{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Initial enthalpy calculations and enthalpy modelling\n", "\n", "Experimentally, the enthalpy of adsorption can be obtained either indirectly,\n", "through the isosteric enthalpy method, or directly, using adsorption\n", "microcalorimetry. Once an enthalpy curve is calculated, a useful performance\n", "indicator is the enthalpy of adsorption at zero loading, corresponding to the\n", "initial interactions of the probe with the surface. pyGAPS contains two methods\n", "to determine the initial enthalpy of adsorption starting from an enthalpy curve.\n", "\n", "First, make sure the data is imported by running the import notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected 5 isotherms with nitrogen at 77K\n", "Selected 2 room temperature calorimetry isotherms\n", "Selected 2 isotherms for IAST calculation\n", "Selected 3 isotherms for isosteric enthalpy calculation\n" ] } ], "source": [ "# import isotherms\n", "%run import.ipynb\n", "\n", "# import the characterisation module\n", "import pygaps.characterisation as pgc" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Initial point method\n", "\n", "The point method of determining enthalpy of adsorption is the simplest method.\n", "It just returns the first measured point in the enthalpy curve.\n", "\n", "Depending on the data, the first point method may or may not be representative\n", "of the actual value." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The initial enthalpy of adsorption is: \n", "\tE = 28.8\n" ] }, { "data": { "image/png": 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bEbn28zNR468Wp512Gs888wxvvvkmkyZNctI3s4pw4u9D1113XaVDMDPLxsldMzN7mxO/mVnGOPGbmWWME7+ZWcY48ffCf/3Xf7HbbrsxdOhQLrnkEgBuu+02nnnmmQ3LzJo1i8WLF3er3IULFzJmzJiSxmpm1saJvxd+/vOfc9ddd/Haa69x7rnnAqVJ/GZm5eTunD10+umn88ILLzBx4kROOeUUnn/+eT73uc9xxx138OCDD/Ld736X4447jqamJo4//ni22mor5syZwzPPPMOZZ57JqlWrGD58OLNmzWLkyJHMnTuXU045ha233poDDjig0i/PzPqx/pH4v/pVePzx0pY5dixcfnmHT19xxRXMnj2b3/3ud9x5550A7LfffkycOJEjjzyST33qUwDcfffdXHrppeRyOdauXcsZZ5zB7bffTn19PTfccAPnn38+V111FSeffDI/+clPOPjggznrrLNK+1rMzPL0j8RfI+bPn8+8efOYMGECAOvXr2fkyJEsX76c1tZWDj74YABOPPFE7r777kqGamb9WP9I/J3UzKtJRLD77rszZ86cjea3trYiqUJRmVnW+ORuiQ0ZMoSVK1cWnN5ll11oaWnZkPjXrl3L008/TV1dHdtttx0PP/wwANdee23fB25mmVH2xC9pc0mPSboznR4m6V5JC9L/HY9lXIOOPfZYfvSjH7HXXnvx/PPPM3nyZE4//XTGjh3L+vXruemmmzjnnHPYc889GTt2LH/84x8BmDlzJl/60pfYd999N9yly8ysHMo+LLOkM4EcsG1EHCnph8CyiLhE0rnA0Ig4p7My+suwzGZmfakiwzJL2gk4AvgecGY6+2jgw+njq4EHgE4Tv5lZsVpbWzfcdKmhoaHXN13qj8rd1HM5cDbwVt687SNiCUD6v+CNbyWdJqlJUlPbvXLNzDrT2trKjBkzaGpqYs2aNTQ1NTFjxgxaW1srHVpVKVvil3Qk0BwRc3uyfkRMj4hcROTq6+tLHJ2Z9UeNjY2sXr2aqVOnMmXKFKZOncrq1atpbGysdGhVpZxNPfsDEyUdDmwJbCvpl8DLkkZGxBJJI4HmMsZgZhnS3NzMiBEjaKss1tfXM2LECJqbnWbyla3GHxFfj4idImI0cCzw24g4AbgDmJQuNgm4vVwxmFm2NDQ0sHTpUtqah1taWli6dCkNDQVblDOrEhdwXQLcKOnzwIvApysQg5n1Q+PHj+epp55i2rRpjBgxgqVLlzJo0CDGjx9f6dCqStm7c5aCu3OaWbHcq+dtFenOaWbW1+rq6jjssMMqHUZV85ANZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGePEb2aWMU78ZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGePEb2aWMR6kzcz6PY/YuTHX+M2sX/N9eDflxG9m/Zrvw7spJ34z69d8H95NOfGbWb/m+/Buyid3zaxf8314N+XEb2b9Wl1dHaeeeuqGXj25XC7zvXqc+M2s3/N9eDfmNn4zs4xx4jczyxg39ZhZzfGVuL3jGr+Z1RRfidt7TvxmVlN8JW7vOfGbWU3xlbi958RvZjXFV+L2nk/umllN8ZW4vefEb2Y1xVfi9p4Tv5n1qVJ0xfSVuL3jNn4z6zPuilkdnPjNrM+4K2Z1cOI3sz7jrpjVwW38ZtZnGhoaaGpqoqWlhfr6+g1dMXO5XKVD20h/HxLCid/M+kwtdMVsOw+xevVqRowYQVNTE0899RSnnnpqv0n+Tvxm1mdqoStm/nmItqOSadOm0djY2G96EpUt8UvaEngIGJRu56aI+JakYcANwGhgIfCZiHitXHGYWXWp9q6YWTgPUc6Tu6uBQyJiT2AscJik8cC5wP0R8T7g/nTazGpEa2srs2fP5pprrmH27Nn9ritmFoaEKFuNPyICWJVODkj/Ajga+HA6/2rgAeCccsVhZqWThfbvWjgP0Vtl7c4paXNJjwPNwL0R8QiwfUQsAUj/F9yNSjpNUpOkprY9r5lVVhb64bedh8jlcgwcOJBcLtevdmxQ5pO7EbEeGCupDrhV0phurDsdmA6Qy+WiPBFam/7efa2WVdNnk4X2b6j+8xC91Se9eiKiVdIDwGHAy5JGRsQSSSNJjgbKohQ/mFL/6MrxI+5tmb05fK/G97g/qbamlVrph2+dU9IUX4aCpXpgbZr0twLuAX4AHAy8GhGXSDoXGBYRZ3dWVi6Xi6ampm5tv/0Ppq2drjs/mFKUUc7ySlXm7NmzaWpq2qT7Wi6X67TWU+n3uCc7jFrbyfT0symXcnyHrXwkzY2ITfbK5Uz8/0Ry8nZzknMJN0bEhZLeAdwIvBN4Efh0RCzrrKyeJP62H8yZixax9fz5rFm7liWLFzNkyBCGDRtWVBnLli1j5cqVjNxhBwYOGNCjMspZXqnKXPryy8RbbzFy5MgN85YsWYI224wR229f1m33tIx169axeMkS4q23GDhwIGvWrEGbbcYOI0eyxRaFD2R7sk6l9fSzKad169axYsUK1qxdy8ABA9h2222r9v3rF8aOhcsv79GqHSX+cvbqeRLYq8D8V4FDy7XdNm1tkVu/8goAAwcMSH7sa9cWXcaatWsZOHAgAwcM6HEZ5SyvVGUOHDCAlStXbvghr1m7ljVr1jBkyJCyb7unZaxYsSJJiO12GCtWrOhwh9GTdSqtp59NOW2xxRZV+35ZcfrtbnpDW+T55284RP5FNw+RHy9wmN3dMspZXqnK3Ky1lRs7OHynk8P3Umy7p2Xcec01rFmzhilTpgAwELj3yisZOHAgJ510UsnWqbSefjZmnem3ib8UfXFL3Z+3HP2DS1FmTy+jr+R73JOTjLV4YrIWhjiw2lO2Nv5S6kkbP1Rnj5Nq7NVT6W339CRtd08y+sSkZU2vTu5KuhSYGRFPlyO4rvQ08Vv/loVePWa90dvEPwU4maRpaCZwfUQsL3mUHXDiNzPrvo4Sf1FDNkTElRGxP3ASyaiaT0q6TtJHShummZmVW9Fj9UjaHNg1/XsFeAI4U9L/lik2MzMrg6J69Ui6DJhIMozyxRHxp/SpH0iaX67gzMys9IrtzjkP+EZEvFHguX1KGI+ZmZVZsYl/JvBJSQeQjKn/cETcCtCXJ3nNzKz3im3j/xlwOvAUSe1/qqSflS0qMzMrm2Jr/AcDY9K7aiHpapKdgJmZ1Zhia/zzSUbTbLMz8GTpwzEzs3Irtsb/DuBZSW29efYG5ki6AyAiJpYjODMzK71iE/8FZY3CzKwG9JchP4pK/BHxYLkDMTOrZtV2G8ze6DTxS1pJ0n1zk6