diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9c890af623..a8ceea7965 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -31,6 +31,7 @@
 
 ## Breaking changes
 
+-    The parameters "Positive/Negative particle distribution in x" and "Positive/Negative surface area per unit volume distribution in x" have been deprecated. Instead, users can provide "Positive/Negative particle radius [m]" and "Positive/Negative surface area per unit volume [m-1]" directly as functions of through-cell position (x [m]) ([#1237](https://github.com/pybamm-team/PyBaMM/pull/1237)) 
 
 # [v0.2.4](https://github.com/pybamm-team/PyBaMM/tree/v0.2.4) - 2020-09-07
 
diff --git a/examples/notebooks/compare-ecker-data.ipynb b/examples/notebooks/compare-ecker-data.ipynb
index 1bcd6bd985..f963d17b56 100644
--- a/examples/notebooks/compare-ecker-data.ipynb
+++ b/examples/notebooks/compare-ecker-data.ipynb
@@ -103,8 +103,8 @@
     "    var.x_n: int(parameter_values.evaluate(model.param.L_n / 1e-6)),\n",
     "    var.x_s: int(parameter_values.evaluate(model.param.L_s / 1e-6)),\n",
     "    var.x_p: int(parameter_values.evaluate(model.param.L_p / 1e-6)),\n",
-    "    var.r_n: int(parameter_values.evaluate(model.param.R_n / 1e-7)),\n",
-    "    var.r_p: int(parameter_values.evaluate(model.param.R_p / 1e-7)),\n",
+    "    var.r_n: int(parameter_values.evaluate(model.param.R_n_typ / 1e-7)),\n",
+    "    var.r_p: int(parameter_values.evaluate(model.param.R_p_typ / 1e-7)),\n",
     "}"
    ]
   },
@@ -221,13 +221,6 @@
     "\n",
     "[2] Richardson, Giles, et. al. \"Generalised single particle models for high-rate operation of graded lithium-ion electrodes: Systematic derivation and validation.\" Electrochemica Acta 339 (2020): 135862"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/examples/notebooks/models/DFN.ipynb b/examples/notebooks/models/DFN.ipynb
index 7dcf280071..a60a823289 100644
--- a/examples/notebooks/models/DFN.ipynb
+++ b/examples/notebooks/models/DFN.ipynb
@@ -71,7 +71,7 @@
     "\n",
     "####  Concentration in the electrode active material:\n",
     "\\begin{gather}\n",
-    "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - \\frac{a_{\\text{k}}\\gamma_{\\text{k}}}{\\mathcal{C}_{\\text{k}}} N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = j_{\\text{k}}, \\quad \\text{k} \\in \\text{n, p}.\n",
+    "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - \\frac{a_{R, \\text{k}}\\gamma_{\\text{k}}}{\\mathcal{C}_{\\text{k}}} N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = j_{\\text{k}}, \\quad \\text{k} \\in \\text{n, p}.\n",
     "\\end{gather}\n",
     "\n",
     "#### Reference potential:\n",
@@ -203,7 +203,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b72126bb294743c38f4aa5069787b9b6",
+       "model_id": "eceebcfd564d4b53bf56bda5abf066ff",
        "version_major": 2,
        "version_minor": 0
       },
@@ -240,7 +240,7 @@
     "|$\\mathcal{C}_{\\text{k}}$   | $\\tau_{\\text{k}}^*/\\tau_{\\text{d}}^*$   | Ratio of solid diffusion and discharge timescales |\n",
     "|$\\mathcal{C}_{\\text{e}}$   |$\\tau_{\\text{e}}^*/\\tau_{\\text{d}}^*$    |Ratio of electrolyte transport and discharge timescales|\n",
     "|$\\mathcal{C}_{\\text{r,k}}$ |$\\tau_{\\text{r,k}}^*/\\tau_{\\text{d}}^*$  |Ratio of reaction and discharge timescales|\n",
-    "|$a_{\\text{k}}$             |$a_{\\text{k}}^* R_{\\text{k}}^*$          | Product of particle radius and surface area to volume ratio|\n",
+    "|$a_{R, \\text{k}}$             |$a_{\\text{k}}^* R_{\\text{k}}^*$          | Product of particle radius and surface area to volume ratio|\n",
     "|$\\gamma_{\\text{k}}$        |$c_{\\text{k,max}}^*/c_{\\text{n,max}}^*$  |Ratio of maximum lithium concentrations in solid|\n",
     "|$\\gamma_{\\text{e}}$        |$c_{\\text{e,typ}}^*/c_{\\text{n,max}}^*$  |Ratio of maximum lithium concentration in the negative electrode solid and typical electrolyte concentration|\n",
     "\n",
diff --git a/examples/notebooks/models/SPM.ipynb b/examples/notebooks/models/SPM.ipynb
index 3ac578ad45..05cdb0da64 100644
--- a/examples/notebooks/models/SPM.ipynb
+++ b/examples/notebooks/models/SPM.ipynb
@@ -23,7 +23,7 @@
     "\\mathcal{C}_{\\text{k}} \\frac{\\partial c_{\\text{s,k}}}{\\partial t} &= -\\frac{1}{r_{\\text{k}}^2} \\frac{\\partial}{\\partial r_{\\text{k}}} \\left(r_{\\text{k}}^2 N_{\\text{s,k}}\\right), \\\\\n",
     "N_{\\text{s,k}} &= -D_{\\text{s,k}}(c_{\\text{s,k}}) \\frac{\\partial c_{\\text{s,k}}}{\\partial r_{\\text{k}}}, \\quad \\text{k} \\in \\text{n, p}, \\end{align}\n",
     "$$\n",
-    "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - \\frac{a_{\\text{k}}\\gamma_{\\text{k}}}{\\mathcal{C}_{\\text{k}}} N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \n",
+    "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - \\frac{a_{R, \\text{k}}\\gamma_{\\text{k}}}{\\mathcal{C}_{\\text{k}}} N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \n",
     "\\begin{cases}\n",
     "\t\t  \\frac{I}{L_{\\text{n}}}, \\quad &\\text{k}=\\text{n}, \\\\ \n",
     "\t\t  -\\frac{I}{L_{\\text{p}}}, \\quad &\\text{k}=\\text{p}, \n",
@@ -57,7 +57,15 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install pybamm -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
@@ -152,11 +160,11 @@
      "text": [
       "SPM domains:\n",
       "1. negative electrode with variables:\n",
-      "  -( 0 ) <= x_n <= ( Negative electrode thickness [m] / Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] )\n",
+      "  -( 0 ) <= x_n <= ( Negative electrode thickness [m] / (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) )\n",
       "2. separator with variables:\n",
-      "  -( Negative electrode thickness [m] / Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] ) <= x_s <= ( Negative electrode thickness [m] / Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] + Separator thickness [m] / Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] )\n",
+      "  -( Negative electrode thickness [m] / (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) ) <= x_s <= ( Negative electrode thickness [m] / (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) + Separator thickness [m] / (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) )\n",
       "3. positive electrode with variables:\n",
-      "  -( Negative electrode thickness [m] / Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] + Separator thickness [m] / Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m] ) <= x_p <= ( 1 )\n",
+      "  -( Negative electrode thickness [m] / (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) + Separator thickness [m] / (Negative electrode thickness [m] + Separator thickness [m] + Positive electrode thickness [m]) ) <= x_p <= ( 1 )\n",
       "4. negative particle with variables:\n",
       "  -( 0 ) <= r_n <= ( 1 )\n",
       "5. positive particle with variables:\n",
@@ -239,8 +247,8 @@
       "x_n has 20 mesh points\n",
       "x_s has 20 mesh points\n",
       "x_p has 20 mesh points\n",
-      "r_n has 10 mesh points\n",
-      "r_p has 10 mesh points\n",
+      "r_n has 30 mesh points\n",
+      "r_p has 30 mesh points\n",
       "y has 10 mesh points\n",
       "z has 10 mesh points\n"
      ]
@@ -499,6 +507,10 @@
       "\t- Negative particle concentration [mol.m-3]\n",
       "\t- X-averaged negative particle concentration\n",
       "\t- X-averaged negative particle concentration [mol.m-3]\n",
+      "\t- R-averaged negative particle concentration\n",
+      "\t- R-averaged negative particle concentration [mol.m-3]\n",
+      "\t- Average negative particle concentration\n",
+      "\t- Average negative particle concentration [mol.m-3]\n",
       "\t- Negative particle surface concentration\n",
       "\t- Negative particle surface concentration [mol.m-3]\n",
       "\t- X-averaged negative particle surface concentration\n",
@@ -506,11 +518,29 @@
       "\t- Negative electrode active volume fraction\n",
       "\t- Negative electrode volume-averaged concentration\n",
       "\t- Negative electrode volume-averaged concentration [mol.m-3]\n",
-      "\t- Negative electrode average extent of lithiation\n",
+      "\t- Negative electrode extent of lithiation\n",
+      "\t- X-averaged negative electrode extent of lithiation\n",
+      "\t- Total lithium in negative electrode [mol]\n",
+      "\t- Minimum negative particle concentration\n",
+      "\t- Maximum negative particle concentration\n",
+      "\t- Minimum negative particle concentration [mol.m-3]\n",
+      "\t- Maximum negative particle concentration [mol.m-3]\n",
+      "\t- Minimum negative particle surface concentration\n",
+      "\t- Maximum negative particle surface concentration\n",
+      "\t- Minimum negative particle surface concentration [mol.m-3]\n",
+      "\t- Maximum negative particle surface concentration [mol.m-3]\n",
+      "\t- Negative particle radius\n",
+      "\t- Negative particle radius [m]\n",
+      "\t- Negative surface area per unit volume\n",
+      "\t- Negative surface area per unit volume [m-1]\n",
       "\t- Positive particle concentration\n",
       "\t- Positive particle concentration [mol.m-3]\n",
       "\t- X-averaged positive particle concentration\n",
       "\t- X-averaged positive particle concentration [mol.m-3]\n",
+      "\t- R-averaged positive particle concentration\n",
+      "\t- R-averaged positive particle concentration [mol.m-3]\n",
+      "\t- Average positive particle concentration\n",
+      "\t- Average positive particle concentration [mol.m-3]\n",
       "\t- Positive particle surface concentration\n",
       "\t- Positive particle surface concentration [mol.m-3]\n",
       "\t- X-averaged positive particle surface concentration\n",
@@ -518,7 +548,21 @@
       "\t- Positive electrode active volume fraction\n",
       "\t- Positive electrode volume-averaged concentration\n",
       "\t- Positive electrode volume-averaged concentration [mol.m-3]\n",
-      "\t- Positive electrode average extent of lithiation\n",
+      "\t- Positive electrode extent of lithiation\n",
+      "\t- X-averaged positive electrode extent of lithiation\n",
+      "\t- Total lithium in positive electrode [mol]\n",
+      "\t- Minimum positive particle concentration\n",
+      "\t- Maximum positive particle concentration\n",
+      "\t- Minimum positive particle concentration [mol.m-3]\n",
+      "\t- Maximum positive particle concentration [mol.m-3]\n",
+      "\t- Minimum positive particle surface concentration\n",
+      "\t- Maximum positive particle surface concentration\n",
+      "\t- Minimum positive particle surface concentration [mol.m-3]\n",
+      "\t- Maximum positive particle surface concentration [mol.m-3]\n",
+      "\t- Positive particle radius\n",
+      "\t- Positive particle radius [m]\n",
+      "\t- Positive surface area per unit volume\n",
+      "\t- Positive surface area per unit volume [m-1]\n",
       "\t- Electrolyte concentration\n",
       "\t- Electrolyte concentration [mol.m-3]\n",
       "\t- Electrolyte concentration [Molar]\n",
@@ -578,6 +622,7 @@
