDiffusion object at 0x7ff7192be190>\n",
+      "positive particle <pybamm.models.submodels.particle.no_distribution.x_averaged_fickian_diffusion.XAveragedFickianDiffusion object at 0x7ff7192be2b0>\n",
+      "negative electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x7ff7192be160>\n",
+      "positive electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x7ff7192be4c0>\n",
+      "leading-order electrolyte conductivity <pybamm.models.submodels.electrolyte_conductivity.leading_order_conductivity.LeadingOrder object at 0x7ff7192be5e0>\n",
+      "electrolyte diffusion <pybamm.models.submodels.electrolyte_diffusion.constant_concentration.ConstantConcentration object at 0x7ff7192be700>\n",
+      "thermal <pybamm.models.submodels.thermal.isothermal.Isothermal object at 0x7ff7192be820>\n",
+      "current collector <pybamm.models.submodels.current_collector.homogeneous_current_collector.Uniform object at 0x7ff7192be940>\n",
+      "sei <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x7ff7192bea60>\n",
+      "lithium plating <pybamm.models.submodels.interface.lithium_plating.no_plating.NoPlating object at 0x7ff7192beb80>\n"
      ]
     }
    ],
@@ -131,7 +129,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.submodels[\"negative particle\"] = pybamm.particle.no_distribution.XAveragedPolynomialProfile(model.param, \"Negative\",\"uniform profile\")"
+    "model.submodels[\"negative particle\"] = pybamm.particle.no_distribution.XAveragedPolynomialProfile(model.param, \"Negative\",\"uniform profile\", options=model.options)"
    ]
   },
   {
@@ -157,30 +155,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "external circuit <pybamm.models.submodels.external_circuit.current_control_external_circuit.CurrentControl object at 0x14c40c670>\n",
-      "porosity <pybamm.models.submodels.porosity.constant_porosity.Constant object at 0x14c49c370>\n",
-      "negative active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x14c49c790>\n",
-      "positive active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x14c49c850>\n",
-      "electrolyte tortuosity <pybamm.models.submodels.tortuosity.bruggeman_tortuosity.Bruggeman object at 0x14c49c0a0>\n",
-      "electrode tortuosity <pybamm.models.submodels.tortuosity.bruggeman_tortuosity.Bruggeman object at 0x14c49c9a0>\n",
-      "through-cell convection <pybamm.models.submodels.convection.through_cell.no_convection.NoConvection object at 0x14c49ca60>\n",
-      "transverse convection <pybamm.models.submodels.convection.transverse.no_convection.NoConvection object at 0x14c49cb20>\n",
-      "negative interface <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x14c49cbe0>\n",
-      "positive interface <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x14c49cca0>\n",
-      "negative interface current <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x14c49cd60>\n",
-      "positive interface current <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x14c49ce20>\n",
-      "negative oxygen interface <pybamm.models.submodels.interface.kinetics.no_reaction.NoReaction object at 0x14c49cee0>\n",
-      "positive oxygen interface <pybamm.models.submodels.interface.kinetics.no_reaction.NoReaction object at 0x14c49cfa0>\n",
-      "negative particle <pybamm.models.submodels.particle.no_distribution.x_averaged_polynomial_profile.XAveragedPolynomialProfile object at 0x14c3f36d0>\n",
-      "positive particle <pybamm.models.submodels.particle.no_distribution.x_averaged_fickian_diffusion.XAveragedFickianDiffusion object at 0x14c496d00>\n",
-      "negative electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x14c48da90>\n",
-      "leading-order electrolyte conductivity <pybamm.models.submodels.electrolyte_conductivity.leading_order_conductivity.LeadingOrder object at 0x14c4b2160>\n",
-      "electrolyte diffusion <pybamm.models.submodels.electrolyte_diffusion.constant_concentration.ConstantConcentration object at 0x14c4b2220>\n",
-      "positive electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x14c4b22e0>\n",
-      "thermal <pybamm.models.submodels.thermal.isothermal.Isothermal object at 0x14c4b23a0>\n",
-      "current collector <pybamm.models.submodels.current_collector.homogeneous_current_collector.Uniform object at 0x14c4b2460>\n",
-      "negative sei <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x14c4b2520>\n",
-      "negative lithium plating <pybamm.models.submodels.interface.lithium_plating.no_plating.NoPlating object at 0x14c4b25e0>\n"
+      "external circuit <pybamm.models.submodels.external_circuit.current_control_external_circuit.CurrentControl object at 0x7ff778121550>\n",
+      "porosity <pybamm.models.submodels.porosity.constant_porosity.Constant object at 0x7ff718ed38b0>\n",
+      "negative active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x7ff718ed3280>\n",
+      "positive active material <pybamm.models.submodels.active_material.constant_active_material.Constant object at 0x7ff718ed3f40>\n",
+      "electrolyte tortuosity <pybamm.models.submodels.tortuosity.bruggeman_tortuosity.Bruggeman object at 0x7ff718ed3370>\n",
+      "electrode tortuosity <pybamm.models.submodels.tortuosity.bruggeman_tortuosity.Bruggeman object at 0x7ff718edc190>\n",
+      "through-cell convection <pybamm.models.submodels.convection.through_cell.no_convection.NoConvection object at 0x7ff718edc2b0>\n",
+      "transverse convection <pybamm.models.submodels.convection.transverse.no_convection.NoConvection object at 0x7ff718edc3d0>\n",
+      "negative interface <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x7ff718edc4f0>\n",
+      "positive interface <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.InverseButlerVolmer object at 0x7ff718edc610>\n",
+      "negative interface current <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x7ff718edc730>\n",
+      "positive interface current <pybamm.models.submodels.interface.inverse_kinetics.inverse_butler_volmer.CurrentForInverseButlerVolmer object at 0x7ff718edc850>\n",
+      "negative oxygen interface <pybamm.models.submodels.interface.kinetics.no_reaction.NoReaction object at 0x7ff718edc970>\n",
+      "positive oxygen interface <pybamm.models.submodels.interface.kinetics.no_reaction.NoReaction object at 0x7ff718edca90>\n",
+      "negative particle <pybamm.models.submodels.particle.no_distribution.x_averaged_polynomial_profile.XAveragedPolynomialProfile object at 0x7ff718f45ee0>\n",
+      "positive particle <pybamm.models.submodels.particle.no_distribution.x_averaged_fickian_diffusion.XAveragedFickianDiffusion object at 0x7ff718edcbb0>\n",
+      "negative electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x7ff718ec61f0>\n",
+      "positive electrode potential <pybamm.models.submodels.electrode.ohm.leading_ohm.LeadingOrder object at 0x7ff718edcdc0>\n",
+      "leading-order electrolyte conductivity <pybamm.models.submodels.electrolyte_conductivity.leading_order_conductivity.LeadingOrder object at 0x7ff718edcee0>\n",
+      "electrolyte diffusion <pybamm.models.submodels.electrolyte_diffusion.constant_concentration.ConstantConcentration object at 0x7ff718e70040>\n",
+      "thermal <pybamm.models.submodels.thermal.isothermal.Isothermal object at 0x7ff718e70160>\n",
+      "current collector <pybamm.models.submodels.current_collector.homogeneous_current_collector.Uniform object at 0x7ff718e70280>\n",
+      "sei <pybamm.models.submodels.interface.sei.no_sei.NoSEI object at 0x7ff718e703a0>\n",
+      "lithium plating <pybamm.models.submodels.interface.lithium_plating.no_plating.NoPlating object at 0x7ff718e704c0>\n"
      ]
     }
    ],
@@ -247,9 +245,9 @@
     {
      "data": {
       "text/plain": [
-       "{Variable(0x52e60f6b8b1ed1db, Discharge capacity [A.h], children=[], domain=[], auxiliary_domains={}): Division(0x418cff78eab51419, /, children=['Current function [A] * 96485.33212 * Maximum concentration in negative electrode [mol.m-3] * (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) / absolute(Typical current [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]))', '3600.0'], domain=[], auxiliary_domains={}),\n",
-       " Variable(0x2cfd9d4ec9544484, R-X-averaged negative particle concentration, children=[], domain=['current collector'], auxiliary_domains={}): MatrixMultiplication(-0x358ad09406cf7a4d, @, children=['mass(R-X-averaged negative particle concentration)', 'broadcast(-3.0 * (Current function [A] / Typical current [A]) * sign(Typical current [A]) / (Negative electrode thickness [m] / (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m])) / ((3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) * Negative particle radius [m]))'], domain=['current collector'], auxiliary_domains={}),\n",
-       " Variable(0x2df5f526df41e54e, X-averaged positive particle concentration, children=[], domain=['positive particle'], auxiliary_domains={'secondary': \"['current collector']\"}): Multiplication(0x36628bd5f76766a, *, children=['1.0 / ((Positive particle radius [m] ** 2.0) / Positive electrode diffusivity [m2.s-1] / (96485.33212 * Maximum concentration in negative electrode [mol.m-3] * (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) / absolute(Typical current [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]))))', 'div((Positive electrode diffusivity [m2.s-1] / Positive electrode diffusivity [m2.s-1]) * grad(X-averaged positive particle concentration))'], domain=['positive particle'], auxiliary_domains={'secondary': \"['current collector']\"})}"
+       "{Variable(-0x19f40e5fcd849ac6, Discharge capacity [A.h], children=[], domain=[], auxiliary_domains={}): Division(-0x1329b359c297ed13, /, children=['Current function [A] * 96485.33212 * Maximum concentration in negative electrode [mol.m-3] * (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) / absolute(Typical current [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]))', '3600.0'], domain=[], auxiliary_domains={}),\n",
+       " Variable(0x19207073dde86b7b, Average negative particle concentration, children=[], domain=['current collector'], auxiliary_domains={}): MatrixMultiplication(0x686fe2dc0860b74f, @, children=['mass(Average negative particle concentration)', \"-3.0 * (Current function [A] / Typical current [A]) * sign(Typical current [A]) / (((3.0 / integral dx ['negative electrode'](broadcast(1.0))) * integral dx ['negative electrode'](Negative electrode active material volume fraction) / (integral dx ['negative electrode'](Negative particle radius [m]) / integral dx ['negative electrode'](broadcast(1.0))) / (3.0 * Negative electrode active material volume fraction / Negative particle radius [m])) * Negative electrode thickness [m] / (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m])) / ((3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) * Negative particle radius [m])\"], domain=['current collector'], auxiliary_domains={}),\n",
+       " Variable(-0x89b5e76ae887eea, X-averaged positive particle concentration, children=[], domain=['positive particle'], auxiliary_domains={'secondary': \"['current collector']\"}): Multiplication(0x19b923ecd5b5cdb8, *, children=['1.0 / ((Positive particle radius [m] ** 2.0) / Positive electrode diffusivity [m2.s-1] / (96485.33212 * Maximum concentration in negative electrode [mol.m-3] * (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) / absolute(Typical current [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]))))', 'div((Positive electrode diffusivity [m2.s-1] / Positive electrode diffusivity [m2.s-1]) * grad(X-averaged positive particle concentration))'], domain=['positive particle'], auxiliary_domains={'secondary': \"['current collector']\"})}"
       ]
      },
      "execution_count": 9,
@@ -276,7 +274,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9c9c7cca6c9c47b3b41e502c6391ef1d",
+       "model_id": "3ed77b36ebf74bd1918a358a21def08c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -290,24 +288,12 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.plotting.quick_plot.QuickPlot at 0x14c291310>"
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x7ff71860ca30>"
       ]
      },
      "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1080x504 with 8 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -412,10 +398,10 @@
    "outputs": [],
    "source": [
     "model.submodels[\"negative particle\"] = pybamm.particle.no_distribution.PolynomialProfile(\n",
-    "    model.param, \"Negative\", \"uniform profile\"\n",
+    "    model.param, \"Negative\", \"uniform profile\", options=model.options\n",
     ")\n",
     "model.submodels[\"positive particle\"] = pybamm.particle.no_distribution.PolynomialProfile(\n",
-    "    model.param, \"Positive\", \"uniform profile\"\n",
+    "    model.param, \"Positive\", \"uniform profile\", options=model.options\n",
     ")"
    ]
   },
@@ -523,7 +509,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "024360bcd54142b9a6c705134912998b",
+       "model_id": "6a49f0c6a6524b39a413496b066e70eb",
        "version_major": 2,
        "version_minor": 0
       },
@@ -537,24 +523,12 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.plotting.quick_plot.QuickPlot at 0x14c7af190>"
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x7ff71866ab50>"
       ]
      },
      "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 1080x504 with 8 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -604,7 +578,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -618,7 +592,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.8.12"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/examples/scripts/custom_model.py b/examples/scripts/custom_model.py
index 3a65d32122..2a2a461be8 100644
--- a/examples/scripts/custom_model.py
+++ b/examples/scripts/custom_model.py
@@ -30,11 +30,11 @@
     model.param, "Positive"
 )
 particle_n = pybamm.particle.no_distribution.XAveragedPolynomialProfile(
-    model.param, "Negative", "uniform profile"
+    model.param, "Negative", "uniform profile", options=model.options
 )
 model.submodels["negative particle"] = particle_n
 particle_p = pybamm.particle.no_distribution.XAveragedPolynomialProfile(
-    model.param, "Positive", "uniform profile"
+    model.param, "Positive", "uniform profile", options=model.options
 )
 model.submodels["positive particle"] = particle_p
 model.submodels["negative interface"] = pybamm.interface.InverseButlerVolmer(
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 2a98f67f9e..55acb07bf8 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -124,6 +124,11 @@ class BatteryModelOptions(pybamm.FuzzyDict):
             * "SEI porosity change" : str
                 Whether to include porosity change due to SEI formation, can be "false"
                 (default) or "true".
+            * "stress-induced diffusion" : str
+                Whether to include stress-induced diffusion, can be "false" or "true".
+                The default is "false" if "particle mechanics" is "none" and "true"
+                otherwise. A 2-tuple can be provided for different behaviour in negative
+                and positive electrodes.
             * "surface form" : str
                 Whether to use the surface formulation of the problem. Can be "false"
                 (default), "differential" or "algebraic".
@@ -186,6 +191,7 @@ def __init__(self, extra_options):
             ],
             "SEI film resistance": ["none", "distributed", "average"],
             "SEI porosity change": ["true", "false"],
+            "stress-induced diffusion": ["true", "false"],
             "surface form": ["false", "differential", "algebraic"],
             "thermal": ["isothermal", "lumped", "x-lumped", "x-full"],
             "total interfacial current density as a state": ["true", "false"],
@@ -238,7 +244,7 @@ def __init__(self, extra_options):
         # The "SEI film resistance" option will still be overridden by extra_options if
         # provided
 