eAiIhtyxKVmVmVaWxsZPXq1ZvcBrOxsbHmbszeaeKPiMrd5sfMrIq03dWvvr4egPr6ekaMGEFzc3OFI+u+osfqAZDUIOmdbX/lCsrMrNo0NDSwdOlSWlpaADbcyKehoaHCkXVfsWP1TAT+A9gBaAZGAc8Cu5cvNDOz6lGOO+hVSrE1/ouA8cBzEfEukpul/6FsUZmZVZm222DmcjkGDhxILperyRO7UHx3zrUR8aqkzSRtFhG/k/SDskZmZlZl6urqau5EbiHFJv5WSYOBh4BrJTUD68oXlpmZlUuxTT1HA/8AvgbMBp4HjipXUGZmVj7FXsD1et7k1WWKxczM+kBRNX5Jx0haIGm5pBWSVkpaUe7gzMys9Ipt4/8hcFREPFvOYMzMrPyKTfwvO+mbmRVWa4O3dTVWzzHpwyZJNwC3Aavbno+IW8oXmplZ9avFwdu6qvHn99x5A/hY3nQATvxmlmm1OHhbV4O0nQwgaf+I2OhKXUn7lzMwM7NaUIuDtxXbj/8nRc4zM8uUWhy8ras2/n2B/YB6SWfmPbUtsHk5AzMzqwW1OHhbV238A4HB6XL5Y/OvAD5VrqDMzGpF2+Btbb16crlc1ffqUUShG2y1W0gaFRF/64N4CsrlctHU1FSpzZuZ1SRJcyMi135+sf34B0maDozOXyciDilNeGZm1leKTfy/Aq4ArgTWF7OCpJ2Ba4ARwFvA9Ij4T0nDgBtIdiILgc9ExGvdC9vMrDb09OKucl4UVmxTz9yI+GC3CpZGAiMj4lFJQ4C5wCeAycCyiLhE0rnA0Ig4p7Oy3NRjZrWo/cVdbSd+u7q4q6frtddRU0+x3Tl/LemLkkZKGtb219kKEbEkIh5NH68kuVXjjiRDPLeN8Hk1yc7AzKzfyb+4a8qUKUydOpXVq1fT2NhYlvWKVWxTz6T0/1l58wJ4dzErSxoN7AU8AmwfEUsg2TlIKtjZVdJpwGkA73yn7+tuZrWnpxd3lfuisKJq/BHxrgJ/xSb9wcDNwFcjouihnCNiekTkIiLX9uLNzGpJTy/uKvdFYV1dwHV2RPwwffzpiPhV3nMXR8R5Xaw/gCTpX5s3oNvLkkamtf2RQPVe12xm1gs9vbir3BeFdXpyV9KjETGu/eNC0wXWFUkb/rKI+Gre/B8Br+ad3B0WEWd3FqRP7ppZrapkr56e9uNXB48LTbe3P3Ai8JSkx9N55wGXADdK+jzwIvDpLsoxM6tZdXV1PRqls6frFaOrxB8dPC40vfGTEQ/T8c7h0C62a2ZmZdJV4t8zvbeugK3y7rMrYMuyRmZmZmXR1Xj8HoHTzKyfKfYCLjMz6yec+M3MMsaJ38wsY5z4zcwyxonfzCxjnPjNzDLGid/MLGOc+M3MMsaJ38wsY5z4zcwyxonfzCxjnPjNzDLGid/MLGOc+M3MMsaJ38wsY5z4zcwyxonfzCxjnPjNzDLGid/MLGOc+M3MMsaJ38wsY5z4zcwyxonfzCxjnPjNzDLGid/MLGOc+M3MMsaJ38wsY5z4zcwyxonfzCxjnPjNzDLGid/MLGOc+M3MMsaJ38wsY5z4zcwyxonfzCxjypb4JV0lqVnSvLx5wyTdK2lB+n9oubZvZmaFlbPGPws4rN28c4H7I+J9wP3ptJmZ9aGyJf6IeAhY1m720cDV6eOrgU+Ua/tmZlZYX7fxbx8RSwDS/w0dLSjpNElNkppaWlr6LEAzs/6uak/uRsT0iMhFRK6+vr7S4ZiZ9Rt9nfhfljQSIP3f3MfbNzPLvL5O/HcAk9LHk4Db+3j7ZmaZV87unNcDc4BdJL0k6fPAJcAESQuACem0mZn1oS3KVXBEHNfBU4eWa5tmZta1qj25a2Zm5eHEb2aWMU78ZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGePEb2aWMU78ZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGePEb2aWMU78ZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGePEb2aWMU78ZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGePEb2aWMU78ZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGePEb2aWMU78ZmYZ48RvZpYxTvxmZhnjxG9mljFO/GZmGVORxC/pMEnzJf1F0rmViMHMLKv6PPFL2hz4GfBx4APAcZI+0NdxmJllVSVq/PsAf4mIFyJiDfC/wNEViMPMLJO2qMA2dwQW5U2/BHyo/UKSTgNOSydXS5rXB7GVwnDglUoHUYRaiRNqJ9ZaiRMcazlUY5yjCs2sROJXgXmxyYyI6cB0AElNEZErd2ClUCux1kqcUDux1kqc4FjLoVbihMo09bwE7Jw3vROwuAJxmJllUiUS//8B75P0LkkDgWOBOyoQh5lZJvV5U09ErJP0ZeD/AZsDV0XE012sNr38kZVMrcRaK3FC7cRaK3GCYy2HWokTRWzSvG5mZv2Yr9w1M8sYJ34zs4yp6sRfK0M7SNpZ0u8kPSvpaUlfqXRMnZG0uaTHJN1Z6Vg6I6lO0k2S/py+t/tWOqaOSPpa+tnPk3S9pC0rHVMbSVdJas6/FkbSMEn3SlqQ/h9ayRjTmArF+aP0839S0q2S6ioY4gaFYs177t8lhaThlYitGFWb+GtsaId1wL9FxG7AeOBLVRwrwFeAZysdRBH+E5gdEbsCe1KlMUvaEfhXIBcRY0g6LRxb2ag2Mgs4rN28c4H7I+J9wP3pdKXNYtM47wXGRMQ/Ac8BX+/roDowi01jRdLOwATgxb4OqDuqNvFTQ0M7RMSSiHg0fbySJEHtWNmoCpO0E3AEcGWlY+mMpG2Bg4BfAETEmohorWhQndsC2ErSFsDWVNG1KRHxELCs3eyjgavTx1cDn+jLmAopFGdE3BMR69LJRpLrfiqug/cU4MfA2RS4KLWaVHPiLzS0Q1Um03ySRgN7AY9UOJSOXE7yxXyrwnF05d1ACzAzbZa6UtI2lQ6qkIj4O3ApSS1vCbA8Iu6pbFRd2j4ilkBScQEaKhxPMU4B7q50EB2RNBH4e0Q8UelYulLNib+ooR2qiaTBwM3AVyNiRaXjaU/SkUBzRMytdCxF2AIYB/x3ROwFvE51NEdsIm0fPxp4F7ADsI2kEyobVf8i6XySJtVrKx1LIZK2Bs4HLqh0LMWo5sRfU0M7SBpAkvSvjYhbKh1PB/YHJkpaSNJ0doikX1Y2pA69BLwUEW1HTjeR7Aiq0UeBv0ZES0SsBW4B9qtwTF15WdJIgPR/c4Xj6ZCkScCRwPFRvRcevYdkx/9E+vvaCXhU0oiKRtWBak78NTO0gySRtEU/GxGXVTqejkTE1yNip4gYTfJ+/jYiqrJmGhFLgUWSdklnHQo8U8GQOvMiMF7S1ul34VCq9ER0njuASenjScDtFYylQ5IOA84BJkbEG5WOpyMR8VRENETE6PT39RIwLv0eV52qTfzpCZ22oR2eBW4sYmiHStkfOJGkBv14+nd4pYPqB84ArpX0JDAWuLiy4RSWHpXcBDwKPEXyu6qay/clXQ/MAXaR9JKkzwOXABMkLSDphXJJJWOEDuP8KTAEuDf9XV1R0SBTHcRaMzxkg5lZxlRtjd/MzMrDid/MLGOc+M3MMsaJ38wsY5z4zcwyxonfMknSqnKVKWkHSTeVunyzUnF3TsskSasiYnC1l2lWDq7xm6UkjZXUmDf2+9B0/qmS/k/SE5JuTsdlIb2qfE763EV55YxuG6dd0mRJt0ianY59/8O85T4v6TlJD0iaIemnff2aLZuc+M3edg1wTjr2+1PAt9L5t0TE3hHRdk+Atqs0/5NkELm9gc4uzR8LfBbYA/iskhv37AB8k+T+DROAXUv9Ysw64sRvBkjaDqiLiAfTWVeT3A8AYIyk30t6Cjge2D2dvz9wffr4fzop/v6IWB4Rb5KMNzSK5H4TD0bEsnRgt1+V8OWYdcqJ36xrs4AvR8QewHeA/NsqFnOSbHXe4/UkQ04XGnbcrE848ZsBEbEceE3SgemsE4G22v8QYEk69Pbxeav9gbdvsZg/vxh/Ag6WNDS9a9e/9Cxys+7botIBmFXI1pJeypu+jGR44ivSk7cvACenz32T5I5qfyNp+x+Szv8KcJ2kr5Dci6FoEfF3SRen5S4maQJa3sPXYtYt7s5pViGSBkfEqrTGfytwVUTcWum4rP9zU49Z5Xxb0uPAPOCvwG0VjcYywzV+M7OMcY3fzCxjnPjNzDLGid/MLGOc+M3MMsaJ38wsY/4/Vjoh1zFV6LgAAAAASUVORK5CYII=" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "The initial enthalpy of adsorption is: \n", "\tE = 34.7\n" ] }, { "data": { "image/png": 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Initial point method\n", "isotherm = next(i for i in isotherms_calorimetry if i.material == 'HKUST-1(Cu)')\n", "res = pgc.initial_enthalpy_point(isotherm, 'enthalpy', verbose=True)\n", "plt.show()\n", "\n", "isotherm = next(i for i in isotherms_calorimetry if i.material == 'Takeda 5A')\n", "res = pgc.initial_enthalpy_point(isotherm, 'enthalpy', verbose=True)\n", "plt.show()\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Compound model method" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "This method attempts to model the enthalpy curve by the superposition of several\n", "contributions. It is slower, as it runs a constrained minimisation algorithm\n", "with several initial starting guesses, then selects the optimal one." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bounds: \n", "\tconst = (19.849417219261177, 37.63022921697883)\n", "\tpreexp = (0, 150), exp = (0, inf), exploc = (0, 0.5)\n", "\tprepowa = (0, 50), powa = (1, 20)\n", "\tprepowr = (-50, 0), powr = (1, 20)\n", "\n", "\n", "Minimizing routine number 1\n", "Initial guess: \n", "\tconst = 28.739823218120005\n", "\tpreexp = 0.0, exp = 0.0, exploc = 0.0\n", "\tprepowa = 0.0, powa = 1.0\n", "\tprepowr = 0.0, powr = 1.0\n", "Optimization terminated successfully (Exit mode 0)\n", " Current function value: 0.14458327076416183\n", " Iterations: 81\n", " Function evaluations: 738\n", " Gradient evaluations: 81\n", "\n", "\n", "Minimizing routine number 2\n", "Initial guess: \n", "\tconst = 14.369911609060003\n", "\tpreexp = 0.048145386979996374, exp = 0.0, exploc = 0.0\n", "\tprepowa = 0.0, powa = 1.0\n", "\tprepowr = -8.554071327020004, powr = 1.0\n", "Optimization terminated successfully (Exit mode 0)\n", " Current function value: 0.14458327307850274\n", " Iterations: 92\n", " Function evaluations: 833\n", " Gradient evaluations: 92\n", "\n", "\n", "Minimizing routine number 3\n", "Initial guess: \n", "\tconst = 28.739823218120005\n", "\tpreexp = 0.07221808046999456, exp = 10.0, exploc = 0.1\n", "\tprepowa = 0.01, powa = 3.0\n", "\tprepowr = 0.0, powr = 1.0\n", "Optimization terminated successfully (Exit mode 0)\n", " Current function value: 0.14458327055352374\n", " Iterations: 87\n", " Function evaluations: 792\n", " Gradient evaluations: 87\n", "\n", "\n", "Minimizing routine number 4\n", "Initial guess: \n", "\tconst = 28.739823218120005\n", "\tpreexp = 0.0, exp = 0.0, exploc = 0.1\n", "\tprepowa = 0.0, powa = 3.0\n", "\tprepowr = -0.01, powr = 3.0\n", "Optimization terminated successfully (Exit mode 0)\n", " Current function value: 0.14458327064989074\n", " Iterations: 112\n", " Function evaluations: 1019\n", " Gradient evaluations: 112\n", "\n", "\n", "Final best fit 0.14458327064989074.