       "\t- Total negative electrode sei thickness [m]\n",
       "\t- X-averaged total negative electrode sei thickness\n",
       "\t- X-averaged total negative electrode sei thickness [m]\n",
+      "\t- X-averaged negative electrode resistance [Ohm.m2]\n",
       "\t- Inner negative electrode sei concentration [mol.m-3]\n",
       "\t- X-averaged inner negative electrode sei concentration [mol.m-3]\n",
       "\t- Outer negative electrode sei concentration [mol.m-3]\n",
@@ -599,7 +644,13 @@
       "\t- X-averaged negative electrode sei interfacial current density [A.m-2]\n",
       "\t- Inner positive electrode sei thickness\n",
       "\t- Inner positive electrode sei thickness [m]\n",
-      "\t- X-averaged inner positive electrode sei thickness\n",
+      "\t- X-averaged inner positive electrode sei thickness\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "\t- X-averaged inner positive electrode sei thickness [m]\n",
       "\t- Outer positive electrode sei thickness\n",
       "\t- Outer positive electrode sei thickness [m]\n",
@@ -609,6 +660,7 @@
       "\t- Total positive electrode sei thickness [m]\n",
       "\t- X-averaged total positive electrode sei thickness\n",
       "\t- X-averaged total positive electrode sei thickness [m]\n",
+      "\t- X-averaged positive electrode resistance [Ohm.m2]\n",
       "\t- Inner positive electrode sei concentration [mol.m-3]\n",
       "\t- X-averaged inner positive electrode sei concentration [mol.m-3]\n",
       "\t- Outer positive electrode sei concentration [mol.m-3]\n",
@@ -657,12 +709,25 @@
       "\t- X-averaged negative particle flux\n",
       "\t- Positive particle flux\n",
       "\t- X-averaged positive particle flux\n",
+      "\t- Total concentration in electrolyte [mol]\n",
       "\t- Ohmic heating\n",
       "\t- Ohmic heating [W.m-3]\n",
+      "\t- X-averaged Ohmic heating\n",
+      "\t- X-averaged Ohmic heating [W.m-3]\n",
+      "\t- Volume-averaged Ohmic heating\n",
+      "\t- Volume-averaged Ohmic heating [W.m-3]\n",
       "\t- Irreversible electrochemical heating\n",
       "\t- Irreversible electrochemical heating [W.m-3]\n",
+      "\t- X-averaged irreversible electrochemical heating\n",
+      "\t- X-averaged irreversible electrochemical heating [W.m-3]\n",
+      "\t- Volume-averaged irreversible electrochemical heating\n",
+      "\t- Volume-averaged irreversible electrochemical heating[W.m-3]\n",
       "\t- Reversible heating\n",
       "\t- Reversible heating [W.m-3]\n",
+      "\t- X-averaged reversible heating\n",
+      "\t- X-averaged reversible heating [W.m-3]\n",
+      "\t- Volume-averaged reversible heating\n",
+      "\t- Volume-averaged reversible heating [W.m-3]\n",
       "\t- Total heating\n",
       "\t- Total heating [W.m-3]\n",
       "\t- X-averaged total heating\n",
@@ -873,6 +938,10 @@
       "\t- X-averaged battery electrolyte ohmic losses [V]\n",
       "\t- X-averaged battery concentration overpotential [V]\n",
       "\t- Battery voltage [V]\n",
+      "\t- Change in measured open circuit voltage\n",
+      "\t- Change in measured open circuit voltage [V]\n",
+      "\t- Local ECM resistance\n",
+      "\t- Local ECM resistance [Ohm]\n",
       "\t- Terminal power [W]\n"
      ]
     }
@@ -915,7 +984,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 936x288 with 3 Axes>"
       ]
@@ -974,7 +1043,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5b1ede34a7c9439db8d9707083ae0c54",
+       "model_id": "78331f956c1345dbb8cb24df52745532",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1023,7 +1092,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "55ac9b31b73049408ab8a87c8863c244",
+       "model_id": "68caf5d90efa4d93b943ede761585424",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1058,7 +1127,7 @@
     "| $L_{\\text{k}}$            | $L_{\\text{k}}^*/L^*$                    | Ratio of region thickness to cell thickness|\n",
     "|$\\mathcal{C}_{\\text{k}}$   | $\\tau_{\\text{k}}^*/\\tau_{\\text{d}}^*$   | Ratio of solid diffusion and discharge timescales |\n",
     "|$\\mathcal{C}_{\\text{r,k}}$ |$\\tau_{\\text{r,k}}^*/\\tau_{\\text{d}}^*$  |Ratio of reaction and discharge timescales|\n",
-    "|$a_{\\text{k}}$             |$a_{\\text{k}}^* R_{\\text{k}}^*$          | Product of particle radius and surface area to volume ratio|\n",
+    "|$a_{R, \\text{k}}$             |$a_{\\text{k}}^* R_{\\text{k}}^*$          | Product of particle radius and surface area to volume ratio|\n",
     "|$\\gamma_{\\text{k}}$        |$c_{\\text{k,max}}^*/c_{\\text{n,max}}^*$  |Ratio of maximum lithium concentrations in solid|"
    ]
   },
diff --git a/examples/notebooks/models/SPMe.ipynb b/examples/notebooks/models/SPMe.ipynb
index b9059f081b..a1927b2629 100644
--- a/examples/notebooks/models/SPMe.ipynb
+++ b/examples/notebooks/models/SPMe.ipynb
@@ -33,7 +33,7 @@
     "\\mathcal{C}_{\\text{k}} \\frac{\\partial c_{\\text{s,k}}}{\\partial t} &= -\\frac{1}{r_{\\text{k}}^2} \\frac{\\partial}{\\partial r_{\\text{k}}} \\left(r_{\\text{k}}^2 N_{\\text{s,k}}\\right), \\\\\n",
     "N_{\\text{s,k}} &= -D_{\\text{s,k}}(c_{\\text{s,k}}) \\frac{\\partial c_{\\text{s,k}}}{\\partial r_{\\text{k}}}, \\quad \\text{k} \\in \\text{n, p}, \\end{align}\n",
     "$$\n",
-    "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - \\frac{a_{\\text{k}}\\gamma_{\\text{k}}}{\\mathcal{C}_{\\text{k}}} N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \n",
+    "N_{\\text{s,k}}\\big|_{r_{\\text{k}}=0} = 0, \\quad \\text{k} \\in \\text{n, p}, \\quad \\ \\ - \\frac{a_{R, \\text{k}}\\gamma_{\\text{k}}}{\\mathcal{C}_{\\text{k}}} N_{\\text{s,k}}\\big|_{r_{\\text{k}}=1} = \n",
     "\\begin{cases}\n",
     "\t\t  \\frac{I}{L_{\\text{n}}}, \\quad &\\text{k}=\\text{n}, \\\\ \n",
     "\t\t  -\\frac{I}{L_{\\text{p}}}, \\quad &\\text{k}=\\text{p}, \n",
@@ -155,7 +155,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.solvers.solution.Solution at 0x7f69f08dd2e8>"
+       "<pybamm.solvers.solution.Solution at 0x7f5cd829b4a8>"
       ]
      },
      "execution_count": 3,
@@ -183,7 +183,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47ee8d1901bd4bdd87fba1d347da16ec",
+       "model_id": "5f19fdea006d4f22ae77a584c87d5eb3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -218,7 +218,7 @@
     "|$\\mathcal{C}_{\\text{k}}$   | $\\tau_{\\text{k}}^*/\\tau_{\\text{d}}^*$   | Ratio of solid diffusion and discharge timescales |\n",
     "|$\\mathcal{C}_{\\text{e}}$   |$\\tau_{\\text{e}}^*/\\tau_{\\text{d}}^*$    |Ratio of electrolyte transport and discharge timescales|\n",
     "|$\\mathcal{C}_{\\text{r,k}}$ |$\\tau_{\\text{r,k}}^*/\\tau_{\\text{d}}^*$  |Ratio of reaction and discharge timescales|\n",
-    "|$a_{\\text{k}}$             |$a_{\\text{k}}^* R_{\\text{k}}^*$          | Product of particle radius and surface area to volume ratio|\n",
+    "|$a_{R, \\text{k}}$             |$a_{\\text{k}}^* R_{\\text{k}}^*$          | Product of particle radius and surface area to volume ratio|\n",
     "|$\\gamma_{\\text{k}}$        |$c_{\\text{k,max}}^*/c_{\\text{n,max}}^*$  |Ratio of maximum lithium concentrations in solid|\n",
     "|$\\gamma_{\\text{e}}$        |$c_{\\text{e,typ}}^*/c_{\\text{n,max}}^*$  |Ratio of maximum lithium concentration in the negative electrode solid and typical electrolyte concentration|\n",
     "\n",
diff --git a/examples/notebooks/models/compare-lithium-ion.ipynb b/examples/notebooks/models/compare-lithium-ion.ipynb
index 3d3ee5dc80..5d904ace04 100644
--- a/examples/notebooks/models/compare-lithium-ion.ipynb
+++ b/examples/notebooks/models/compare-lithium-ion.ipynb
@@ -36,7 +36,15 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install pybamm -q    # install PyBaMM if it is not installed\n",
     "import pybamm\n",
@@ -103,7 +111,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x13ac79590>"
+       "<pybamm.models.full_battery_models.lithium_ion.dfn.DFN at 0x7f505040a198>"
       ]
      },
      "execution_count": 4,
@@ -248,9 +256,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Solved the Doyle-Fuller-Newman model in 0.420 seconds\n",
-      "Solved the Single Particle Model in 0.071 seconds\n",
-      "Solved the Single Particle Model with electrolyte in 0.190 seconds\n"
+      "Solved the Doyle-Fuller-Newman model in 0.604 seconds\n",
+      "Solved the Single Particle Model in 0.055 seconds\n",
+      "Solved the Single Particle Model with electrolyte in 0.081 seconds\n"
      ]
     }
    ],
@@ -288,7 +296,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -341,7 +349,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "94384243ffee420db59217c59b2dcf6e",
+       "model_id": "ab6a3a9354d14b7cbdff69d2cb0fc8b6",
        "version_major": 2,
        "version_minor": 0
       },
@@ -380,7 +388,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "380e2f7a84974c6fa04c382c17e7dba8",
+       "model_id": "021df018d33c43b488aa2e16597563b0",
        "version_major": 2,
        "version_minor": 0
       },
@@ -443,7 +451,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.8"
+   "version": "3.6.9"
   }
  },
  "nbformat": 4,
diff --git a/examples/notebooks/models/pouch-cell-model.ipynb b/examples/notebooks/models/pouch-cell-model.ipynb
index fd3d054500..bd86489183 100644
--- a/examples/notebooks/models/pouch-cell-model.ipynb
+++ b/examples/notebooks/models/pouch-cell-model.ipynb
@@ -91,7 +91,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/user/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:361: UserWarning: 1+1D Thermal models are only valid if both tabs are placed at the top of the cell.\n",
+      "/home/user/Documents/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:375: UserWarning: 1+1D Thermal models are only valid if both tabs are placed at the top of the cell.\n",
       "  \"1+1D Thermal models are only valid if both tabs are \"\n"
      ]
     }
@@ -339,7 +339,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We then add the solution object from the 1+1D model. This is just so that PyBaMM uses the same times behind the scenes when dealing with COMSOL model and the reduced-order models: the variables in `comsol_model.variables` are functions of time only that return the (interpolated in space) COMSOL solution."
+    "We then add the solution object from the 1+1D model. This is just so that PyBaMM uses the same (dimensionless) times behind the scenes when dealing with COMSOL model and the reduced-order models: the variables in `comsol_model.variables` are functions of time only that return the (interpolated in space) COMSOL solution. We also need to update the time and length scales for the COMSOL model so that any dimensionless variables are scaled correctly. "
    ]
   },
   {
diff --git a/examples/scripts/compare_lithium_ion_particle_distribution.py b/examples/scripts/compare_lithium_ion_particle_distribution.py
index 04ead95b71..4bd5fc7382 100644
--- a/examples/scripts/compare_lithium_ion_particle_distribution.py
+++ b/examples/scripts/compare_lithium_ion_particle_distribution.py
@@ -16,17 +16,22 @@
 params = [models[0].default_parameter_values, models[1].default_parameter_values]
 