-        # Change the default for swelling based on which LAM option is
+        # Change the default for particle mechanics based on which LAM option is
         # provided
         # return "none" if option not given
         lam_option = extra_options.get("loss of active material", "none")
@@ -246,9 +252,19 @@ def __init__(self, extra_options):
             default_options["particle mechanics"] = "swelling only"
         else:
             default_options["particle mechanics"] = "none"
-        # The "SEI film resistance" option will still be overridden by extra_options if
+        # The "particle mechanics" option will still be overridden by extra_options if
         # provided
 
+        # Change the default for stress-induced diffusion based on which particle
+        # mechanics option is provided
+        mechanics_option = extra_options.get("particle mechanics", "none")
+        if mechanics_option == "none":
+            default_options["stress-induced diffusion"] = "false"
+        else:
+            default_options["stress-induced diffusion"] = "true"
+        # The "stress-induced diffusion" option will still be overridden by
+        # extra_options if provided
+
         options = pybamm.FuzzyDict(default_options)
         # any extra options overwrite the default options
         for name, opt in extra_options.items():
@@ -283,15 +299,16 @@ def __init__(self, extra_options):
 
         # Options not yet compatible with particle-size distributions
         if options["particle size"] == "distribution":
-            if options["SEI"] != "none":
-                raise NotImplementedError(
-                    "SEI submodels do not yet support particle-size distributions."
-                )
             if options["lithium plating"] != "none":
                 raise NotImplementedError(
                     "Lithium plating submodels do not yet support particle-size "
                     "distributions."
                 )
+            if options["particle"] in ["quadratic profile", "quartic profile"]:
+                raise NotImplementedError(
+                    "'quadratic' and 'quartic' concentration profiles have not yet "
+                    "been implemented for particle-size ditributions"
+                )
             if options["particle mechanics"] != "none":
                 raise NotImplementedError(
                     "Particle mechanics submodels do not yet support particle-size"
@@ -299,22 +316,37 @@ def __init__(self, extra_options):
                 )
             if options["particle shape"] != "spherical":
                 raise NotImplementedError(
-                    "Particle shape must be 'spherical' for particle-size distributions"
+                    "Particle shape must be 'spherical' for particle-size distribution"
                     " submodels."
                 )
+            if options["SEI"] != "none":
+                raise NotImplementedError(
+                    "SEI submodels do not yet support particle-size distributions."
+                )
+            if options["stress-induced diffusion"] == "true":
+                raise NotImplementedError(
+                    "stress-induced diffusion cannot yet be included in "
+                    "particle-size distributions."
+                )
             if options["thermal"] == "x-full":
                 raise NotImplementedError(
                     "X-full thermal submodels do not yet support particle-size"
                     " distributions."
                 )
 
-        # Some standard checks to make sure options are compatible
+        # Renamed options
+        if options["particle"] == "fast diffusion":
+            raise pybamm.OptionError(
+                "The 'fast diffusion' option has been renamed. "
+                "Use 'uniform profile' instead."
+            )
         if options["SEI porosity change"] in [True, False]:
             raise pybamm.OptionError(
                 "SEI porosity change must now be given in string format "
                 "('true' or 'false')"
             )
 
+        # Some standard checks to make sure options are compatible
         if options["dimensionality"] == 0:
             if options["current collector"] not in ["uniform"]:
                 raise pybamm.OptionError(
@@ -324,12 +356,15 @@ def __init__(self, extra_options):
                 raise pybamm.OptionError(
                     "cannot have transverse convection in 0D model"
                 )
-
-        if options["particle"] == "fast diffusion":
-            raise NotImplementedError(
-                "The 'fast diffusion' option has been renamed. "
-                "Use 'uniform profile' instead."
-            )
+        if isinstance(options["stress-induced diffusion"], str):
+            if (
+                options["stress-induced diffusion"] == "true"
+                and options["particle mechanics"] == "none"
+            ):
+                raise pybamm.OptionError(
+                    "cannot have stress-induced diffusion without a particle "
+                    "mechanics model"
+                )
 
