diff --git a/docs/source/figures/illustrations/bulk-water/effect_cutoff-dark.png b/docs/source/figures/illustrations/bulk-water/effect_cutoff-dark.png
new file mode 100644
index 0000000..271ba38
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diff --git a/docs/source/figures/illustrations/bulk-water/effect_cutoff-light.png b/docs/source/figures/illustrations/bulk-water/effect_cutoff-light.png
new file mode 100644
index 0000000..11bb0fa
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diff --git a/examples/illustrations/bulk-water.ipynb b/examples/illustrations/bulk-water.ipynb
index 744108f..46ba3a8 100644
--- a/examples/illustrations/bulk-water.ipynb
+++ b/examples/illustrations/bulk-water.ipynb
@@ -258,13 +258,107 @@
     "    save_figure(plt, fig, mode, git_path, path_figures, filename)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "49d74fda",
+   "metadata": {},
+   "source": [
+    "# Varying cut-off"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "fb847ec6",
+   "execution_count": 44,
+   "id": "68f3e9c4",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "all_co = []\n",
+    "R1_intra_vs_co = []\n",
+    "R2_intra_vs_co = []\n",
+    "gij_intra_vs_co = []\n",
+    "f_vs_co = []\n",
+    "t_vs_co = []\n",
+    "R1_inter_vs_co = []\n",
+    "R2_inter_vs_co = []\n",
+    "gij_inter_vs_co = []\n",
+    "for cutoff in [\"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\"]:\n",
+    "    if np.int32(cutoff) < 14:\n",
+    "        t, f, gij, R1, R2 = read_dic(\"bulk-water-tip4p-vs-cutoff/raw_data/N4000_intra_cutoff\"+cutoff+\".npy\")\n",
+    "        t_vs_T, f_vs_co, gij_intra_vs_co, R1_intra_vs_co, R2_intra_vs_co = append_data(t, f, gij, R1, R2, t_vs_co, f_vs_co, gij_intra_vs_co, R1_intra_vs_co, R2_intra_vs_co)\n",
+    "        t, f, gij, R1, R2 = read_dic(\"bulk-water-tip4p-vs-cutoff/raw_data/N4000_inter_cutoff\"+cutoff+\".npy\")\n",
+    "        t_vs_co, f_vs_co, gij_inter_vs_co, R1_inter_vs_co, R2_inter_vs_co = append_data(t, f, gij, R1, R2, [], [], gij_inter_vs_co, R1_inter_vs_co, R2_inter_vs_co) \n",
+    "    else:\n",
+    "        t, f, gij, R1, R2 = read_dic(\"bulk-water-tip4p-vs-temperature/raw_data/N4000_intra_T300K.npy\")\n",
+    "        t_vs_T, f_vs_co, gij_intra_vs_co, R1_intra_vs_co, R2_intra_vs_co = append_data(t, f, gij, R1, R2, t_vs_co, f_vs_co, gij_intra_vs_co, R1_intra_vs_co, R2_intra_vs_co)\n",
+    "        t, f, gij, R1, R2 = read_dic(\"bulk-water-tip4p-vs-temperature/raw_data/N4000_inter_T300K.npy\")\n",
+    "        t_vs_co, f_vs_co, gij_inter_vs_co, R1_inter_vs_co, R2_inter_vs_co = append_data(t, f, gij, R1, R2, [], [], gij_inter_vs_co, R1_inter_vs_co, R2_inter_vs_co) \n",
+    "    all_co.append(np.int32(cutoff))\n",
+    "all_co = np.array(all_co)\n",
+    "R10_inter_vs_co = extract_R0(R1_inter_vs_co)\n",
+    "R20_inter_vs_co = extract_R0(R2_inter_vs_co)\n",
+    "R10_intra_vs_co = extract_R0(R1_intra_vs_co)\n",
+    "R20_intra_vs_co = extract_R0(R2_intra_vs_co)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "fb847ec6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "filename = \"effect_cutoff\"\n",
+    "for mode, mygray in zip(['light', 'dark'], [colors[\"mylightgray\"], colors[\"mydarkgray\"]]):    \n",
+    "    fig = plt.figure(figsize=(18,12))\n",
+    "    ax, n, l_tot, c_tot = [], 0, 2, 1\n",
+    "    n += 1\n",
+    "    ax.append(plt.subplot(l_tot, c_tot, n))\n",
+    "    ax[-1].plot(all_co, 1/(R10_inter_vs_co+R10_intra_vs_co), \"o\", color=colors[\"mydarkblue\"],\n",
+    "                markersize = 12, linewidth=4) #, label=r'TIP4P$-\\epsilon$')\n",
+    "    complete_panel(ax[-1], r'cut-off LJ ($\\AA$)', r'$T_1^\\mathrm{inter}$ (s)',\n",
+    "                   legend=True, axis_color=mygray, xpad=15)\n",
+    "    set_boundaries(plt, x_boundaries=(6.5, 14.5), y_boundaries=(2.8, 3.8))\n",
+    "\n",
+    "    # x_ticks=np.arange(-1, 0.2, 0.2)\n",
+    "    # add_subplotlabels(fig, ax, [\"a\", \"b\"], color=mygray)\n",
+    "    save_figure(plt, fig, mode, git_path, path_figures, filename)"
+   ]
   },
   {
    "cell_type": "code",