 
-def negative_distribution(x):
-    return 1 + 2 * x / models[1].param.l_n
+def negative_radius(x):
+    "Negative particle radius as a function of through-cell position (x_n [m])"
+    R_n_0 = params[0]["Negative particle radius [m]"]
+    grading = 1 + 2 * x / models[1].param.L_n
+    return grading * R_n_0
 
 
-def positive_distribution(x):
-    return 1 + 2 * (1 - x) / models[1].param.l_p
+def positive_radius(x):
+    "Positive particle radius as a function of through-cell position (x_p [m])"
+    R_p_0 = params[0]["Positive particle radius [m]"]
+    grading = 1 + 2 * (models[1].param.L_x - x) / models[1].param.L_p
+    return grading * R_p_0
 
 
-params[1]["Negative particle distribution in x"] = negative_distribution
-params[1]["Positive particle distribution in x"] = positive_distribution
-
+params[1]["Negative particle radius [m]"] = negative_radius
+params[1]["Positive particle radius [m]"] = positive_radius
 
 # set up and solve simulations
 t_eval = np.linspace(0, 3600, 100)
@@ -45,8 +50,8 @@ def positive_distribution(x):
     "Electrolyte potential [V]",
     "Positive electrode potential [V]",
     "Terminal voltage [V]",
-    "Negative particle distribution in x",
-    "Positive particle distribution in x",
+    "Negative particle radius",
+    "Positive particle radius",
 ]
 
 # plot
diff --git a/examples/scripts/compare_particle_shape.py b/examples/scripts/compare_particle_shape.py
index 0b09e868fe..393dd85211 100644
--- a/examples/scripts/compare_particle_shape.py
+++ b/examples/scripts/compare_particle_shape.py
@@ -27,8 +27,6 @@
             {
                 "Negative electrode surface area to volume ratio [m-1]": 170000,
                 "Positive electrode surface area to volume ratio [m-1]": 200000,
-                "Negative surface area per unit volume distribution in x": 1,
-                "Positive surface area per unit volume distribution in x": 1,
             },
             check_already_exists=False,
         )
diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/parameters.csv
index 36af231e09..257a9f8f6e 100644
--- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/parameters.csv
@@ -11,7 +11,6 @@ Negative electrode OCP [V],[data]graphite_LGM50_ocp_Chen2020,Chen 2020,
 Negative electrode porosity,0.25,Chen 2020,
 Negative electrode active material volume fraction,0.75,Chen 2020,
 Negative particle radius [m],5.86E-6,Chen 2020,
-Negative particle distribution in x,1,default,
 Negative electrode Bruggeman coefficient (electrolyte),1.5,Chen 2020,theoretical
 Negative electrode Bruggeman coefficient (electrode),1.5,default,
 ,,,
diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv
index f59ba699ae..771e702884 100644
--- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv
@@ -13,7 +13,6 @@ Negative electrode OCP [V],[function]graphite_ocp_Ecker2015_function,,
 Negative electrode porosity,0.329,,
 Negative electrode active material volume fraction, 0.372403,,
 Negative particle radius [m],1.37E-05,,
-Negative particle distribution in x,1,,
 Negative electrode Bruggeman coefficient (electrolyte),1.6372789338386007,Solve for permeability factor B=0.162=eps^b,
 Negative electrode Bruggeman coefficient (electrode),0,No Bruggeman correction to the solid conductivity,
 ,,,
diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/parameters.csv
index 7322e75402..0469e22fd8 100644
--- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/parameters.csv
@@ -11,7 +11,6 @@ Negative electrode OCP [V],[function]graphite_ocp_Kim2011,
 Negative electrode porosity,0.4,,
 Negative electrode active material volume fraction,0.51,,
 Negative particle radius [m],5.083E-7,,
-Negative particle distribution in x,1,,
 Negative electrode Bruggeman coefficient (electrolyte),2,,
 Negative electrode Bruggeman coefficient (electrode),2,,
 ,,,
diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/parameters.csv
index 12fba05b21..6d6511094a 100644
--- a/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_Ramadass2004/parameters.csv
@@ -11,7 +11,6 @@ Negative electrode OCP [V],[function]graphite_ocp_Ramadass2004,
 Negative electrode porosity,0.485,Ramadass,electrolyte volume fraction
 Negative electrode active material volume fraction,0.49,Ramadass,
 Negative particle radius [m],2e-06,Ramadass,
-Negative particle distribution in x,1,,
 Negative electrode Bruggeman coefficient (electrolyte),4,Guess,
 Negative electrode Bruggeman coefficient (electrode),4,Ramadass,
 ,,,
diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/parameters.csv
index 7f4b9188fb..315908eecb 100644
--- a/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/parameters.csv
@@ -12,7 +12,6 @@ Negative electrode OCP [V],[function]graphite_ocp_PeymanMPM,Peyman MPM,
 Negative electrode porosity,0.3,Peyman MPM,
 Negative electrode active material volume fraction,0.61,Peyman MPM,rest is binder
 Negative particle radius [m],2.5E-06,Peyman MPM,
-Negative particle distribution in x,1,,
 Negative electrode Bruggeman coefficient (electrode),1.5,Peyman MPM,
 Negative electrode Bruggeman coefficient (electrolyte),1.5,Peyman MPM,
 Negative electrode tortuosity, 0.16,
diff --git a/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv b/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv
index 02f5ad6747..abc612b06d 100644
--- a/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv
@@ -11,7 +11,6 @@ Negative electrode OCP [V],[function]graphite_mcmb2528_ocp_Dualfoil1998,
 Negative electrode porosity,0.3,Scott Moura FastDFN,electrolyte volume fraction
 Negative electrode active material volume fraction,0.6,,
 Negative particle radius [m],1E-05,Scott Moura FastDFN,
-Negative particle distribution in x,1,,
 Negative electrode Bruggeman coefficient (electrolyte),1.5,Scott Moura FastDFN,
 Negative electrode Bruggeman coefficient (electrode),1.5,Scott Moura FastDFN,
 ,,,
diff --git a/pybamm/input/parameters/lithium-ion/cathodes/LFP_Prada2013/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/LFP_Prada2013/parameters.csv
index c500cc53e8..f2de48cd58 100644
--- a/pybamm/input/parameters/lithium-ion/cathodes/LFP_Prada2013/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/cathodes/LFP_Prada2013/parameters.csv
@@ -11,7 +11,6 @@ Positive electrode OCP [V],[function]LFP_ocp_ashfar2017,,
 Positive electrode porosity,0.12728395,Calculation minimized to Severson,
 Positive electrode active material volume fraction,0.28485556,Calculation minimized to Severson,
 Positive particle radius [m],1.00E-08,Calculation minimized to Severson,
-Positive particle distribution in x,1,,
 Positive electrode Bruggeman coefficient (electrode),1.5,,
 Positive electrode Bruggeman coefficient (electrolyte),1.5,,
 ,,,
diff --git a/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv
index 3e13409322..13cb9769b5 100644
--- a/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/cathodes/LiNiCoO2_Ecker2015/parameters.csv
@@ -13,7 +13,6 @@ Positive electrode OCP [V],[function]nco_ocp_Ecker2015_function,,
 Positive electrode porosity,0.296,,
 Positive electrode active material volume fraction, 0.40832,,
 Positive particle radius [m],6.5E-06,,
-Positive particle distribution in x,1,,
 Positive electrode Bruggeman coefficient (electrolyte),1.5442267190786427,Solve for permeability factor B=0.1526=eps^b,
 Positive electrode Bruggeman coefficient (electrode),0,No Bruggeman correction to solid conductivity,
 Positive electrode exchange-current density [A.m-2],[function]nco_electrolyte_exchange_current_density_Ecker2015,,
diff --git a/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/parameters.csv
index 94d301c422..ef0ac497f6 100644
--- a/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/cathodes/NMC_UMBL_Mohtat2020/parameters.csv
@@ -11,7 +11,6 @@ Positive electrode OCP [V],[function]NMC_ocp_PeymanMPM,,
 Positive electrode porosity,0.3,Peyman MPM,
 Positive electrode active material volume fraction,0.445,Peyman MPM,rest is binder
 Positive particle radius [m],3.5E-06,Peyman MPM,
-Positive particle distribution in x,1,,
 Positive electrode Bruggeman coefficient (electrode),1.5,Peyman MPM,
 Positive electrode Bruggeman coefficient (electrolyte),1.5,Peyman MPM,
 Positive electrode tortuosity,0.16,
diff --git a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv
index 93d0b43ed0..99cec014e3 100644
--- a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv
@@ -11,7 +11,6 @@ Positive electrode OCP [V],[function]lico2_ocp_Dualfoil1998,,
 Positive electrode porosity,0.3,Scott Moura FastDFN,electrolyte volume fraction
 Positive electrode active material volume fraction,0.5,,
 Positive particle radius [m],1E-05,Scott Moura FastDFN,
-Positive particle distribution in x,1,,
 Positive electrode Bruggeman coefficient (electrolyte),1.5,Scott Moura FastDFN,
 Positive electrode Bruggeman coefficient (electrode),1.5,Scott Moura FastDFN,
 ,,,
diff --git a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/parameters.csv
index 8d26275f26..08954e4444 100644
--- a/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/cathodes/lico2_Ramadass2004/parameters.csv
@@ -11,7 +11,6 @@ Positive electrode OCP [V],[function]lico2_ocp_Ramadass2004,,
 Positive electrode porosity,0.385,Ramadass,electrolyte volume fraction
 Positive electrode active material volume fraction,0.59,Ramadass,
 Positive particle radius [m],2e-06,Ramadass,
-Positive particle distribution in x,1,,
 Positive electrode Bruggeman coefficient (electrolyte),4,Ramadass,
 Positive electrode Bruggeman coefficient (electrode),4,Ramadass,
 ,,,
diff --git a/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/parameters.csv
index 0a0f00fe42..767566934b 100644
--- a/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/cathodes/nca_Kim2011/parameters.csv
@@ -11,7 +11,6 @@ Positive electrode OCP [V],[data]nca_ocp_Kim2011_data,
 Positive electrode porosity,0.4,,
 Positive electrode active material volume fraction,0.41,,
 Positive particle radius [m],1.633E-6,,
-Positive particle distribution in x,1,,
 Positive electrode Bruggeman coefficient (electrolyte),2,,
 Positive electrode Bruggeman coefficient (electrode),2,,
 ,,,
diff --git a/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/parameters.csv b/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/parameters.csv
index 997f4ba416..c49b71cf93 100644
--- a/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/parameters.csv
+++ b/pybamm/input/parameters/lithium-ion/cathodes/nmc_Chen2020/parameters.csv
@@ -11,7 +11,6 @@ Positive electrode OCP [V],[data]nmc_LGM50_ocp_Chen2020,Chen 2020,
 Positive electrode porosity,0.335,Chen 2020,
 Positive electrode active material volume fraction,0.665,Chen 2020,
 Positive particle radius [m],5.22E-6,Chen 2020,
-Positive particle distribution in x,1,default,
 Positive electrode Bruggeman coefficient (electrolyte),1.5,Chen 2020,theoretical
 Positive electrode Bruggeman coefficient (electrode),1.5,default,
 ,,,
diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py
index 74b1d10b6c..1e7369eaa8 100644
--- a/pybamm/models/full_battery_models/base_battery_model.py
+++ b/pybamm/models/full_battery_models/base_battery_model.py
@@ -801,9 +801,7 @@ def set_voltage_variables(self):
         # Battery-wide variables
         V_dim = self.variables["Terminal voltage [V]"]
         eta_e_av = self.variables.get("X-averaged electrolyte ohmic losses", 0)
-        eta_c_av = self.variables.get(
-            "X-averaged concentration overpotential", 0
-        )
+        eta_c_av = self.variables.get("X-averaged concentration overpotential", 0)
         eta_e_av_dim = self.variables.get("X-averaged electrolyte ohmic losses [V]", 0)
         eta_c_av_dim = self.variables.get(
             "X-averaged concentration overpotential [V]", 0
@@ -844,8 +842,13 @@ def set_voltage_variables(self):
         i_cc_dim = self.variables["Current collector current density [A.m-2]"]
         # Gather all overpotentials
         v_ecm = -(eta_ocv + eta_r_av + eta_c_av + eta_e_av + delta_phi_s_av)
-        v_ecm_dim = -(eta_ocv_dim + eta_r_av_dim + eta_c_av_dim + eta_e_av_dim +
-                      delta_phi_s_av_dim)
+        v_ecm_dim = -(
+            eta_ocv_dim
+            + eta_r_av_dim
+            + eta_c_av_dim
+            + eta_e_av_dim
+            + delta_phi_s_av_dim
+        )
         # Current collector area for turning resistivity into resistance
         A_cc = self.param.A_cc
         self.variables.update(
diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
index 3371f4bd6b..991ace494a 100644
--- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
+++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py
@@ -25,8 +25,8 @@ def __init__(self, options=None, name="Unnamed lithium-ion model", build=False):
             "negative electrode": self.param.L_x,
             "separator": self.param.L_x,
             "positive electrode": self.param.L_x,
-            "negative particle": self.param.R_n,
-            "positive particle": self.param.R_p,
+            "negative particle": self.param.R_n_typ,
+            "positive particle": self.param.R_p_typ,
             "current collector y": self.param.L_y,
             "current collector z": self.param.L_z,
         }
@@ -40,9 +40,9 @@ def set_standard_output_variables(self):
         self.variables.update(
             {
                 "r_n": var.r_n,
-                "r_n [m]": var.r_n * self.param.R_n,
+                "r_n [m]": var.r_n * self.param.R_n_typ,
                 "r_p": var.r_p,
-                "r_p [m]": var.r_p * self.param.R_p,
+                "r_p [m]": var.r_p * self.param.R_p_typ,
             }
         )
 
diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py
index 4e246d6fa1..bc708999cf 100644
--- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py
+++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn.py
@@ -167,14 +167,18 @@ def __init__(self, name="Doyle-Fuller-Newman model"):
         self.boundary_conditions[c_s_n] = {
             "left": (pybamm.Scalar(0), "Neumann"),
             "right": (
-                -param.C_n * j_n / param.a_n / param.D_n(c_s_surf_n, T),
+                -param.C_n * j_n / param.a_R_n / param.D_n(c_s_surf_n, T),
                 "Neumann",
             ),
         }
         self.boundary_conditions[c_s_p] = {
             "left": (pybamm.Scalar(0), "Neumann"),
             "right": (
-                -param.C_p * j_p / param.a_p / param.gamma_p / param.D_p(c_s_surf_p, T),
+                -param.C_p
+                * j_p
+                / param.a_R_p
+                / param.gamma_p
+                / param.D_p(c_s_surf_p, T),
                 "Neumann",
             ),
         }
diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
index 050f472c5c..6fa6c7b510 100644
--- a/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
+++ b/pybamm/models/full_battery_models/lithium_ion/basic_dfn_half_cell.py
@@ -32,11 +32,7 @@ class BasicDFNHalfCell(BaseModel):
     **Extends:** :class:`pybamm.lithium_ion.BaseModel`
     """
 
-    def __init__(
-        self,
-        name="Doyle-Fuller-Newman half cell model",
-        options=None,
-    ):
+    def __init__(self, name="Doyle-Fuller-Newman half cell model", options=None):
         super().__init__({}, name)
         pybamm.citations.register("marquis2019asymptotic")
         # `param` is a class containing all the relevant parameters and functions for
@@ -237,7 +233,7 @@ def __init__(
             self.boundary_conditions[c_s_n] = {
                 "left": (pybamm.Scalar(0), "Neumann"),
                 "right": (
-                    -param.C_n * j_n / param.a_n / param.D_n(c_s_surf_n, T),
+                    -param.C_n * j_n / param.a_R_n / param.D_n(c_s_surf_n, T),
                     "Neumann",
                 ),
             }
@@ -273,7 +269,7 @@ def __init__(
                 "right": (
                     -param.C_p
                     * j_p
-                    / param.a_p
+                    / param.a_R_p
                     / param.gamma_p
                     / param.D_p(c_s_surf_p, T),
                     "Neumann",
diff --git a/pybamm/models/full_battery_models/lithium_ion/basic_spm.py b/pybamm/models/full_battery_models/lithium_ion/basic_spm.py
index ecaa68837c..18019287d0 100644
--- a/pybamm/models/full_battery_models/lithium_ion/basic_spm.py
+++ b/pybamm/models/full_battery_models/lithium_ion/basic_spm.py
@@ -89,14 +89,18 @@ def __init__(self, name="Single Particle Model"):
         self.boundary_conditions[c_s_n] = {
             "left": (pybamm.Scalar(0), "Neumann"),
             "right": (
-                -param.C_n * j_n / param.a_n / param.D_n(c_s_surf_n, T),
+                -param.C_n * j_n / param.a_R_n / param.D_n(c_s_surf_n, T),
                 "Neumann",
             ),
         }
         self.boundary_conditions[c_s_p] = {
             "left": (pybamm.Scalar(0), "Neumann"),
             "right": (
-                -param.C_p * j_p / param.a_p / param.gamma_p / param.D_p(c_s_surf_p, T),
+                -param.C_p
+                * j_p
+                / param.a_R_p
+                / param.gamma_p
+                / param.D_p(c_s_surf_p, T),
                 "Neumann",
             ),
         }
diff --git a/pybamm/models/submodels/electrode/ohm/full_ohm.py b/pybamm/models/submodels/electrode/ohm/full_ohm.py
index a6476407c4..c649610cd4 100644
--- a/pybamm/models/submodels/electrode/ohm/full_ohm.py
+++ b/pybamm/models/submodels/electrode/ohm/full_ohm.py
@@ -60,9 +60,9 @@ def set_algebraic(self, variables):
         phi_s = variables[self.domain + " electrode potential"]
         i_s = variables[self.domain + " electrode current density"]
 
-        # Get surface area per unit volume distribution in x (to account for
-        # graded electrodes)
-        a = variables[self.domain + " surface area per unit volume distribution in x"]
+        # Get surface area per unit volume (could be a distribution in x to
+        # account for graded electrodes)
+        a = variables[self.domain + " electrode surface area per unit volume"]
 
         # Variable summing all of the interfacial current densities
         sum_j = variables[
diff --git a/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py
index 35a5c27c07..affd5dcf8d 100644
--- a/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py
+++ b/pybamm/models/submodels/electrolyte_conductivity/full_conductivity.py
@@ -52,10 +52,10 @@ def set_algebraic(self, variables):
         phi_e = variables["Electrolyte potential"]
         i_e = variables["Electrolyte current density"]
 
-        # Get surface area per unit volume distribution in x (to account for
-        # graded electrodes)
-        a_n = variables["Negative surface area per unit volume distribution in x"]
-        a_p = variables["Positive surface area per unit volume distribution in x"]
+        # Get surface area per unit volume (could be a distribution in x to
+        # account for graded electrodes)
+        a_n = variables["Negative electrode surface area per unit volume"]
+        a_p = variables["Positive electrode surface area per unit volume"]
         a = pybamm.Concatenation(
             a_n, pybamm.FullBroadcast(0, "separator", "current collector"), a_p
         )
diff --git a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py
index 4afce31f33..67231020ec 100644
--- a/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py
+++ b/pybamm/models/submodels/electrolyte_conductivity/surface_potential_form/full_surface_form_conductivity.py
@@ -217,9 +217,9 @@ def set_algebraic(self, variables):
         delta_phi = variables[self.domain + " electrode surface potential difference"]
         i_e = variables[self.domain + " electrolyte current density"]
 
-        # Get surface area per unit volume distribution in x (to account for
-        # graded electrodes)
-        a = variables[self.domain + " surface area per unit volume distribution in x"]
+        # Get surface area per unit volume (could be a distribution in x to
+        # account for graded electrodes)
+        a = variables[self.domain + " electrode surface area per unit volume"]
 
         # Variable summing all of the interfacial current densities
         sum_j = variables[
@@ -259,9 +259,9 @@ def set_rhs(self, variables):
         delta_phi = variables[self.domain + " electrode surface potential difference"]
         i_e = variables[self.domain + " electrolyte current density"]
 
-        # Get surface area per unit volume distribution in x (to account for
-        # graded electrodes)
-        a = variables[self.domain + " surface area per unit volume distribution in x"]
+        # Get surface area per unit volume (could be a distribution in x to
+        # account for graded electrodes)
+        a = variables[self.domain + " electrode surface area per unit volume"]
 
         # Variable summing all of the interfacial current densities
         sum_j = variables[
diff --git a/pybamm/models/submodels/interface/base_interface.py b/pybamm/models/submodels/interface/base_interface.py
index 4e5e5184ef..4d891e8cdd 100644
--- a/pybamm/models/submodels/interface/base_interface.py
+++ b/pybamm/models/submodels/interface/base_interface.py
@@ -173,12 +173,12 @@ def _get_number_of_electrons_in_reaction(self):
         else:
             return pybamm.Scalar(0)
 
-    def _get_surface_area_per_unit_volume_distribution(self):
-        "Returns the distribution of surface area per unit volume in x"
+    def _get_surface_area_per_unit_volume(self):
+        "Returns the surface area per unit volume (which may depend on position)"
         x_n = pybamm.standard_spatial_vars.x_n
         x_p = pybamm.standard_spatial_vars.x_p
-        a_n = self.param.a_n_of_x(x_n)
-        a_p = self.param.a_p_of_x(x_p)
+        a_n = self.param.a_n(x_n)
+        a_p = self.param.a_p(x_p)
         return a_n, a_p
 
     def _get_electrolyte_reaction_signed_stoichiometry(self):
@@ -236,9 +236,9 @@ def _get_standard_interfacial_current_variables(self, j):
         i_typ = self.param.i_typ
         L_x = self.param.L_x
         if self.domain == "Negative":
-            j_scale = i_typ / (self.param.a_n_dim * L_x)
+            j_scale = i_typ / (self.param.a_n_typ * L_x)
         elif self.domain == "Positive":
-            j_scale = i_typ / (self.param.a_p_dim * L_x)
+            j_scale = i_typ / (self.param.a_p_typ * L_x)
 