         for option, value in options.items():
             if option == "external submodels" or option == "working electrode":
@@ -348,6 +383,7 @@ def __init__(self, extra_options):
                                 "loss of active material",
                                 "particle mechanics",
                                 "particle",
+                                "stress-induced diffusion",
                             ]
                             and isinstance(value, tuple)
                             and len(value) == 2
diff --git a/pybamm/models/full_battery_models/lithium_ion/dfn.py b/pybamm/models/full_battery_models/lithium_ion/dfn.py
index ecbd8361a4..493825edc4 100644
--- a/pybamm/models/full_battery_models/lithium_ion/dfn.py
+++ b/pybamm/models/full_battery_models/lithium_ion/dfn.py
@@ -94,7 +94,9 @@ def set_particle_submodel(self):
                     self.submodels[
                         domain.lower() + " particle"
                     ] = pybamm.particle.no_distribution.FickianDiffusion(
-                        self.param, domain
+                        self.param,
+                        domain,
+                        self.options,
                     )
                 elif particle_side in [
                     "uniform profile",
@@ -104,7 +106,10 @@ def set_particle_submodel(self):
                     self.submodels[
                         domain.lower() + " particle"
                     ] = pybamm.particle.no_distribution.PolynomialProfile(
-                        self.param, domain, particle_side
+                        self.param,
+                        domain,
+                        particle_side,
+                        self.options,
                     )
             elif self.options["particle size"] == "distribution":
                 if particle_side == "Fickian diffusion":
@@ -113,15 +118,12 @@ def set_particle_submodel(self):
                     ] = pybamm.particle.size_distribution.FickianDiffusion(
                         self.param, domain
                     )
-                elif particle_side in [
-                    "uniform profile",
-                    "quadratic profile",
-                    "quartic profile",
-                ]:
+                elif particle_side == "uniform profile":
                     self.submodels[
                         domain.lower() + " particle"
                     ] = pybamm.particle.size_distribution.UniformProfile(
-                        self.param, domain
+                        self.param,
+                        domain,
                     )
 
     def set_solid_submodel(self):
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 5f6961c8ba..4985c5efee 100644
--- a/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py
+++ b/pybamm/models/full_battery_models/lithium_ion/newman_tobias.py
@@ -59,11 +59,11 @@ def set_particle_submodel(self):
 
         if self.options["particle"] == "Fickian diffusion":
             submod_n = pybamm.particle.no_distribution.XAveragedFickianDiffusion(
-                self.param, "Negative"
+                self.param, "Negative", self.options
             )
             self.submodels["negative particle"] = submod_n
             submod_p = pybamm.particle.no_distribution.XAveragedFickianDiffusion(
-                self.param, "Positive"
+                self.param, "Positive", self.options
             )
             self.submodels["positive particle"] = submod_p
         elif self.options["particle"] in [
@@ -74,12 +74,12 @@ def set_particle_submodel(self):
             self.submodels[
                 "negative particle"
             ] = pybamm.particle.no_distribution.XAveragedPolynomialProfile(
-                self.param, "Negative", self.options["particle"]
+                self.param, "Negative", self.options["particle"], self.options
             )
             self.submodels[
                 "positive particle"
             ] = pybamm.particle.no_distribution.XAveragedPolynomialProfile(
-                self.param, "Positive", self.options["particle"]
+                self.param, "Positive", self.options["particle"], self.options
             )
 
     def set_electrolyte_submodel(self):
diff --git a/pybamm/models/full_battery_models/lithium_ion/spm.py b/pybamm/models/full_battery_models/lithium_ion/spm.py
index 87a3cee847..2673339199 100644
--- a/pybamm/models/full_battery_models/lithium_ion/spm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/spm.py
@@ -113,7 +113,7 @@ def set_particle_submodel(self):
                 self.submodels[
                     domain.lower() + " particle"
                 ] = pybamm.particle.no_distribution.XAveragedFickianDiffusion(
-                    self.param, domain
+                    self.param, domain, self.options
                 )
             elif particle_side in [
                 "uniform profile",
@@ -123,7 +123,7 @@ def set_particle_submodel(self):
                 self.submodels[
                     domain.lower() + " particle"
                 ] = pybamm.particle.no_distribution.XAveragedPolynomialProfile(
-                    self.param, domain, particle_side
+                    self.param, domain, particle_side, self.options
                 )
 
     def set_solid_submodel(self):
diff --git a/pybamm/models/standard_variables.py b/pybamm/models/standard_variables.py
index 7d9b398270..514a41b474 100644
--- a/pybamm/models/standard_variables.py
+++ b/pybamm/models/standard_variables.py
@@ -175,12 +175,12 @@ def __init__(self):
             auxiliary_domains={"secondary": "current collector"},
             bounds=(0, 1),
         )
-        self.c_s_n_rxav = pybamm.Variable(
+        self.c_s_n_av = pybamm.Variable(
             "Average negative particle concentration",
             domain="current collector",
             bounds=(0, 1),
         )
-        self.c_s_p_rxav = pybamm.Variable(
+        self.c_s_p_av = pybamm.Variable(
             "Average positive particle concentration",
             domain="current collector",
             bounds=(0, 1),
@@ -220,11 +220,11 @@ def __init__(self):
             domain="positive electrode",
             auxiliary_domains={"secondary": "current collector"},
         )
-        self.q_s_n_rxav = pybamm.Variable(
+        self.q_s_n_av = pybamm.Variable(
             "Average negative particle concentration gradient",
             domain="current collector",
         )
-        self.q_s_p_rxav = pybamm.Variable(
+        self.q_s_p_av = pybamm.Variable(
             "Average positive particle concentration gradient",
             domain="current collector",
         )
diff --git a/pybamm/models/submodels/base_submodel.py b/pybamm/models/submodels/base_submodel.py
index 8ee101065b..030e07eff1 100644
--- a/pybamm/models/submodels/base_submodel.py
+++ b/pybamm/models/submodels/base_submodel.py
@@ -16,6 +16,16 @@ class BaseSubModel(pybamm.BaseModel):
     ----------
     param: parameter class
         The model parameter symbols
+    domain : str
+        The domain of the model either 'Negative' or 'Positive'
+    name: str
+        A string giving the name of the submodel
+    external: bool, optional
+        Whether the variables defined by the submodel will be provided externally
+        by the users. Default is 'False'.
+    options: dict
+        A dictionary of options to be passed to the model.
+        See :class:`pybamm.BaseBatteryModel`
 
     Attributes
     ----------
diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
index cdbda45a31..f12df7af1a 100644
--- a/pybamm/models/submodels/particle/base_particle.py
+++ b/pybamm/models/submodels/particle/base_particle.py
@@ -14,12 +14,15 @@ class BaseParticle(pybamm.BaseSubModel):
         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`
 
     **Extends:** :class:`pybamm.BaseSubModel`
     """
 
-    def __init__(self, param, domain):
-        super().__init__(param, domain)
+    def __init__(self, param, domain, options=None):
+        super().__init__(param, domain, options=options)
 
     def _get_standard_concentration_variables(
         self, c_s, c_s_xav=None, c_s_rav=None, c_s_av=None, c_s_surf=None
@@ -148,4 +151,3 @@ def _get_standard_flux_variables(self, N_s, N_s_xav):
         }
 
         return variables
-
diff --git a/pybamm/models/submodels/particle/no_distribution/__init__.py b/pybamm/models/submodels/particle/no_distribution/__init__.py
index 5036e9c5e7..702c3f842a 100644
--- a/pybamm/models/submodels/particle/no_distribution/__init__.py
+++ b/pybamm/models/submodels/particle/no_distribution/__init__.py
@@ -1,4 +1,5 @@
+from .base_fickian import BaseFickian
 from .fickian_diffusion import FickianDiffusion
 from .x_averaged_fickian_diffusion import XAveragedFickianDiffusion
 from .x_averaged_polynomial_profile import XAveragedPolynomialProfile
-from .polynomial_profile import PolynomialProfile
\ No newline at end of file
+from .polynomial_profile import PolynomialProfile
diff --git a/pybamm/models/submodels/particle/no_distribution/base_fickian.py b/pybamm/models/submodels/particle/no_distribution/base_fickian.py
new file mode 100644
index 0000000000..b61be85394
--- /dev/null
+++ b/pybamm/models/submodels/particle/no_distribution/base_fickian.py
@@ -0,0 +1,77 @@
+#
+# Base class for particles with Fickian diffusion
+#
+import pybamm
+from ..base_particle import BaseParticle
+
+
+class BaseFickian(BaseParticle):
+    """
+    Base class for molar conservation in particles with Fickian diffusion.
+
+    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`
+
+    **Extends:** :class:`pybamm.particle.BaseParticle`
+    """
+
+    def __init__(self, param, domain, options):
+        super().__init__(param, domain, options)
+
+    def _get_effective_diffusivity(self, c, T):
+        param = self.param
+
+        # Get diffusivity
+        if self.domain == "Negative":
+            D = param.D_n(c, T)
+        elif self.domain == "Positive":
+            D = param.D_p(c, T)
+
+        # Account for stress-induced diffusion by defining a multiplicative
+        # "stress factor"
+        # This option can either be a string (both sides the same) or a 2-tuple
+        # to indicate different options in negative and positive electrodes
+        if isinstance(self.options["stress-induced diffusion"], str):
+            stress_left = self.options["stress-induced diffusion"]
+            stress_right = self.options["stress-induced diffusion"]
+        else:
+            stress_left, stress_right = self.options["stress-induced diffusion"]
+
+        if self.domain == "Negative" and stress_left == "true":
+            stress_factor = 1 + param.theta_n * (c - param.c_n_0) / (
+                1 + param.Theta * T
+            )
+        elif self.domain == "Positive" and stress_right == "true":
+            stress_factor = 1 + param.theta_p * (c - param.c_p_0) / (
+                1 + param.Theta * T
+            )
+        else:
+            stress_factor = 1
+
+        return D * stress_factor
+
+    def _get_standard_diffusivity_variables(self, D_eff):
+        if self.domain == "Negative":
+            D_scale = self.param.D_n_typ_dim
+        elif self.domain == "Positive":
+            D_scale = self.param.D_p_typ_dim
+
+        variables = {
+            self.domain + " effective diffusivity": D_eff,
+            self.domain + " effective diffusivity [m2.s-1]": D_eff * D_scale,
+            "X-averaged "
+            + self.domain.lower()
+            + " effective diffusivity": pybamm.x_average(D_eff),
+            "X-averaged "
+            + self.domain.lower()
+            + " effective diffusivity [m2.s-1]": pybamm.x_average(D_eff * D_scale),
+        }
+
+        return variables
diff --git a/pybamm/models/submodels/particle/no_distribution/fickian_diffusion.py b/pybamm/models/submodels/particle/no_distribution/fickian_diffusion.py
index ff9ee7ca34..0db5d29d9d 100644
--- a/pybamm/models/submodels/particle/no_distribution/fickian_diffusion.py
+++ b/pybamm/models/submodels/particle/no_distribution/fickian_diffusion.py
@@ -2,10 +2,10 @@
 # Class for particles with Fickian diffusion and x-dependence
 #
 import pybamm
-from ..base_particle import BaseParticle
+from .base_fickian import BaseFickian
 
 
-class FickianDiffusion(BaseParticle):
+class FickianDiffusion(BaseFickian):
     """
     Class for molar conservation in particles, employing Fick's law, and allowing
     variation in the electrode domain. I.e., the concentration varies with r
@@ -17,12 +17,15 @@ class FickianDiffusion(BaseParticle):
         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`
 
     **Extends:** :class:`pybamm.particle.BaseParticle`
     """
 
-    def __init__(self, param, domain):
-        super().__init__(param, domain)
+    def __init__(self, param, domain, options):
+        super().__init__(param, domain, options)
 
     def get_fundamental_variables(self):
         if self.domain == "Negative":
@@ -42,13 +45,11 @@ def get_coupled_variables(self, variables):
             [self.domain.lower() + " particle"],
         )
 
-        if self.domain == "Negative":
-            N_s = -self.param.D_n(c_s, T) * pybamm.grad(c_s)
-
-        elif self.domain == "Positive":
-            N_s = -self.param.D_p(c_s, T) * pybamm.grad(c_s)
+        D_eff = self._get_effective_diffusivity(c_s, T)
+        N_s = -D_eff * pybamm.grad(c_s)
 
         variables.update(self._get_standard_flux_variables(N_s, N_s))
+        variables.update(self._get_standard_diffusivity_variables(D_eff))
         variables.update(self._get_total_concentration_variables(variables))
 
         return variables
@@ -67,21 +68,12 @@ def set_rhs(self, variables):
     def set_boundary_conditions(self, variables):
 
         c_s = variables[self.domain + " particle concentration"]
-        T = pybamm.PrimaryBroadcast(
-            variables[self.domain + " electrode temperature"],
-            c_s.domain,
-        )
+        D_eff = variables[self.domain + " effective diffusivity"]
         j = variables[self.domain + " electrode interfacial current density"]
         R = variables[self.domain + " particle radius"]
 
         if self.domain == "Negative":
-            rbc = (
-                -self.param.C_n
-                * j
-                * R
-                / self.param.a_R_n
-                / pybamm.surf(self.param.D_n(c_s, T))
-            )
+            rbc = -self.param.C_n * j * R / self.param.a_R_n / pybamm.surf(D_eff)
 
         elif self.domain == "Positive":
             rbc = (
@@ -90,7 +82,7 @@ def set_boundary_conditions(self, variables):
                 * R
                 / self.param.a_R_p
                 / self.param.gamma_p
-                / pybamm.surf(self.param.D_p(c_s, T))
+                / pybamm.surf(D_eff)
             )
 
         self.boundary_conditions = {
diff --git a/pybamm/models/submodels/particle/no_distribution/polynomial_profile.py b/pybamm/models/submodels/particle/no_distribution/polynomial_profile.py
index 9217ee1e7c..137d884d68 100644
--- a/pybamm/models/submodels/particle/no_distribution/polynomial_profile.py
+++ b/pybamm/models/submodels/particle/no_distribution/polynomial_profile.py
@@ -3,14 +3,14 @@
 #
 import pybamm
 
-from ..base_particle import BaseParticle
+from .base_fickian import BaseFickian
 
 
-class PolynomialProfile(BaseParticle):
+class PolynomialProfile(BaseFickian):
     """
-    Class for molar conservation in particles, assuming a polynomial
-    concentration profile in r, and allowing variation in the electrode domain.
-    Model equations from [1]_.
+    Class for molar conservation in particles employing Fick's
+    law, assuming a polynomial concentration profile in r, and allowing variation
+    in the electrode domain. Model equations from [1]_.
 
     Parameters
     ----------
@@ -21,6 +21,9 @@ class PolynomialProfile(BaseParticle):
     name : str
         The name of the polynomial approximation to be used. Can be "uniform
         profile", "quadratic profile" or "quartic profile".
+    options: dict
+        A dictionary of options to be passed to the model.
+        See :class:`pybamm.BaseBatteryModel`
 
     References
     ----------
@@ -31,8 +34,8 @@ class PolynomialProfile(BaseParticle):
     **Extends:** :class:`pybamm.particle.BaseParticle`
     """
 
-    def __init__(self, param, domain, name):
-        super().__init__(param, domain)
+    def __init__(self, param, domain, name, options):
+        super().__init__(param, domain, options)
         self.name = name
 
         pybamm.citations.register("Subramanian2005")
@@ -133,6 +136,7 @@ def get_coupled_variables(self, variables):
             variables[self.domain + " electrode temperature"],
             [self.domain.lower() + " particle"],
         )
+        D_eff = self._get_effective_diffusivity(c_s, T)
 
         # Set flux depending on polynomial order
         if self.name == "uniform profile":
@@ -152,10 +156,10 @@ def get_coupled_variables(self, variables):
             # The flux may be computed directly from the polynomial for c
             if self.domain == "Negative":
                 r = pybamm.standard_spatial_vars.r_n
-                N_s = -self.param.D_n(c_s, T) * 5 * (c_s_surf - c_s_rav) * r
+                N_s = -D_eff * 5 * (c_s_surf - c_s_rav) * r
             elif self.domain == "Positive":
                 r = pybamm.standard_spatial_vars.r_p
-                N_s = -self.param.D_p(c_s, T) * 5 * (c_s_surf - c_s_rav) * r
+                N_s = -D_eff * 5 * (c_s_surf - c_s_rav) * r
             N_s_xav = pybamm.x_average(N_s)
         elif self.name == "quartic profile":
             q_s_rav = variables[
@@ -164,19 +168,20 @@ def get_coupled_variables(self, variables):
             # The flux may be computed directly from the polynomial for c
             if self.domain == "Negative":
                 r = pybamm.standard_spatial_vars.r_n
-                N_s = -self.param.D_n(c_s, T) * (
+                N_s = -D_eff * (
                     (-70 * c_s_surf + 20 * q_s_rav + 70 * c_s_rav) * r
                     + (105 * c_s_surf - 28 * q_s_rav - 105 * c_s_rav) * r ** 3
                 )
             elif self.domain == "Positive":
                 r = pybamm.standard_spatial_vars.r_p
-                N_s = -self.param.D_p(c_s, T) * (
+                N_s = -D_eff * (
                     (-70 * c_s_surf + 20 * q_s_rav + 70 * c_s_rav) * r
                     + (105 * c_s_surf - 28 * q_s_rav - 105 * c_s_rav) * r ** 3
                 )
             N_s_xav = pybamm.x_average(N_s)
 
         variables.update(self._get_standard_flux_variables(N_s, N_s_xav))
+        variables.update(self._get_standard_diffusivity_variables(D_eff))
         variables.update(self._get_total_concentration_variables(variables))
 
         return variables
@@ -202,12 +207,13 @@ def set_rhs(self, variables):
             c_s_rav = variables[
                 "R-averaged " + self.domain.lower() + " particle concentration"
             ]
-            T = variables[self.domain + " electrode temperature"]
+            D_eff = variables[self.domain + " effective diffusivity"]
+
             if self.domain == "Negative":
                 self.rhs.update(
                     {
                         q_s_rav: -30
-                        * self.param.D_n(c_s_rav, T)
+                        * pybamm.r_average(D_eff)
                         * q_s_rav
                         / self.param.C_n
                         - 45 * j / self.param.a_R_n / 2
@@ -217,7 +223,7 @@ def set_rhs(self, variables):
                 self.rhs.update(
                     {
                         q_s_rav: -30
-                        * self.param.D_p(c_s_rav, T)
+                        * pybamm.r_average(D_eff)
                         * q_s_rav
                         / self.param.C_p
                         - 45 * j / self.param.a_R_p / self.param.gamma_p / 2
@@ -229,9 +235,10 @@ def set_algebraic(self, variables):
         c_s_rav = variables[
             "R-averaged " + self.domain.lower() + " particle concentration"
         ]
+        D_eff = variables[self.domain + " effective diffusivity"]
         j = variables[self.domain + " electrode interfacial current density"]
-        T = variables[self.domain + " electrode temperature"]
         R = variables[self.domain + " particle radius"]
+
         if self.name == "uniform profile":
             # No algebraic equations since we only solve for the average concentration
             pass
@@ -239,13 +246,13 @@ def set_algebraic(self, variables):
             # We solve an algebraic equation for the surface concentration
             if self.domain == "Negative":
                 self.algebraic = {
-                    c_s_surf: self.param.D_n(c_s_surf, T) * (c_s_surf - c_s_rav)
+                    c_s_surf: pybamm.surf(D_eff) * (c_s_surf - c_s_rav)
                     + self.param.C_n * (j * R / self.param.a_R_n / 5)
                 }
 