         # Average, and broadcast if necessary
         if j.domain == []:
@@ -287,9 +287,9 @@ def _get_standard_total_interfacial_current_variables(self, j_tot_av):
         i_typ = self.param.i_typ
         L_x = self.param.L_x
         if self.domain == "Negative":
-            j_scale = i_typ / (self.param.a_n_dim * L_x)
+            j_scale = i_typ / (self.param.a_n_typ * L_x)
         elif self.domain == "Positive":
-            j_scale = i_typ / (self.param.a_p_dim * L_x)
+            j_scale = i_typ / (self.param.a_p_typ * L_x)
 
         variables = {
             "X-averaged "
@@ -315,8 +315,8 @@ def _get_standard_whole_cell_interfacial_current_variables(self, variables):
         """
         i_typ = self.param.i_typ
         L_x = self.param.L_x
-        j_n_scale = i_typ / (self.param.a_n_dim * L_x)
-        j_p_scale = i_typ / (self.param.a_p_dim * L_x)
+        j_n_scale = i_typ / (self.param.a_n_typ * L_x)
+        j_p_scale = i_typ / (self.param.a_p_typ * L_x)
 
         j_n_av = variables[
             "X-averaged negative electrode"
@@ -347,14 +347,14 @@ def _get_standard_whole_cell_interfacial_current_variables(self, variables):
             }
         )
 
-        a_n, a_p = self._get_surface_area_per_unit_volume_distribution()
+        a_n, a_p = self._get_surface_area_per_unit_volume()
         a = pybamm.Concatenation(
             a_n, pybamm.FullBroadcast(0, "separator", "current collector"), a_p
         )
         variables.update(
             {
-                "Negative surface area per unit volume distribution in x": a_n,
-                "Positive surface area per unit volume distribution in x": a_p,
+                "Negative electrode surface area per unit volume": a_n,
+                "Positive electrode surface area per unit volume": a_p,
             }
         )
 
@@ -396,9 +396,9 @@ def _get_standard_exchange_current_variables(self, j0):
         i_typ = self.param.i_typ
         L_x = self.param.L_x
         if self.domain == "Negative":
-            j_scale = i_typ / (self.param.a_n_dim * L_x)
+            j_scale = i_typ / (self.param.a_n_typ * L_x)
         elif self.domain == "Positive":
-            j_scale = i_typ / (self.param.a_p_dim * L_x)
+            j_scale = i_typ / (self.param.a_p_typ * L_x)
 
         # Average, and broadcast if necessary
         if j0.domain == []:
@@ -448,8 +448,8 @@ def _get_standard_whole_cell_exchange_current_variables(self, variables):
 
         i_typ = self.param.i_typ
         L_x = self.param.L_x
-        j_n_scale = i_typ / (self.param.a_n_dim * L_x)
-        j_p_scale = i_typ / (self.param.a_p_dim * L_x)
+        j_n_scale = i_typ / (self.param.a_n_typ * L_x)
+        j_p_scale = i_typ / (self.param.a_p_typ * L_x)
 
         j0_n = variables[
             "Negative electrode" + self.reaction_name + " exchange current density"
diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py
index 02556918ae..8c0ee8bc4d 100644
--- a/pybamm/models/submodels/interface/sei/base_sei.py
+++ b/pybamm/models/submodels/interface/sei/base_sei.py
@@ -112,9 +112,9 @@ def _get_standard_concentraion_variables(self, variables):
             n_outer_scale = self.param.c_ec_0_dim
             v_bar = 1
         else:
-            n_scale = param.L_sei_0_dim * param.a_n_dim / param.V_bar_inner_dimensional
+            n_scale = param.L_sei_0_dim * param.a_n_typ / param.V_bar_inner_dimensional
             n_outer_scale = (
-                param.L_sei_0_dim * param.a_n_dim / param.V_bar_outer_dimensional
+                param.L_sei_0_dim * param.a_n_typ / param.V_bar_outer_dimensional
             )
             v_bar = param.v_bar
 
diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
index 8f56b85649..e4336c06fe 100644
--- a/pybamm/models/submodels/particle/base_particle.py
+++ b/pybamm/models/submodels/particle/base_particle.py
@@ -121,6 +121,31 @@ def _get_standard_concentration_variables(
             * c_scale,
         }
 
+        variables.update(self._get_microstrcuture_variables())
+
+        return variables
+
+    def _get_microstrcuture_variables(self):
+        if self.domain == "Negative":
+            x = pybamm.standard_spatial_vars.x_n
+            R = self.param.R_n(x)
+            R_scale = self.param.R_n_typ
+            a = self.param.a_n(x)
+            a_scale = self.param.a_n_typ
+        elif self.domain == "Positive":
+            x = pybamm.standard_spatial_vars.x_p
+            R = self.param.R_p(x)
+            R_scale = self.param.R_p_typ
+            a = self.param.a_p(x)
+            a_scale = self.param.a_p_typ
+
+        variables = {
+            self.domain + " particle radius": R,
+            self.domain + " particle radius [m]": R * R_scale,
+            self.domain + " electrode surface area per unit volume": a,
+            self.domain + " electrode surface area per unit volume [m-1]": a * a_scale,
+        }
+
         return variables
 
     def _get_standard_flux_variables(self, N_s, N_s_xav):
diff --git a/pybamm/models/submodels/particle/fickian_many_particles.py b/pybamm/models/submodels/particle/fickian_many_particles.py
index 5559f17c52..d4b6d5f4cf 100644
--- a/pybamm/models/submodels/particle/fickian_many_particles.py
+++ b/pybamm/models/submodels/particle/fickian_many_particles.py
@@ -42,17 +42,10 @@ def get_coupled_variables(self, variables):
 
         if self.domain == "Negative":
             N_s = -self.param.D_n(c_s, T) * pybamm.grad(c_s)
-            x = pybamm.standard_spatial_vars.x_n
-            R = self.param.R_n_of_x(x)
-            variables.update({"Negative particle distribution in x": R})
 
         elif self.domain == "Positive":
             N_s = -self.param.D_p(c_s, T) * pybamm.grad(c_s)
 
-            x = pybamm.standard_spatial_vars.x_p
-            R = self.param.R_p_of_x(x)
-            variables.update({"Positive particle distribution in x": R})
-
         variables.update(self._get_standard_flux_variables(N_s, N_s))
 
         return variables
@@ -60,7 +53,7 @@ def get_coupled_variables(self, variables):
     def set_rhs(self, variables):
         c_s = variables[self.domain + " particle concentration"]
         N_s = variables[self.domain + " particle flux"]
-        R = variables[self.domain + " particle distribution in x"]
+        R = variables[self.domain + " particle radius"]
 
         if self.domain == "Negative":
             self.rhs = {c_s: -(1 / (R ** 2 * self.param.C_n)) * pybamm.div(N_s)}
@@ -74,17 +67,19 @@ def set_boundary_conditions(self, variables):
         c_s_surf = variables[self.domain + " particle surface concentration"]
         T = variables[self.domain + " electrode temperature"]
         j = variables[self.domain + " electrode interfacial current density"]
-        R = variables[self.domain + " particle distribution in x"]
+        R = variables[self.domain + " particle radius"]
 
         if self.domain == "Negative":
-            rbc = -self.param.C_n * j * R / self.param.a_n / self.param.D_n(c_s_surf, T)
+            rbc = (
+                -self.param.C_n * j * R / self.param.a_R_n / self.param.D_n(c_s_surf, T)
+            )
 
         elif self.domain == "Positive":
             rbc = (
                 -self.param.C_p
                 * j
                 * R
-                / self.param.a_p
+                / self.param.a_R_p
                 / self.param.gamma_p
                 / self.param.D_p(c_s_surf, T)
             )
diff --git a/pybamm/models/submodels/particle/fickian_single_particle.py b/pybamm/models/submodels/particle/fickian_single_particle.py
index 450c57062f..a05c7efca3 100644
--- a/pybamm/models/submodels/particle/fickian_single_particle.py
+++ b/pybamm/models/submodels/particle/fickian_single_particle.py
@@ -90,7 +90,7 @@ def set_boundary_conditions(self, variables):
             rbc = (
                 -self.param.C_n
                 * j_xav
-                / self.param.a_n
+                / self.param.a_R_n
                 / self.param.D_n(c_s_surf_xav, T_xav)
             )
 
@@ -98,7 +98,7 @@ def set_boundary_conditions(self, variables):
             rbc = (
                 -self.param.C_p
                 * j_xav
-                / self.param.a_p
+                / self.param.a_R_p
                 / self.param.gamma_p
                 / self.param.D_p(c_s_surf_xav, T_xav)
             )
diff --git a/pybamm/models/submodels/particle/polynomial_many_particles.py b/pybamm/models/submodels/particle/polynomial_many_particles.py
index 318955b317..a669204946 100644
--- a/pybamm/models/submodels/particle/polynomial_many_particles.py
+++ b/pybamm/models/submodels/particle/polynomial_many_particles.py
@@ -37,12 +37,10 @@ def __init__(self, param, domain, name):
         pybamm.citations.register("subramanian2005")
 
     def get_fundamental_variables(self):
+        variables = {}
         if self.domain == "Negative":
             # For all orders we solve an equation for the average concentration
             c_s_rav = pybamm.standard_variables.c_s_n_rav
-            x = pybamm.standard_spatial_vars.x_n
-            R = self.param.R_n_of_x(x)
-            variables = {"Negative particle distribution in x": R}
             if self.name == "uniform profile":
                 # The concentration is uniform so the surface value is equal to
                 # the average
@@ -66,9 +64,6 @@ def get_fundamental_variables(self):
         elif self.domain == "Positive":
             # For all orders we solve an equation for the average concentration
             c_s_rav = pybamm.standard_variables.c_s_p_rav
-            x = pybamm.standard_spatial_vars.x_p
-            R = self.param.R_p_of_x(x)
-            variables = {"Positive particle distribution in x": R}
             if self.name == "uniform profile":
                 # The concentration is uniform so the surface value is equal to
                 # the average
@@ -189,13 +184,13 @@ def set_rhs(self, variables):
             "R-averaged " + self.domain.lower() + " particle concentration"
         ]
         j = variables[self.domain + " electrode interfacial current density"]
-        R = variables[self.domain + " particle distribution in x"]
+        R = variables[self.domain + " particle radius"]
 
         if self.domain == "Negative":
-            self.rhs = {c_s_rav: -3 * j / self.param.a_n / R}
+            self.rhs = {c_s_rav: -3 * j / self.param.a_R_n / R}
 
         elif self.domain == "Positive":
-            self.rhs = {c_s_rav: -3 * j / self.param.a_p / self.param.gamma_p / R}
+            self.rhs = {c_s_rav: -3 * j / self.param.a_R_p / self.param.gamma_p / R}
 
         if self.name == "quartic profile":
             # We solve an extra ODE for the average particle flux
@@ -213,7 +208,7 @@ def set_rhs(self, variables):
                         * self.param.D_n(c_s_rav, T)
                         * q_s_rav
                         / self.param.C_n
-                        - 45 * j / self.param.a_n / 2
+                        - 45 * j / self.param.a_R_n / 2
                     }
                 )
             elif self.domain == "Positive":
@@ -223,7 +218,7 @@ def set_rhs(self, variables):
                         * self.param.D_p(c_s_rav, T)
                         * q_s_rav
                         / self.param.C_p
-                        - 45 * j / self.param.a_p / self.param.gamma_p / 2
+                        - 45 * j / self.param.a_R_p / self.param.gamma_p / 2
                     }
                 )
 