             elif self.domain == "Positive":
                 self.algebraic = {
-                    c_s_surf: self.param.D_p(c_s_surf, T) * (c_s_surf - c_s_rav)
+                    c_s_surf: pybamm.surf(D_eff) * (c_s_surf - c_s_rav)
                     + self.param.C_p
                     * (j * R / self.param.a_R_p / self.param.gamma_p / 5)
                 }
@@ -257,14 +264,14 @@ def set_algebraic(self, variables):
             ]
             if self.domain == "Negative":
                 self.algebraic = {
-                    c_s_surf: self.param.D_n(c_s_surf, T)
+                    c_s_surf: pybamm.surf(D_eff)
                     * (35 * (c_s_surf - c_s_rav) - 8 * q_s_rav)
                     + self.param.C_n * (j * R / self.param.a_R_n)
                 }
 
             elif self.domain == "Positive":
                 self.algebraic = {
-                    c_s_surf: self.param.D_p(c_s_surf, T)
+                    c_s_surf: pybamm.surf(D_eff)
                     * (35 * (c_s_surf - c_s_rav) - 8 * q_s_rav)
                     + self.param.C_p * (j * R / self.param.a_R_p / self.param.gamma_p)
                 }
diff --git a/pybamm/models/submodels/particle/no_distribution/x_averaged_fickian_diffusion.py b/pybamm/models/submodels/particle/no_distribution/x_averaged_fickian_diffusion.py
index ea4370af6d..2228088a56 100644
--- a/pybamm/models/submodels/particle/no_distribution/x_averaged_fickian_diffusion.py
+++ b/pybamm/models/submodels/particle/no_distribution/x_averaged_fickian_diffusion.py
@@ -3,10 +3,10 @@
 #
 import pybamm
 
-from ..base_particle import BaseParticle
+from .base_fickian import BaseFickian
 
 
-class XAveragedFickianDiffusion(BaseParticle):
+class XAveragedFickianDiffusion(BaseFickian):
     """
     Class for molar conservation in a single x-averaged particle, employing Fick's
     law. I.e., the concentration varies with r (internal spherical coordinate)
@@ -18,12 +18,15 @@ class XAveragedFickianDiffusion(BaseParticle):
         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`
 
     **Extends:** :class:`pybamm.particle.BaseParticle`
     """
 
-    def __init__(self, param, domain):
-        super().__init__(param, domain)
+    def __init__(self, param, domain, options):
+        super().__init__(param, domain, options)
 
     def get_fundamental_variables(self):
         if self.domain == "Negative":
@@ -47,15 +50,16 @@ def get_coupled_variables(self, variables):
             [self.domain.lower() + " particle"],
         )
 
-        if self.domain == "Negative":
-            N_s_xav = -self.param.D_n(c_s_xav, T_xav) * pybamm.grad(c_s_xav)
-
-        elif self.domain == "Positive":
-            N_s_xav = -self.param.D_p(c_s_xav, T_xav) * pybamm.grad(c_s_xav)
+        D_eff_xav = self._get_effective_diffusivity(c_s_xav, T_xav)
+        N_s_xav = -D_eff_xav * pybamm.grad(c_s_xav)
 
+        D_eff = pybamm.SecondaryBroadcast(
+            D_eff_xav, [self._domain.lower() + " electrode"]
+        )
         N_s = pybamm.SecondaryBroadcast(N_s_xav, [self._domain.lower() + " electrode"])
 
         variables.update(self._get_standard_flux_variables(N_s, N_s_xav))
+        variables.update(self._get_standard_diffusivity_variables(D_eff))
         variables.update(self._get_total_concentration_variables(variables))
 
         return variables
@@ -76,10 +80,9 @@ def set_boundary_conditions(self, variables):
         c_s_xav = variables[
             "X-averaged " + self.domain.lower() + " particle concentration"
         ]
-        T_xav = pybamm.PrimaryBroadcast(
-            variables["X-averaged " + self.domain.lower() + " electrode temperature"],
-            c_s_xav.domain[0],
-        )
+        D_eff_xav = variables[
+            "X-averaged " + self.domain.lower() + " effective diffusivity"
+        ]
         j_xav = variables[
             "X-averaged "
             + self.domain.lower()
@@ -87,12 +90,7 @@ def set_boundary_conditions(self, variables):
         ]
 
         if self.domain == "Negative":
-            rbc = (
-                -self.param.C_n
-                * j_xav
-                / self.param.a_R_n
-                / pybamm.surf(self.param.D_n(c_s_xav, T_xav))
-            )
+            rbc = -self.param.C_n * j_xav / self.param.a_R_n / pybamm.surf(D_eff_xav)
 
         elif self.domain == "Positive":
             rbc = (
@@ -100,7 +98,7 @@ def set_boundary_conditions(self, variables):
                 * j_xav
                 / self.param.a_R_p
                 / self.param.gamma_p
-                / pybamm.surf(self.param.D_p(c_s_xav, T_xav))
+                / pybamm.surf(D_eff_xav)
             )
 
         self.boundary_conditions = {
diff --git a/pybamm/models/submodels/particle/no_distribution/x_averaged_polynomial_profile.py b/pybamm/models/submodels/particle/no_distribution/x_averaged_polynomial_profile.py
index c05ed3851e..07176b35c6 100644
--- a/pybamm/models/submodels/particle/no_distribution/x_averaged_polynomial_profile.py
+++ b/pybamm/models/submodels/particle/no_distribution/x_averaged_polynomial_profile.py
@@ -3,13 +3,13 @@
 #
 import pybamm
 
-from ..base_particle import BaseParticle
+from .base_fickian import BaseFickian
 
 
-class XAveragedPolynomialProfile(BaseParticle):
+class XAveragedPolynomialProfile(BaseFickian):
     """
-    Class for molar conservation in a single x-averaged particle with
-    an assumed polynomial concentration profile in r. Model equations from [1]_.
+    Class for molar conservation in a single x-averaged particle employing Fick's law,
+    with an assumed polynomial concentration profile in r. Model equations from [1]_.
 
     Parameters
     ----------
@@ -20,6 +20,9 @@ class XAveragedPolynomialProfile(BaseParticle):
     name : str
         The name of the polynomial approximation to be used. Can be "uniform
         profile", "quadratic profile" or "quartic profile".
+    options: dict
+        A dictionary of options to be passed to the model.
+        See :class:`pybamm.BaseBatteryModel`
 
     References
     ----------
@@ -30,8 +33,8 @@ class XAveragedPolynomialProfile(BaseParticle):
     **Extends:** :class:`pybamm.particle.BaseParticle`
     """
 
-    def __init__(self, param, domain, name):
-        super().__init__(param, domain)
+    def __init__(self, param, domain, name, options):
+        super().__init__(param, domain, options)
         self.name = name
 
         pybamm.citations.register("Subramanian2005")
@@ -39,13 +42,13 @@ def __init__(self, param, domain, name):
     def get_fundamental_variables(self):
         # For all orders we solve an equation for the average concentration
         if self.domain == "Negative":
-            c_s_rxav = pybamm.standard_variables.c_s_n_rxav
+            c_s_av = pybamm.standard_variables.c_s_n_av
 
         elif self.domain == "Positive":
-            c_s_rxav = pybamm.standard_variables.c_s_p_rxav
+            c_s_av = pybamm.standard_variables.c_s_p_av
 
         variables = {
-            "Average " + self.domain.lower() + " particle concentration": c_s_rxav
+            "Average " + self.domain.lower() + " particle concentration": c_s_av
         }
 
         # For the fourth order polynomial approximation we also solve an
@@ -55,39 +58,36 @@ def get_fundamental_variables(self):
         # gradient q = dc/dr
         if self.name == "quartic profile":
             if self.domain == "Negative":
-                q_s_rxav = pybamm.standard_variables.q_s_n_rxav
+                q_s_av = pybamm.standard_variables.q_s_n_av
             elif self.domain == "Positive":
-                q_s_rxav = pybamm.standard_variables.q_s_p_rxav
+                q_s_av = pybamm.standard_variables.q_s_p_av
             variables.update(
                 {
                     "Average "
                     + self.domain.lower()
-                    + " particle concentration gradient": q_s_rxav
+                    + " particle concentration gradient": q_s_av
                 }
             )
 
         return variables
 
     def get_coupled_variables(self, variables):
-        c_s_rxav = variables[
-            "Average " + self.domain.lower() + " particle concentration"
-        ]
+        c_s_av = variables["Average " + self.domain.lower() + " particle concentration"]
+        T_av = variables["X-averaged " + self.domain.lower() + " electrode temperature"]
+
+        D_eff_av = self._get_effective_diffusivity(c_s_av, T_av)
         i_boundary_cc = variables["Current collector current density"]
         a_av = variables[
             "X-averaged "
             + self.domain.lower()
             + " electrode surface area to volume ratio"
         ]
-        T_xav = pybamm.PrimaryBroadcast(
-            variables["X-averaged " + self.domain.lower() + " electrode temperature"],
-            [self.domain.lower() + " particle"],
-        )
 
         # Set surface concentration based on polynomial order
         if self.name == "uniform profile":
             # The concentration is uniform so the surface value is equal to
             # the average
-            c_s_surf_xav = c_s_rxav
+            c_s_surf_xav = c_s_av
         elif self.name == "quadratic profile":
             # The surface concentration is computed from the average concentration
             # and boundary flux
@@ -106,72 +106,53 @@ def get_coupled_variables(self, variables):
             # than DAEs.
             if self.domain == "Negative":
                 j_xav = i_boundary_cc / (a_av * self.param.l_n)
-                c_s_surf_xav = c_s_rxav - self.param.C_n * (
-                    j_xav
-                    / 5
-                    / self.param.a_R_n
-                    / self.param.D_n(c_s_rxav, pybamm.surf(T_xav))
+                c_s_surf_xav = c_s_av - self.param.C_n * (
+                    j_xav / 5 / self.param.a_R_n / D_eff_av
                 )
 
             if self.domain == "Positive":
                 j_xav = -i_boundary_cc / (a_av * self.param.l_p)
-                c_s_surf_xav = c_s_rxav - self.param.C_p * (
-                    j_xav
-                    / 5
-                    / self.param.a_R_p
-                    / self.param.gamma_p
-                    / self.param.D_p(c_s_rxav, pybamm.surf(T_xav))
+                c_s_surf_xav = c_s_av - self.param.C_p * (
+                    j_xav / 5 / self.param.a_R_p / self.param.gamma_p / D_eff_av
                 )
         elif self.name == "quartic profile":
             # The surface concentration is computed from the average concentration,
             # the average concentration gradient and the boundary flux (see notes
-            # for the case order=2)
-            q_s_rxav = variables[
+            # for the quadratic profile)
+            q_s_av = variables[
                 "Average " + self.domain.lower() + " particle concentration gradient"
             ]
             if self.domain == "Negative":
                 j_xav = i_boundary_cc / (a_av * self.param.l_n)
                 c_s_surf_xav = (
-                    c_s_rxav
-                    + 8 * q_s_rxav / 35
-                    - self.param.C_n
-                    * (
-                        j_xav
-                        / 35
-                        / self.param.a_R_n
-                        / self.param.D_n(c_s_rxav, pybamm.surf(T_xav))
-                    )
+                    c_s_av
+                    + 8 * q_s_av / 35
+                    - self.param.C_n * (j_xav / 35 / self.param.a_R_n / D_eff_av)
                 )
 
             if self.domain == "Positive":
                 j_xav = -i_boundary_cc / (a_av * self.param.l_p)
                 c_s_surf_xav = (
-                    c_s_rxav
-                    + 8 * q_s_rxav / 35
+                    c_s_av
+                    + 8 * q_s_av / 35
                     - self.param.C_p
-                    * (
-                        j_xav
-                        / 35
-                        / self.param.a_R_p
-                        / self.param.gamma_p
-                        / self.param.D_p(c_s_rxav, pybamm.surf(T_xav))
-                    )
+                    * (j_xav / 35 / self.param.a_R_p / self.param.gamma_p / D_eff_av)
                 )
 
         # Set concentration depending on polynomial order
         if self.name == "uniform profile":
             # The concentration is uniform
             c_s_xav = pybamm.PrimaryBroadcast(
-                c_s_rxav, [self.domain.lower() + " particle"]
+                c_s_av, [self.domain.lower() + " particle"]
             )
         elif self.name == "quadratic profile":
             # The concentration is given by c = A + B*r**2
             A = pybamm.PrimaryBroadcast(
-                (1 / 2) * (5 * c_s_rxav - 3 * c_s_surf_xav),
+                (1 / 2) * (5 * c_s_av - 3 * c_s_surf_xav),
                 [self.domain.lower() + " particle"],
             )
             B = pybamm.PrimaryBroadcast(
-                (5 / 2) * (c_s_surf_xav - c_s_rxav), [self.domain.lower() + " particle"]
+                (5 / 2) * (c_s_surf_xav - c_s_av), [self.domain.lower() + " particle"]
             )
             if self.domain == "Negative":
                 # Since c_s_xav doesn't depend on x, we need to define a spatial
@@ -199,15 +180,15 @@ def get_coupled_variables(self, variables):
         elif self.name == "quartic profile":
             # The concentration is given by c = A + B*r**2 + C*r**4
             A = pybamm.PrimaryBroadcast(
-                39 * c_s_surf_xav / 4 - 3 * q_s_rxav - 35 * c_s_rxav / 4,
+                39 * c_s_surf_xav / 4 - 3 * q_s_av - 35 * c_s_av / 4,
                 [self.domain.lower() + " particle"],
             )
             B = pybamm.PrimaryBroadcast(
-                -35 * c_s_surf_xav + 10 * q_s_rxav + 35 * c_s_rxav,
+                -35 * c_s_surf_xav + 10 * q_s_av + 35 * c_s_av,
                 [self.domain.lower() + " particle"],
             )
             C = pybamm.PrimaryBroadcast(
-                105 * c_s_surf_xav / 4 - 7 * q_s_rxav - 105 * c_s_rxav / 4,
+                105 * c_s_surf_xav / 4 - 7 * q_s_av - 105 * c_s_av / 4,
                 [self.domain.lower() + " particle"],
             )
             if self.domain == "Negative":
@@ -239,6 +220,12 @@ def get_coupled_variables(self, variables):
         )
 
         # Set flux based on polynomial order
+        T_xav = pybamm.PrimaryBroadcast(
+            T_av,
+            [self.domain.lower() + " particle"],
+        )
+        D_eff_xav = self._get_effective_diffusivity(c_s_xav, T_xav)
+
         if self.name == "uniform profile":
             # The flux is zero since there is no concentration gradient
             N_s_xav = pybamm.FullBroadcastToEdges(
@@ -247,37 +234,37 @@ def get_coupled_variables(self, variables):
         elif self.name == "quadratic profile":
             # The flux may be computed directly from the polynomial for c
             if self.domain == "Negative":
-                N_s_xav = (
-                    -self.param.D_n(c_s_xav, T_xav) * 5 * (c_s_surf_xav - c_s_rxav) * r
-                )
+                N_s_xav = -D_eff_xav * 5 * (c_s_surf_xav - c_s_av) * r
             if self.domain == "Positive":
-                N_s_xav = (
-                    -self.param.D_p(c_s_xav, T_xav) * 5 * (c_s_surf_xav - c_s_rxav) * r
-                )
+                N_s_xav = -D_eff_xav * 5 * (c_s_surf_xav - c_s_av) * r
         elif self.name == "quartic profile":
-            q_s_rxav = variables[
+            q_s_av = variables[
                 "Average " + self.domain.lower() + " particle concentration gradient"
             ]
             # The flux may be computed directly from the polynomial for c
             if self.domain == "Negative":
-                N_s_xav = -self.param.D_n(c_s_xav, T_xav) * (
-                    (-70 * c_s_surf_xav + 20 * q_s_rxav + 70 * c_s_rxav) * r
-                    + (105 * c_s_surf_xav - 28 * q_s_rxav - 105 * c_s_rxav) * r ** 3
+                N_s_xav = -D_eff_xav * (
+                    (-70 * c_s_surf_xav + 20 * q_s_av + 70 * c_s_av) * r
+                    + (105 * c_s_surf_xav - 28 * q_s_av - 105 * c_s_av) * r ** 3
                 )
             elif self.domain == "Positive":
-                N_s_xav = -self.param.D_p(c_s_xav, T_xav) * (
-                    (-70 * c_s_surf_xav + 20 * q_s_rxav + 70 * c_s_rxav) * r
-                    + (105 * c_s_surf_xav - 28 * q_s_rxav - 105 * c_s_rxav) * r ** 3
+                N_s_xav = -D_eff_xav * (
+                    (-70 * c_s_surf_xav + 20 * q_s_av + 70 * c_s_av) * r
+                    + (105 * c_s_surf_xav - 28 * q_s_av - 105 * c_s_av) * r ** 3
                 )
 
+        D_eff = pybamm.SecondaryBroadcast(
+            D_eff_xav, [self._domain.lower() + " electrode"]
+        )
         N_s = pybamm.SecondaryBroadcast(N_s_xav, [self._domain.lower() + " electrode"])
 
         variables.update(
             self._get_standard_concentration_variables(
-                c_s, c_s_av=c_s_rxav, c_s_surf=c_s_surf
+                c_s, c_s_av=c_s_av, c_s_surf=c_s_surf
             )
         )
         variables.update(self._get_standard_flux_variables(N_s, N_s_xav))
+        variables.update(self._get_standard_diffusivity_variables(D_eff))
         variables.update(self._get_total_concentration_variables(variables))
 
         return variables
@@ -288,9 +275,7 @@ def set_rhs(self, variables):
         # using this model with 2D current collectors with the finite element
         # method (see #1399)
 
-        c_s_rxav = variables[
-            "Average " + self.domain.lower() + " particle concentration"
-        ]
+        c_s_av = variables["Average " + self.domain.lower() + " particle concentration"]
         j_xav = variables[
             "X-averaged "
             + self.domain.lower()
@@ -298,51 +283,41 @@ def set_rhs(self, variables):
         ]
 
         if self.domain == "Negative":
-            self.rhs = {
-                c_s_rxav: pybamm.source(-3 * j_xav / self.param.a_R_n, c_s_rxav)
-            }
+            self.rhs = {c_s_av: pybamm.source(-3 * j_xav / self.param.a_R_n, c_s_av)}
 
         elif self.domain == "Positive":
             self.rhs = {
-                c_s_rxav: pybamm.source(
-                    -3 * j_xav / self.param.a_R_p / self.param.gamma_p, c_s_rxav
+                c_s_av: pybamm.source(
+                    -3 * j_xav / self.param.a_R_p / self.param.gamma_p, c_s_av
                 )
             }
 