@@ -234,7 +229,7 @@ def set_algebraic(self, variables):
         ]
         j = variables[self.domain + " electrode interfacial current density"]
         T = variables[self.domain + " electrode temperature"]
-        R = variables[self.domain + " particle distribution in x"]
+        R = variables[self.domain + " particle radius"]
         if self.name == "uniform profile":
             # No algebraic equations since we only solve for the average concentration
             pass
@@ -243,13 +238,14 @@ 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 - c_s_rav)
-                    + self.param.C_n * (j * R / self.param.a_n / 5)
+                    + 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)
-                    + self.param.C_p * (j * R / self.param.a_p / self.param.gamma_p / 5)
+                    + self.param.C_p
+                    * (j * R / self.param.a_R_p / self.param.gamma_p / 5)
                 }
         elif self.name == "quartic profile":
             # We solve a different algebraic equation for the surface concentration
@@ -261,14 +257,14 @@ def set_algebraic(self, variables):
                 self.algebraic = {
                     c_s_surf: self.param.D_n(c_s_surf, T)
                     * (35 * (c_s_surf - c_s_rav) - 8 * q_s_rav)
-                    + self.param.C_n * (j * R / self.param.a_n)
+                    + 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)
                     * (35 * (c_s_surf - c_s_rav) - 8 * q_s_rav)
-                    + self.param.C_p * (j * R / self.param.a_p / self.param.gamma_p)
+                    + self.param.C_p * (j * R / self.param.a_R_p / self.param.gamma_p)
                 }
 
     def set_initial_conditions(self, variables):
diff --git a/pybamm/models/submodels/particle/polynomial_single_particle.py b/pybamm/models/submodels/particle/polynomial_single_particle.py
index 93b3ca8dee..22a7d1b916 100644
--- a/pybamm/models/submodels/particle/polynomial_single_particle.py
+++ b/pybamm/models/submodels/particle/polynomial_single_particle.py
@@ -104,7 +104,7 @@ def get_coupled_variables(self, variables):
                 c_s_surf_xav = c_s_rxav - self.param.C_n * (
                     j_xav
                     / 5
-                    / self.param.a_n
+                    / self.param.a_R_n
                     / self.param.D_n(c_s_rxav, pybamm.surf(T_xav))
                 )
 
@@ -113,7 +113,7 @@ def get_coupled_variables(self, variables):
                 c_s_surf_xav = c_s_rxav - self.param.C_p * (
                     j_xav
                     / 5
-                    / self.param.a_p
+                    / self.param.a_R_p
                     / self.param.gamma_p
                     / self.param.D_p(c_s_rxav, pybamm.surf(T_xav))
                 )
@@ -133,7 +133,7 @@ def get_coupled_variables(self, variables):
                     * (
                         j_xav
                         / 35
-                        / self.param.a_n
+                        / self.param.a_R_n
                         / self.param.D_n(c_s_rxav, pybamm.surf(T_xav))
                     )
                 )
@@ -147,7 +147,7 @@ def get_coupled_variables(self, variables):
                     * (
                         j_xav
                         / 35
-                        / self.param.a_p
+                        / self.param.a_R_p
                         / self.param.gamma_p
                         / self.param.D_p(c_s_rxav, pybamm.surf(T_xav))
                     )
@@ -286,10 +286,10 @@ def set_rhs(self, variables):
         ]
 
         if self.domain == "Negative":
-            self.rhs = {c_s_rxav: -3 * j_xav / self.param.a_n}
+            self.rhs = {c_s_rxav: -3 * j_xav / self.param.a_R_n}
 
         elif self.domain == "Positive":
-            self.rhs = {c_s_rxav: -3 * j_xav / self.param.a_p / self.param.gamma_p}
+            self.rhs = {c_s_rxav: -3 * j_xav / self.param.a_R_p / self.param.gamma_p}
 
         if self.name == "quartic profile":
             # We solve an extra ODE for the average particle concentration gradient
@@ -309,7 +309,7 @@ def set_rhs(self, variables):
                         * self.param.D_n(c_s_surf_xav, T_xav)
                         * q_s_rxav
                         / self.param.C_n
-                        - 45 * j_xav / self.param.a_n / 2
+                        - 45 * j_xav / self.param.a_R_n / 2
                     }
                 )
             elif self.domain == "Positive":
@@ -319,7 +319,7 @@ def set_rhs(self, variables):
                         * self.param.D_p(c_s_surf_xav, T_xav)
                         * q_s_rxav
                         / self.param.C_p
-                        - 45 * j_xav / self.param.a_p / self.param.gamma_p / 2
+                        - 45 * j_xav / self.param.a_R_p / self.param.gamma_p / 2
                     }
                 )
 
diff --git a/pybamm/models/submodels/thermal/base_thermal.py b/pybamm/models/submodels/thermal/base_thermal.py
index 4a3c5db8f2..832e9d5634 100644
--- a/pybamm/models/submodels/thermal/base_thermal.py
+++ b/pybamm/models/submodels/thermal/base_thermal.py
@@ -91,6 +91,9 @@ def _get_standard_coupled_variables(self, variables):
         T = variables["Cell temperature"]
         T_n, _, T_p = T.orphans
 
+        a_n = variables["Negative electrode surface area per unit volume"]
+        a_p = variables["Positive electrode surface area per unit volume"]
+
         j_n = variables["Negative electrode interfacial current density"]
         j_p = variables["Positive electrode interfacial current density"]
 
@@ -133,8 +136,8 @@ def _get_standard_coupled_variables(self, variables):
         Q_ohm = Q_ohm_s + Q_ohm_e
 
         # Irreversible electrochemical heating
-        Q_rxn_n = j_n * eta_r_n
-        Q_rxn_p = j_p * eta_r_p
+        Q_rxn_n = a_n * j_n * eta_r_n
+        Q_rxn_p = a_p * j_p * eta_r_p
         Q_rxn = pybamm.Concatenation(
             *[
                 Q_rxn_n,
@@ -144,8 +147,8 @@ def _get_standard_coupled_variables(self, variables):
         )
 
         # Reversible electrochemical heating
-        Q_rev_n = j_n * (param.Theta ** (-1) + T_n) * dUdT_n
-        Q_rev_p = j_p * (param.Theta ** (-1) + T_p) * dUdT_p
+        Q_rev_n = a_n * j_n * (param.Theta ** (-1) + T_n) * dUdT_n
+        Q_rev_p = a_p * j_p * (param.Theta ** (-1) + T_p) * dUdT_p
         Q_rev = pybamm.Concatenation(
             *[
                 Q_rev_n,
diff --git a/pybamm/parameters/geometric_parameters.py b/pybamm/parameters/geometric_parameters.py
index 7efae1228c..91b7de39a7 100644
--- a/pybamm/parameters/geometric_parameters.py
+++ b/pybamm/parameters/geometric_parameters.py
@@ -49,8 +49,9 @@ def _set_dimensional_parameters(self):
         self.A_tab_p = self.L_tab_p * self.L_cp  # Area of negative tab
 
         # Microscale geometry
-        # Note: the definition of the surface area to volume ratio is
-        # overwritten in lithium_ion_parameters.py to be computed
+        # Note: parameters related to the particles in li-ion cells are defined
+        # in lithium_ion_parameters.py. The definition of the surface area to
+        # volume ratio is overwritten in lithium_ion_parameters.py to be computed
         # based on the assumed particle shape
         self.a_n_dim = pybamm.Parameter(
             "Negative electrode surface area to volume ratio [m-1]"
@@ -58,8 +59,6 @@ def _set_dimensional_parameters(self):
         self.a_p_dim = pybamm.Parameter(
             "Positive electrode surface area to volume ratio [m-1]"
         )
-        self.R_n = pybamm.Parameter("Negative particle radius [m]")
-        self.R_p = pybamm.Parameter("Positive particle radius [m]")
         self.b_e_n = pybamm.Parameter(
             "Negative electrode Bruggeman coefficient (electrolyte)"
         )
diff --git a/pybamm/parameters/lead_acid_parameters.py b/pybamm/parameters/lead_acid_parameters.py
index 162793aef5..19ef208af8 100644
--- a/pybamm/parameters/lead_acid_parameters.py
+++ b/pybamm/parameters/lead_acid_parameters.py
@@ -86,8 +86,8 @@ def _set_dimensional_parameters(self):
         )  # pybamm.Parameter("Typical oxygen concentration [mol.m-3]")
 
         # Microstructure
-        self.a_n_dim = self.geo.a_n_dim
-        self.a_p_dim = self.geo.a_p_dim
+        # Note: the surface area per unit volume can be set as a function of
+        # through-cell position, so is defined later as a function
         self.b_e_n = self.geo.b_e_n
         self.b_e_s = self.geo.b_e_s
         self.b_e_p = self.geo.b_e_p
@@ -316,17 +316,41 @@ def j0_p_Ox_dimensional(self, c_e, T):
             "Positive electrode oxygen exchange-current density [A.m-2]", inputs
         )
 
+    def a_n_dimensional(self, x):
+        """
+        Negative electrode surface area per unit volume as a function of
+        through-cell distance
+        """
+        inputs = {"Through-cell distance (x_n) [m]": x}
+        return pybamm.FunctionParameter(
+            "Negative electrode surface area to volume ratio [m-1]", inputs
+        )
+
+    def a_p_dimensional(self, x):
+        """
+        Positive electrode surface area per unit volume as a function of
+        through-cell distance
+        """
+        inputs = {"Through-cell distance (x_p) [m]": x}
+        return pybamm.FunctionParameter(
+            "Positive electrode surface area to volume ratio [m-1]", inputs
+        )
+
     def _set_scales(self):
         "Define the scales used in the non-dimensionalisation scheme"
 
+        # Microscale (typical values at electrode/current collector interface)
+        self.a_n_typ = self.a_n_dimensional(0)
+        self.a_p_typ = self.a_p_dimensional(self.L_x)
+
         # Concentrations
         self.electrolyte_concentration_scale = self.c_e_typ
 
         # Electrical
         self.potential_scale = self.R * self.T_ref / self.F
         self.current_scale = self.i_typ
-        self.j_scale_n = self.i_typ / (self.a_n_dim * self.L_x)
-        self.j_scale_p = self.i_typ / (self.a_p_dim * self.L_x)
+        self.j_scale_n = self.i_typ / (self.a_n_typ * self.L_x)
+        self.j_scale_p = self.i_typ / (self.a_p_typ * self.L_x)
 
         # Reaction velocity scale
         self.velocity_scale = self.i_typ / (self.c_e_typ * self.F)
@@ -453,8 +477,8 @@ def _set_dimensionless_parameters(self):
         self.sigma_p_prime = self.sigma_p * self.delta ** 2
         self.sigma_cn_prime = self.sigma_cn * self.delta ** 2
         self.sigma_cp_prime = self.sigma_cp * self.delta ** 2
-        self.delta_pore_n = 1 / (self.a_n_dim * self.L_x)
-        self.delta_pore_p = 1 / (self.a_p_dim * self.L_x)
+        self.delta_pore_n = 1 / (self.a_n_typ * self.L_x)
+        self.delta_pore_p = 1 / (self.a_p_typ * self.L_x)
         self.Q_n_max = self.Q_n_max_dimensional / (self.c_e_typ * self.F)
         self.Q_p_max = self.Q_p_max_dimensional / (self.c_e_typ * self.F)
         self.beta_U_n = 1 / self.Q_n_max
@@ -711,29 +735,21 @@ def c_p_init(self, x):
         """
         return self.c_e_init
 