         if self.name == "quartic profile":
             # We solve an extra ODE for the average particle concentration gradient
-            q_s_rxav = variables[
+            q_s_av = variables[
                 "Average " + self.domain.lower() + " particle concentration gradient"
             ]
-            c_s_surf_xav = variables[
-                "X-averaged " + self.domain.lower() + " particle surface concentration"
-            ]
-            T_xav = variables[
-                "X-averaged " + self.domain.lower() + " electrode temperature"
+            D_eff_xav = variables[
+                "X-averaged " + self.domain.lower() + " effective diffusivity"
             ]
+
             if self.domain == "Negative":
                 self.rhs.update(
                     {
-                        q_s_rxav: pybamm.source(
-                            -30
-                            * self.param.D_n(c_s_surf_xav, T_xav)
-                            * q_s_rxav
-                            / self.param.C_n
+                        q_s_av: pybamm.source(
+                            -30 * pybamm.surf(D_eff_xav) * q_s_av / self.param.C_n
                             - 45 * j_xav / self.param.a_R_n / 2,
-                            q_s_rxav,
+                            q_s_av,
                         )
                     }
                 )
             elif self.domain == "Positive":
                 self.rhs.update(
                     {
-                        q_s_rxav: pybamm.source(
-                            -30
-                            * self.param.D_p(c_s_surf_xav, T_xav)
-                            * q_s_rxav
-                            / self.param.C_p
+                        q_s_av: pybamm.source(
+                            -30 * pybamm.surf(D_eff_xav) * q_s_av / self.param.C_p
                             - 45 * j_xav / self.param.a_R_p / self.param.gamma_p / 2,
-                            q_s_rxav,
+                            q_s_av,
                         )
                     }
                 )
@@ -353,9 +328,7 @@ def set_initial_conditions(self, variables):
         so we arbitrarily evaluate them at x=0 in the negative electrode and x=1 in the
         positive electrode (they will usually be constant)
         """
-        c_s_rxav = variables[
-            "Average " + self.domain.lower() + " particle concentration"
-        ]
+        c_s_av = variables["Average " + self.domain.lower() + " particle concentration"]
 
         if self.domain == "Negative":
             c_init = self.param.c_n_init(0)
@@ -363,11 +336,11 @@ def set_initial_conditions(self, variables):
         elif self.domain == "Positive":
             c_init = self.param.c_p_init(1)
 
-        self.initial_conditions = {c_s_rxav: c_init}
+        self.initial_conditions = {c_s_av: c_init}
         if self.name == "quartic profile":
             # We also need to provide an initial condition for the average
             # concentration gradient
-            q_s_rxav = variables[
+            q_s_av = variables[
                 "Average " + self.domain.lower() + " particle concentration gradient"
             ]
-            self.initial_conditions.update({q_s_rxav: 0})
+            self.initial_conditions.update({q_s_av: 0})
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 4bc8f16c4b..ca32b6f0f5 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -245,27 +245,27 @@ def _set_dimensional_parameters(self):
             "Initial concentration in electrolyte [mol.m-3]"
         )
 
-        # mechanical parameters
-        self.nu_p = pybamm.Parameter("Positive electrode Poisson's ratio")
-        self.E_p = pybamm.Parameter("Positive electrode Young's modulus [Pa]")
-        self.c_p_0_dim = pybamm.Parameter(
-            "Positive electrode reference concentration for free of deformation "
-            "[mol.m-3]"
-        )
-        self.Omega_p = pybamm.Parameter(
-            "Positive electrode partial molar volume [m3.mol-1]"
-        )
+        # Mechanical parameters
         self.nu_n = pybamm.Parameter("Negative electrode Poisson's ratio")
+        self.nu_p = pybamm.Parameter("Positive electrode Poisson's ratio")
         self.E_n = pybamm.Parameter("Negative electrode Young's modulus [Pa]")
+        self.E_p = pybamm.Parameter("Positive electrode Young's modulus [Pa]")
         self.c_n_0_dim = pybamm.Parameter(
             "Negative electrode reference concentration for free of deformation "
             "[mol.m-3]"
         )
+        self.c_p_0_dim = pybamm.Parameter(
+            "Positive electrode reference concentration for free of deformation "
+            "[mol.m-3]"
+        )
         self.Omega_n = pybamm.Parameter(
             "Negative electrode partial molar volume [m3.mol-1]"
         )
-        self.l_cr_p_0 = pybamm.Parameter("Positive electrode initial crack length [m]")
+        self.Omega_p = pybamm.Parameter(
+            "Positive electrode partial molar volume [m3.mol-1]"
+        )
         self.l_cr_n_0 = pybamm.Parameter("Negative electrode initial crack length [m]")
+        self.l_cr_p_0 = pybamm.Parameter("Positive electrode initial crack length [m]")
         self.w_cr = pybamm.Parameter("Negative electrode initial crack width [m]")
         self.rho_cr_n_dim = pybamm.Parameter(
             "Negative electrode number of cracks per unit area [m-2]"
@@ -274,27 +274,25 @@ def _set_dimensional_parameters(self):
             "Positive electrode number of cracks per unit area [m-2]"
         )
         self.b_cr_n = pybamm.Parameter("Negative electrode Paris' law constant b")
+        self.b_cr_p = pybamm.Parameter("Positive electrode Paris' law constant b")
         self.m_cr_n = pybamm.Parameter("Negative electrode Paris' law constant m")
+        self.m_cr_p = pybamm.Parameter("Positive electrode Paris' law constant m")
         self.Eac_cr_n = pybamm.Parameter(
             "Negative electrode activation energy for cracking rate [kJ.mol-1]"
         )  # noqa
-        self.b_cr_p = pybamm.Parameter("Positive electrode Paris' law constant b")
-        self.m_cr_p = pybamm.Parameter("Positive electrode Paris' law constant m")
         self.Eac_cr_p = pybamm.Parameter(
             "Positive electrode activation energy for cracking rate [kJ.mol-1]"
         )  # noqa
-        self.alpha_T_cell_dim = pybamm.Parameter(
-            "Cell thermal expansion coefficient [m.K-1]"
+        # intermediate variables  [K*m^3/mol]
+        self.theta_n_dim = (
+            (self.Omega_n / self.R) * 2 * self.Omega_n * self.E_n / 9 / (1 - self.nu_n)
         )
-        self.R_const = pybamm.constants.R
         self.theta_p_dim = (
-            self.Omega_p ** 2 / self.R_const * 2 / 9 * self.E_p / (1 - self.nu_p)
+            (self.Omega_p / self.R) * 2 * self.Omega_p * self.E_p / 9 / (1 - self.nu_p)
         )
-        # intermediate variable  [K*m^3/mol]
-        self.theta_n_dim = (
-            self.Omega_n ** 2 / self.R_const * 2 / 9 * self.E_n / (1 - self.nu_n)
+        self.alpha_T_cell_dim = pybamm.Parameter(
+            "Cell thermal expansion coefficient [m.K-1]"
         )
-        # intermediate variable  [K*m^3/mol]
 
         # Electrode capacities
         x_n = pybamm.SpatialVariable(
@@ -330,7 +328,8 @@ def _set_dimensional_parameters(self):
 
         self.n_Li_particles_init = self.n_Li_n_init + self.n_Li_p_init
         self.n_Li_init = self.n_Li_particles_init + self.n_Li_e_init
-        # loss of active material parameters
+
+        # Loss of active material parameters
         self.m_LAM_n = pybamm.Parameter(
             "Negative electrode LAM constant exponential term"
         )
@@ -384,32 +383,16 @@ def D_n_dimensional(self, sto, T):
         """Dimensional diffusivity in negative particle. Note this is defined as a
         function of stochiometry"""
         inputs = {"Negative particle stoichiometry": sto, "Temperature [K]": T}
-        crack = self.options["particle mechanics"]
-        if crack != "none" or (isinstance(crack, tuple) and crack[0] != "none"):
-            mech_effects = (
-                1 + self.theta_n_dim * (sto * self.c_n_max - self.c_n_0_dim) / T
-            )
-        else:
-            mech_effects = 1
-        return (
-            pybamm.FunctionParameter("Negative electrode diffusivity [m2.s-1]", inputs)
-            * mech_effects
+        return pybamm.FunctionParameter(
+            "Negative electrode diffusivity [m2.s-1]", inputs
         )
 
     def D_p_dimensional(self, sto, T):
         """Dimensional diffusivity in positive particle. Note this is defined as a
         function of stochiometry"""
         inputs = {"Positive particle stoichiometry": sto, "Temperature [K]": T}
-        crack = self.options["particle mechanics"]
-        if crack != "none" or (isinstance(crack, tuple) and crack[1] != "none"):
-            mech_effects = (
-                1 + self.theta_p_dim * (sto * self.c_p_max - self.c_p_0_dim) / T
-            )
-        else:
-            mech_effects = 1
-        return (
-            pybamm.FunctionParameter("Positive electrode diffusivity [m2.s-1]", inputs)
-            * mech_effects
+        return pybamm.FunctionParameter(
+            "Positive electrode diffusivity [m2.s-1]", inputs
         )
 
     def j0_n_dimensional(self, c_e, c_s_surf, T):
@@ -611,15 +594,12 @@ def _set_scales(self):
         self.D_e_typ = self.D_e_dimensional(self.c_e_typ, self.T_ref)
         self.tau_diffusion_e = self.L_x ** 2 / self.D_e_typ
 
+        # Particle diffusion timescales
         self.D_n_typ_dim = self.D_n_dimensional(pybamm.Scalar(1), self.T_ref)
+        self.D_p_typ_dim = self.D_p_dimensional(pybamm.Scalar(1), self.T_ref)
 
-        # Particle diffusion timescales
-        self.tau_diffusion_n = self.R_n_typ ** 2 / self.D_n_dimensional(
-            pybamm.Scalar(1), self.T_ref
-        )
-        self.tau_diffusion_p = self.R_p_typ ** 2 / self.D_p_dimensional(
-            pybamm.Scalar(1), self.T_ref
-        )
+        self.tau_diffusion_n = self.R_n_typ ** 2 / self.D_n_typ_dim
+        self.tau_diffusion_p = self.R_p_typ ** 2 / self.D_p_typ_dim
 
         # Thermal diffusion timescale
         self.tau_th_yz = self.therm.tau_th_yz
@@ -854,8 +834,8 @@ def _set_dimensionless_parameters(self):
         # Dimensionless mechanical parameters
         self.rho_cr_n = self.rho_cr_n_dim * self.l_cr_n_0 * self.w_cr
         self.rho_cr_p = self.rho_cr_p_dim * self.l_cr_p_0 * self.w_cr
-        self.theta_p = self.theta_p_dim * self.c_p_max / self.Delta_T
-        self.theta_n = self.theta_n_dim * self.c_n_max / self.Delta_T
+        self.theta_p = self.theta_p_dim * self.c_p_max / self.T_ref
+        self.theta_n = self.theta_n_dim * self.c_n_max / self.T_ref
         self.c_p_0 = self.c_p_0_dim / self.c_p_max
         self.c_n_0 = self.c_n_0_dim / self.c_n_max
         self.t0_cr = 3600 / self.C_rate / self.timescale
@@ -954,9 +934,7 @@ def D_p(self, c_s_p, T):
         """Dimensionless positive particle diffusivity"""
         sto = c_s_p
         T_dim = self.Delta_T * T + self.T_ref
-        return self.D_p_dimensional(sto, T_dim) / self.D_p_dimensional(
-            pybamm.Scalar(1), self.T_ref
-        )
+        return self.D_p_dimensional(sto, T_dim) / self.D_p_typ_dim
 
     def j0_n(self, c_e, c_s_surf, T):
         """Dimensionless negative exchange-current density"""
@@ -1040,25 +1018,9 @@ def rho(self, T):
             + self.rho_cp(T) * self.l_cp
         ) / self.l
 
-    def _set_input_current(self):
-        """Set the input current"""
-
-        self.dimensional_current_with_time = pybamm.FunctionParameter(
-            "Current function [A]", {"Time [s]": pybamm.t * self.timescale}
-        )
-        self.dimensional_current_density_with_time = (
-            self.dimensional_current_with_time
-            / (self.n_electrodes_parallel * self.geo.A_cc)
-        )
-        self.current_with_time = (
-            self.dimensional_current_with_time
-            / self.I_typ
-            * pybamm.Function(np.sign, self.I_typ)
-        )
-
     def t_n_change(self, sto):
         """
-        Dimentionless volume change for the negative electrode;
+        Dimensionless volume change for the negative electrode;
         sto should be R-averaged
         """
         return pybamm.FunctionParameter(
@@ -1067,7 +1029,7 @@ def t_n_change(self, sto):
 
     def t_p_change(self, sto):
         """
-        Dimentionless volume change for the positive electrode;
+        Dimensionless volume change for the positive electrode;
         sto should be R-averaged
         """
         return pybamm.FunctionParameter(
@@ -1076,7 +1038,7 @@ def t_p_change(self, sto):
 
     def k_cr_p(self, T):
         """
-        Dimentionless cracking rate for the positive electrode;
+        Dimensionless cracking rate for the positive electrode;
         """
         T_dim = self.Delta_T * T + self.T_ref
         delta_k_cr = self.E_p ** self.m_cr_p * self.l_cr_p_0 ** (self.m_cr_p / 2 - 1)
@@ -1089,7 +1051,7 @@ def k_cr_p(self, T):
 
     def k_cr_n(self, T):
         """
-        Dimentionless cracking rate for the negative electrode;
+        Dimensionless cracking rate for the negative electrode;
         """
         T_dim = self.Delta_T * T + self.T_ref
         delta_k_cr = self.E_n ** self.m_cr_n * self.l_cr_n_0 ** (self.m_cr_n / 2 - 1)
@@ -1100,6 +1062,22 @@ def k_cr_n(self, T):
             * delta_k_cr
         )
 
+    def _set_input_current(self):
+        """Set the input current"""
+
+        self.dimensional_current_with_time = pybamm.FunctionParameter(
+            "Current function [A]", {"Time [s]": pybamm.t * self.timescale}
+        )
+        self.dimensional_current_density_with_time = (
+            self.dimensional_current_with_time
+            / (self.n_electrodes_parallel * self.geo.A_cc)
+        )
+        self.current_with_time = (
+            self.dimensional_current_with_time
+            / self.I_typ
+            * pybamm.Function(np.sign, self.I_typ)
+        )
+
     @property
     def options(self):
         return self._options
diff --git a/tests/unit/test_citations.py b/tests/unit/test_citations.py
index 34401aa42a..f443b12bd6 100644
--- a/tests/unit/test_citations.py
+++ b/tests/unit/test_citations.py
@@ -123,14 +123,14 @@ def test_subramanian_2005(self):
         citations._reset()
         self.assertNotIn("Subramanian2005", citations._papers_to_cite)
         pybamm.particle.no_distribution.XAveragedPolynomialProfile(
-            None, "Negative", "quadratic profile"
+            None, "Negative", "quadratic profile", None
         )
         self.assertIn("Subramanian2005", citations._papers_to_cite)
 
         citations._reset()
         self.assertNotIn("Subramanian2005", citations._papers_to_cite)
         pybamm.particle.no_distribution.PolynomialProfile(
-            None, "Negative", "quadratic profile"
+            None, "Negative", "quadratic profile", None
         )
         self.assertIn("Subramanian2005", citations._papers_to_cite)
 
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 ac7e735b75..f35919d005 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
@@ -36,6 +36,7 @@
 'total interfacial current density as a state': 'false' (possible: ['true', 'false'])
 'working electrode': 'both' (possible: ['both', 'negative', 'positive'])
 'SEI film resistance': 'none' (possible: ['none', 'distributed', 'average'])
+'stress-induced diffusion': 'false' (possible: ['true', 'false'])
 """  # noqa: E501
 
 
@@ -185,7 +186,7 @@ def test_options(self):
             pybamm.BaseBatteryModel({"convection": "full transverse"})
         with self.assertRaisesRegex(pybamm.OptionError, "particle"):
             pybamm.BaseBatteryModel({"particle": "bad particle"})
-        with self.assertRaisesRegex(NotImplementedError, "The 'fast diffusion'"):
+        with self.assertRaisesRegex(pybamm.OptionError, "The 'fast diffusion'"):
             pybamm.BaseBatteryModel({"particle": "fast diffusion"})
         with self.assertRaisesRegex(pybamm.OptionError, "particle shape"):
             pybamm.BaseBatteryModel({"particle shape": "bad particle shape"})
@@ -259,6 +260,11 @@ def test_options(self):
                     "plating porosity change"
                 }
             )
+        # stress-induced diffusion
+        with self.assertRaisesRegex(pybamm.OptionError, "cannot have stress"):
+            pybamm.BaseBatteryModel({"stress-induced diffusion": "true"})
+
+        # hydrolysis
         with self.assertRaisesRegex(pybamm.OptionError, "surface formulation"):
             pybamm.lead_acid.LOQS({"hydrolysis": "true", "surface form": "false"})
 
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 e95338f10a..0798d095c2 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
@@ -50,6 +50,11 @@ def test_particle_uniform(self):
         model = pybamm.lithium_ion.MPM(options)
         model.check_well_posedness()
 
+    def test_particle_quadratic(self):
+        options = {"particle": "quadratic profile"}
+        with self.assertRaises(NotImplementedError):
+            pybamm.lithium_ion.MPM(options)
+
     def test_necessary_options(self):
         options = {"particle size": "single"}
         with self.assertRaises(pybamm.OptionError):
@@ -78,7 +83,7 @@ def test_loss_active_material_stress_both_not_implemented(self):
         with self.assertRaises(NotImplementedError):
             pybamm.lithium_ion.MPM(options)
 
-    def test_well_posed_reversible_plating_with_porosity_not_implemented(self):
+    def test_reversible_plating_with_porosity_not_implemented(self):
         options = {
             "lithium plating": "reversible",
             "lithium plating porosity change": "true",
@@ -86,6 +91,11 @@ def test_well_posed_reversible_plating_with_porosity_not_implemented(self):
         with self.assertRaises(NotImplementedError):
             pybamm.lithium_ion.MPM(options)
 
+    def test_stress_induced_diffusion_not_implemented(self):
+        options = {"stress-induced diffusion": "true"}
+        with self.assertRaises(NotImplementedError):
+            pybamm.lithium_ion.MPM(options)
+
 
 class TestMPMExternalCircuits(unittest.TestCase):
     def test_well_posed_voltage(self):
diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
index 75c5b204cd..8c3d12af94 100644
--- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
+++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_spm.py
@@ -161,9 +161,10 @@ def test_new_model(self):
         self.assertEqual(new_model.timescale.id, model.timescale.id)
 
         # with custom submodels
-        model = pybamm.lithium_ion.SPM({"thermal": "x-full"}, build=False)
+        options = {"stress-induced diffusion": "false", "thermal": "x-full"}
+        model = pybamm.lithium_ion.SPM(options, build=False)
         particle_n = pybamm.particle.no_distribution.XAveragedPolynomialProfile(
-            model.param, "Negative", "quadratic profile"
+            model.param, "Negative", "quadratic profile", options
         )
         model.submodels["negative particle"] = particle_n
         model.build_model()
@@ -256,6 +257,14 @@ def test_well_posed_both_swelling_only(self):
         model = pybamm.lithium_ion.SPM(options)
         model.check_well_posedness()
 
+    def test_stress_induced_diffusion_mixed(self):
+        options = {
+            "particle mechanics": "swelling only",
+            "stress-induced diffusion": ("true", "false"),
+        }
+        model = pybamm.lithium_ion.SPM(options)
+        model.check_well_posedness()
+
 
 class TestSPMWithPlating(unittest.TestCase):
     def test_well_posed_none_plating(self):