-    def a_n_of_x(self, x):
+    def a_n(self, x):
         """
-        Dimensionless surface area per unit volume distribution in x (as a function
-        for consistency with lithium-ion). The surface area per unit volume
-        distribution is defined so that the actual surface area per unit volume
-        as a function of x is given by a*a_of_x (so that a_of_x = 1 gives uniform
-        surface area per unit volume in x).
-
-        Returns 1 to give uniform surface area per unit volume in x.
+        Dimensionless negative electrode surface area per unit volume as a
+        function of dimensionless position x
         """
-        return pybamm.FullBroadcast(1, "negative electrode", "current collector")
+        x_dim = x * self.L_x
+        return self.a_n_dimensional(x_dim) / self.a_n_typ
 
-    def a_p_of_x(self, x):
+    def a_p(self, x):
         """
-        Dimensionless surface area per unit volume distribution in x (as a function
-        for consistency with lithium-ion). The surface area per unit volume
-        distribution is defined so that the actual surface area per unit volume
-        as a function of x is given by a*a_of_x (so that a_of_x = 1 gives uniform
-        surface area per unit volume in x).
-
-        Returns 1 to give uniform surface area per unit volume in x.
+        Dimensionless positive electrode surface area per unit volume as a
+        function of dimensionless position x
         """
-        return pybamm.FullBroadcast(1, "positive electrode", "current collector")
+        x_dim = x * self.L_x
+        return self.a_p_dimensional(x_dim) / self.a_p_typ
 
     def _set_input_current(self):
         "Set the input current"
diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py
index 29bc22c841..5be1a8f582 100644
--- a/pybamm/parameters/lithium_ion_parameters.py
+++ b/pybamm/parameters/lithium_ion_parameters.py
@@ -110,6 +110,9 @@ def _set_dimensional_parameters(self):
         )
 
         # Microscale geometry
+        # Note: the particle radius in the electrodes can be set as a function
+        # of through-cell position, so is defined later as a function, along with
+        # the surface area per unit volume
         inputs = {"Through-cell distance (x_n) [m]": pybamm.standard_spatial_vars.x_n}
         self.epsilon_n = pybamm.FunctionParameter("Negative electrode porosity", inputs)
 
@@ -138,26 +141,6 @@ def _set_dimensional_parameters(self):
         self.b_s_s = self.geo.b_s_s
         self.b_s_p = self.geo.b_s_p
 
-        self.R_n = self.geo.R_n
-        self.R_p = self.geo.R_p
-
-        if self.options["particle shape"] == "spherical":
-            self.a_n_dim = 3 * self.epsilon_s_n / self.R_n
-            self.a_p_dim = 3 * self.epsilon_s_p / self.R_p
-        elif self.options["particle shape"] == "user":
-            self.a_n_dim = self.geo.a_n_dim
-            self.a_p_dim = self.geo.a_p_dim
-
-        self.a_k_dim = pybamm.Concatenation(
-            pybamm.FullBroadcast(
-                self.a_n_dim, ["negative electrode"], "current collector"
-            ),
-            pybamm.FullBroadcast(0, ["separator"], "current collector"),
-            pybamm.FullBroadcast(
-                self.a_p_dim, ["positive electrode"], "current collector"
-            ),
-        )
-
         # Electrochemical reactions
         self.ne_n = pybamm.Parameter("Negative electrode electrons in reaction")
         self.ne_p = pybamm.Parameter("Positive electrode electrons in reaction")
@@ -310,6 +293,42 @@ def dUdT_p_dimensional(self, sto):
             "Positive electrode OCP entropic change [V.K-1]", inputs
         )
 
+    def R_n_dimensional(self, x):
+        "Negative particle radius as a function of through-cell distance"
+        inputs = {"Through-cell distance (x_n) [m]": x}
+        return pybamm.FunctionParameter("Negative particle radius [m]", inputs)
+
+    def R_p_dimensional(self, x):
+        "Positive particle radius as a function of through-cell distance"
+        inputs = {"Through-cell distance (x_p) [m]": x}
+        return pybamm.FunctionParameter("Positive particle radius [m]", inputs)
+
+    def a_n_dimensional(self, x):
+        """
+        Negative electrode surface area per unit volume as a function of
+        through-cell distance
+        """
+        inputs = {"Through-cell distance (x_n) [m]": x}
+        if self.options["particle shape"] == "spherical":
+            return 3 * self.epsilon_s_n / self.R_n_dimensional(x)
+        elif self.options["particle shape"] == "user":
+            return pybamm.FunctionParameter(
+                "Negative electrode surface area to volume ratio [m-1]", inputs
+            )
+
+    def a_p_dimensional(self, x):
+        """
+        Positive electrode surface area per unit volume as a function of
+        through-cell distance
+        """
+        inputs = {"Through-cell distance (x_p) [m]": x}
+        if self.options["particle shape"] == "spherical":
+            return 3 * self.epsilon_s_p / self.R_p_dimensional(x)
+        elif self.options["particle shape"] == "user":
+            return pybamm.FunctionParameter(
+                "Positive electrode surface area to volume ratio [m-1]", inputs
+            )
+
     def c_n_init_dimensional(self, x):
         "Initial concentration as a function of dimensionless position x"
         inputs = {"Dimensionless through-cell position (x_n)": x}
@@ -327,6 +346,12 @@ def c_p_init_dimensional(self, x):
     def _set_scales(self):
         "Define the scales used in the non-dimensionalisation scheme"
 
+        # Microscale (typical values at electrode/current collector interface)
+        self.R_n_typ = self.R_n_dimensional(0)
+        self.R_p_typ = self.R_p_dimensional(self.L_x)
+        self.a_n_typ = self.a_n_dimensional(0)
+        self.a_p_typ = self.a_p_dimensional(self.L_x)
+
         # Concentration
         self.electrolyte_concentration_scale = self.c_e_typ
         self.negative_particle_concentration_scale = self.c_n_max
@@ -335,8 +360,8 @@ def _set_scales(self):
         # Electrical
         self.potential_scale = self.R * self.T_ref / self.F
         self.current_scale = self.i_typ
-        self.j_scale_n = self.i_typ / (self.a_n_dim * self.L_x)
-        self.j_scale_p = self.i_typ / (self.a_p_dim * self.L_x)
+        self.j_scale_n = self.i_typ / (self.a_n_typ * self.L_x)
+        self.j_scale_p = self.i_typ / (self.a_p_typ * self.L_x)
 
         # Reference OCP based on initial concentration at
         # current collector/electrode interface
@@ -367,10 +392,10 @@ def _set_scales(self):
 
         # Reaction timescales
         self.tau_r_n = (
-            self.F * self.c_n_max / (self.j0_n_ref_dimensional * self.a_n_dim)
+            self.F * self.c_n_max / (self.j0_n_ref_dimensional * self.a_n_typ)
         )
         self.tau_r_p = (
-            self.F * self.c_p_max / (self.j0_p_ref_dimensional * self.a_p_dim)
+            self.F * self.c_p_max / (self.j0_p_ref_dimensional * self.a_p_typ)
         )
 
         # Electrolyte diffusion timescale
@@ -378,10 +403,10 @@ def _set_scales(self):
         self.tau_diffusion_e = self.L_x ** 2 / self.D_e_typ
 
         # Particle diffusion timescales
-        self.tau_diffusion_n = self.R_n ** 2 / self.D_n_dimensional(
+        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 ** 2 / self.D_p_dimensional(
+        self.tau_diffusion_p = self.R_p_typ ** 2 / self.D_p_dimensional(
             pybamm.Scalar(1), self.T_ref
         )
 
@@ -429,11 +454,9 @@ def _set_dimensionless_parameters(self):
         self.centre_y_tab_p = self.geo.centre_y_tab_p
         self.centre_z_tab_p = self.geo.centre_z_tab_p
 
-        # Microscale geometry, see 'self._set_dimensional_parameters' for the
-        # definition on the dimensional surface area to volume ratio based on
-        # particle shape
-        self.a_n = self.a_n_dim * self.R_n
-        self.a_p = self.a_p_dim * self.R_p
+        # Microscale geometry
+        self.a_R_n = self.a_n_typ * self.R_n_typ
+        self.a_R_p = self.a_p_typ * self.R_p_typ
 
         # Electrode Properties
         self.sigma_cn = (
@@ -616,7 +639,7 @@ def _set_dimensionless_parameters(self):
                 )
             )
         )
-        self.beta_sei_n = self.a_n_dim * self.L_sei_0_dim * self.Gamma_SEI_n
+        self.beta_sei_n = self.a_n_typ * self.L_sei_0_dim * self.Gamma_SEI_n
 
         # Initial conditions
         self.epsilon_n_init = pybamm.Parameter("Negative electrode porosity")
@@ -719,67 +742,45 @@ def dUdT_p(self, c_s_p):
         sto = c_s_p
         return self.dUdT_p_dimensional(sto) * self.Delta_T / self.potential_scale
 
-    def c_n_init(self, x):
-        "Dimensionless initial concentration as a function of dimensionless position x"
-        return self.c_n_init_dimensional(x) / self.c_n_max
-
-    def c_p_init(self, x):
-        "Dimensionless initial concentration as a function of dimensionless position x"
-        return self.c_p_init_dimensional(x) / self.c_p_max
-
-    def R_n_of_x(self, x):
+    def R_n(self, x):
         """
-        Dimensionless negative particle distribution in x. The particle distribution is
-        defined so that the actual particle radius as a function of x is given by
-        R*R_of_x (so that R_of_x = 1 gives particles of uniform size in x).
+        Dimensionless negative particle radius as a function of dimensionless
+        position x
         """
-        inputs = {"Through-cell distance (x_n) [m]": x}
-        return pybamm.FunctionParameter("Negative particle distribution in x", inputs)
+        x_dim = x * self.L_x
+        return self.R_n_dimensional(x_dim) / self.R_n_typ
 
-    def R_p_of_x(self, x):
+    def R_p(self, x):
         """
-        Dimensionless positive particle distribution in x. The particle distribution is
-        defined so that the actual particle radius as a function of x is given by
-        R*R_of_x (so that R_of_x = 1 gives particles of uniform size in x).
+        Dimensionless positive particle radius as a function of dimensionless
+        position x
         """
-        inputs = {"Through-cell distance (x_p) [m]": x}
-        return pybamm.FunctionParameter("Positive particle distribution in x", inputs)
+        x_dim = x * self.L_x
+        return self.R_p_dimensional(x_dim) / self.R_p_typ
 
-    def a_n_of_x(self, x):
+    def a_n(self, x):
         """
-        Dimensionless surface area per unit volume distribution in x. The surface
-        area per unit volume distribution is defined so that the actual surface
-        area per unit volume as a function of x is given by a*a_of_x (so that
-        a_of_x = 1 gives uniform surface area per unit volume in x).
+        Dimensionless negative electrode surface area per unit volume as a
+        function of dimensionless position x.
         """
-        if self.options["particle shape"] == "spherical":
-            # Currently the active material volume fraction is a scalar, so the
-            # distribution of surface are per unit volume is simply the reciprocal
-            # of the particle radius distribution
-            return 1 / self.R_n_of_x(x)
-        elif self.options["particle shape"] == "user":
-            inputs = {"Through-cell distance (x_n) [m]": x}
-            return pybamm.FunctionParameter(
-                "Negative surface area per unit volume distribution in x", inputs
-            )
+        x_dim = x * self.L_x
+        return self.a_n_dimensional(x_dim) / self.a_n_typ
 
-    def a_p_of_x(self, x):
+    def a_p(self, x):
         """
-        Dimensionless surface area per unit volume distribution in x. The surface
-        area per unit volume distribution is defined so that the actual surface
-        area per unit volume as a function of x is given by a*a_of_x (so that
-        a_of_x = 1 gives uniform surface area per unit volume in x).
+        Dimensionless positive electrode surface area per unit volume as a
+        function of dimensionless position x.
         """
-        if self.options["particle shape"] == "spherical":
-            # Currently the active material volume fraction is a scalar, so the
-            # distribution of surface are per unit volume is simply the reciprocal
-            # of the particle radius distribution
-            return 1 / self.R_p_of_x(x)
-        elif self.options["particle shape"] == "user":
-            inputs = {"Through-cell distance (x_p) [m]": x}
-            return pybamm.FunctionParameter(
-                "Positive surface area per unit volume distribution in x", inputs
-            )
+        x_dim = x * self.L_x
+        return self.a_p_dimensional(x_dim) / self.a_p_typ
+
+    def c_n_init(self, x):
+        "Dimensionless initial concentration as a function of dimensionless position x"
+        return self.c_n_init_dimensional(x) / self.c_n_max
+
+    def c_p_init(self, x):
+        "Dimensionless initial concentration as a function of dimensionless position x"
+        return self.c_p_init_dimensional(x) / self.c_p_max
 
     def _set_input_current(self):
         "Set the input current"
diff --git a/pybamm/parameters/parameter_values.py b/pybamm/parameters/parameter_values.py
index c97e499277..b4c8bf1737 100644
--- a/pybamm/parameters/parameter_values.py
+++ b/pybamm/parameters/parameter_values.py
@@ -313,6 +313,21 @@ def check_parameter_values(self, values):
                     "Parameters involving 'reaction rate' have been replaced with "
                     "'exchange-current density' ('{}' found)".format(param)
                 )
+        for param in values:
+            if "particle distribution in x" in param:
+                raise ValueError(
+                    "The parameter '{}' has been deprecated".format(param)
+                    + "The particle radius is now set as a function of x directly "
+                    "instead of providing a reference value and a distribution."
+                )
+        for param in values:
+            if "surface area per unit volume distribution in x" in param:
+                raise ValueError(
+                    "The parameter '{}' has been deprecated".format(param)
+                    + "The surface area per unit volume is now set as a function "
+                    "of x directly instead of providing a reference value and a "
+                    "distribution."
+                )
 
     def process_model(self, unprocessed_model, inplace=True):
         """Assign parameter values to a model.
diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py
index 8530aae667..370c6cf4a0 100644
--- a/tests/integration/test_models/standard_output_tests.py
+++ b/tests/integration/test_models/standard_output_tests.py
@@ -81,8 +81,8 @@ def __init__(self, model, param, disc, solution, operating_condition):
         self.x_edge = disc.mesh.combine_submeshes(*whole_cell).edges * L_x
 
         if isinstance(self.model, pybamm.lithium_ion.BaseModel):
-            R_n = param.evaluate(geo.R_n)
-            R_p = param.evaluate(geo.R_p)
+            R_n = param.evaluate(model.param.R_n_typ)
+            R_p = param.evaluate(model.param.R_p_typ)
             self.r_n = disc.mesh["negative particle"].nodes * R_n
             self.r_p = disc.mesh["positive particle"].nodes * R_p
             self.r_n_edge = disc.mesh["negative particle"].edges * R_n
@@ -595,8 +595,8 @@ def __init__(self, model, param, disc, solution, operating_condition):
         self.i_s = solution["Electrode current density"]
         self.i_e = solution["Electrolyte current density"]
 
-        self.a_n = solution["Negative surface area per unit volume distribution in x"]
-        self.a_p = solution["Positive surface area per unit volume distribution in x"]
+        self.a_n = solution["Negative electrode surface area per unit volume"]
+        self.a_p = solution["Positive electrode surface area per unit volume"]
 
     def test_interfacial_current_average(self):
         """Test that average of the surface area density distribution (in x)
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs.py
index 495d605d50..c188373e57 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs.py
@@ -137,8 +137,6 @@ def test_compare_particle_shape(self):
                         "Positive electrode surface area to volume ratio [m-1]": 3
                         * eps_s_p
                         / R_p,
-                        "Negative surface area per unit volume distribution in x": 1,
-                        "Positive surface area per unit volume distribution in x": 1,
                     },
                     check_already_exists=False,
                 )
diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
index a9b85eb76c..20a0831e7f 100644
--- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
+++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_dfn.py
@@ -122,15 +122,18 @@ def test_surface_form_algebraic(self):
     def test_particle_distribution_in_x(self):
         model = pybamm.lithium_ion.DFN()
         param = model.default_parameter_values
+        L_n = model.param.L_n
+        L_p = model.param.L_p
+        L = model.param.L_x
 
-        def negative_distribution(x):
-            return 1 + x
+        def negative_radius(x):
+            return (1 + x / L_n) * 1e-5
 
-        def positive_distribution(x):
-            return 1 + (x - (1 - model.param.l_p))
+        def positive_radius(x):
+            return (1 + (x - L_p) / (L - L_p)) * 1e-5
 
-        param["Negative particle distribution in x"] = negative_distribution
-        param["Positive particle distribution in x"] = positive_distribution
+        param["Negative particle radius [m]"] = negative_radius
+        param["Positive particle radius [m]"] = positive_radius
         modeltest = tests.StandardModelTest(model, parameter_values=param)
         modeltest.test_all()
 
diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py
index 575898299d..5675654696 100644
--- a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py
+++ b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_full_conductivity.py
@@ -11,7 +11,7 @@ class TestFull(unittest.TestCase):
     def test_public_functions(self):
         param = pybamm.LithiumIonParameters()
         a = pybamm.Scalar(0)
-        surf = "surface area per unit volume distribution in x"
+        surf = "electrode surface area per unit volume"
         variables = {
             "Electrolyte tortuosity": a,
             "Electrolyte concentration": pybamm.FullBroadcast(
diff --git a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py
index 327f8e85b0..3dbe5e2bcf 100644
--- a/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py
+++ b/tests/unit/test_models/test_submodels/test_electrolyte_conductivity/test_surface_form/test_full_surface_form_conductivity.py
@@ -25,7 +25,7 @@ def test_public_functions(self):
             "Negative electrode porosity": a_n,
             "Negative electrolyte tortuosity": a_n,
             "Negative electrode tortuosity": a_n,
-            "Negative surface area per unit volume distribution in x": a_n,
+            "Negative electrode surface area per unit volume": a_n,
             "Negative electrolyte concentration": a_n,
             "Sum of negative electrode interfacial current densities": a_n,
             "Electrolyte potential": pybamm.Concatenation(a_n, a_s, a_p),
@@ -55,7 +55,7 @@ def test_public_functions(self):
             "Positive electrode porosity": a_p,
             "Positive electrolyte tortuosity": a_p,
             "Positive electrode tortuosity": a_p,
-            "Positive surface area per unit volume distribution in x": a_p,
+            "Positive electrode surface area per unit volume": a_p,
             "Positive electrolyte concentration": a_p,
             "Sum of positive electrode interfacial current densities": a_p,
             "Positive electrode temperature": a_p,
diff --git a/tests/unit/test_models/test_submodels/test_thermal/coupled_variables.py b/tests/unit/test_models/test_submodels/test_thermal/coupled_variables.py
index 9fde9fd885..7fdc1fa565 100644
--- a/tests/unit/test_models/test_submodels/test_thermal/coupled_variables.py
+++ b/tests/unit/test_models/test_submodels/test_thermal/coupled_variables.py
@@ -10,6 +10,8 @@
 b = pybamm.PrimaryBroadcast(pybamm.Scalar(1), "current collector")
 
 coupled_variables = {
+    "Negative electrode surface area per unit volume": a_n,
+    "Positive electrode surface area per unit volume": a_p,
     "Negative electrode interfacial current density": a_n,
     "Positive electrode interfacial current density": a_p,
     "Negative electrode reaction overpotential": a_n,
diff --git a/tests/unit/test_parameters/test_lithium_ion_parameters.py b/tests/unit/test_parameters/test_lithium_ion_parameters.py
index 965e604056..e6f75aee49 100644
--- a/tests/unit/test_parameters/test_lithium_ion_parameters.py
+++ b/tests/unit/test_parameters/test_lithium_ion_parameters.py
@@ -23,26 +23,33 @@ def test_lithium_ion(self):
         c_rate = param.i_typ / 24  # roughly for the numbers I used before
 
         "particle geometry"
+        # Note: in general these can be functions, but are constant for this
+        # set, so we just arbitrarily evaluate at 0
+
         # a_n dimensional
         np.testing.assert_almost_equal(
-            values.evaluate(param.a_n_dim), 0.18 * 10 ** (6), 2
+            values.evaluate(param.a_n_dimensional(0)), 0.18 * 10 ** (6), 2
         )
         # R_n dimensional
-        np.testing.assert_almost_equal(values.evaluate(param.R_n), 1 * 10 ** (-5), 2)
+        np.testing.assert_almost_equal(
+            values.evaluate(param.R_n_dimensional(0)), 1 * 10 ** (-5), 2
+        )
 
-        # a_n
-        np.testing.assert_almost_equal(values.evaluate(param.a_n), 1.8, 2)
+        # a_R_n = a_n_typ * R_n_typ
+        np.testing.assert_almost_equal(values.evaluate(param.a_R_n), 1.8, 2)
 
         # a_p dimensional
         np.testing.assert_almost_equal(
-            values.evaluate(param.a_p_dim), 0.15 * 10 ** (6), 2
+            values.evaluate(param.a_p_dimensional(0)), 0.15 * 10 ** (6), 2
         )
 
         # R_p dimensional
-        np.testing.assert_almost_equal(values.evaluate(param.R_n), 1 * 10 ** (-5), 2)
+        np.testing.assert_almost_equal(
+            values.evaluate(param.R_n_dimensional(0)), 1 * 10 ** (-5), 2
+        )
 
-        # a_p
-        np.testing.assert_almost_equal(values.evaluate(param.a_p), 1.5, 2)
+        # a_p = a_p_typ * R_p_typ
+        np.testing.assert_almost_equal(values.evaluate(param.a_R_p), 1.5, 2)
 
         # j0_m
         np.testing.assert_almost_equal(
diff --git a/tests/unit/test_parameters/test_parameter_values.py b/tests/unit/test_parameters/test_parameter_values.py
index ce107d0b6f..f00006473b 100644
--- a/tests/unit/test_parameters/test_parameter_values.py
+++ b/tests/unit/test_parameters/test_parameter_values.py
@@ -103,6 +103,12 @@ def test_check_parameter_values(self):
             pybamm.ParameterValues({"Negative surface area density": 1})
         with self.assertRaisesRegex(ValueError, "reaction rate"):
             pybamm.ParameterValues({"Negative reaction rate": 1})
+        with self.assertRaisesRegex(ValueError, "particle distribution"):
+            pybamm.ParameterValues({"Negative particle distribution in x": 1})
+        with self.assertRaisesRegex(ValueError, "surface area per unit volume"):
+            pybamm.ParameterValues(
+                {"Negative electrode surface area per unit volume distribution in x": 1}
+            )
 
     def test_process_symbol(self):
         parameter_values = pybamm.ParameterValues({"a": 1, "b": 2, "c": 3})