\n", "\n", "\n", "The initial enthalpy of adsorption is: \n", "\tE = 28.5\n", "The constant contribution is \n", "\t19.8\n", "The exponential contribution is \n", "\t8.71 * exp(15.1 * n)with the limit at 0.407\n", "The guest-guest attractive contribution is \n", "\t47.7 * n^2.6\n", "The guest-guest repulsive contribution is \n", "\t-50 * n^5.48\n" ] }, { "data": { "image/png": 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Bounds: \n", "\tconst = (23.797607891739087, 32.54769036367995)\n", "\tpreexp = (0, 150), exp = (0, inf), exploc = (0, 0.5)\n", "\tprepowa = (0, 50), powa = (1, 20)\n", "\tprepowr = (-50, 0), powr = (1, 20)\n", "\n", "\n", "Minimizing routine number 1\n", "Initial guess: \n", "\tconst = 28.17264912770952\n", "\tpreexp = 0.0, exp = 0.0, exploc = 0.0\n", "\tprepowa = 0.0, powa = 1.0\n", "\tprepowr = 0.0, powr = 1.0\n", "Optimization terminated successfully (Exit mode 0)\n", " Current function value: 0.09541800223250922\n", " Iterations: 9\n", " Function evaluations: 81\n", " Gradient evaluations: 9\n", "\n", "\n", "Minimizing routine number 2\n", "Initial guess: \n", "\tconst = 14.08632456385476\n", "\tpreexp = 6.557036324590477, exp = 0.0, exploc = 0.0\n", "\tprepowa = 2.9277314033904815, powa = 1.0\n", "\tprepowr = 0.0, powr = 1.0\n", "Optimization terminated successfully (Exit mode 0)\n", " Current function value: 0.007235460364955422\n", " Iterations: 75\n", " Function evaluations: 678\n", " Gradient evaluations: 75\n", "\n", "\n", "Minimizing routine number 3\n", "Initial guess: \n", "\tconst = 28.17264912770952\n", "\tpreexp = 9.835554486885716, exp = 10.0, exploc = 0.1\n", "\tprepowa = 0.01, powa = 3.0\n", "\tprepowr = 0.0, powr = 1.0\n", "Optimization terminated successfully (Exit mode 0)\n", " Current function value: 0.007235460317026574\n", " Iterations: 62\n", " Function evaluations: 558\n", " Gradient evaluations: 62\n", "\n", "\n", "Minimizing routine number 4\n", "Initial guess: \n", "\tconst = 28.17264912770952\n", "\tpreexp = 0.0, exp = 0.0, exploc = 0.1\n", "\tprepowa = 0.0, powa = 3.0\n", "\tprepowr = -0.01, powr = 3.0\n", "Optimization terminated successfully (Exit mode 0)\n", " Current function value: 0.007334815292145825\n", " Iterations: 62\n", " Function evaluations: 561\n", " Gradient evaluations: 62\n", "\n", "\n", "Final best fit 0.007334815292145825.\n", "\n", "\n", "The initial enthalpy of adsorption is: \n", "\tE = 37.1\n", "The constant contribution is \n", "\t26.5\n", "The exponential contribution is \n", "\t21.2 * exp(12.3 * n)with the limit at 5.53e-18\n", "The guest-guest attractive contribution is \n", "\t50 * n^18.1\n", "The guest-guest repulsive contribution is \n", "\t-45.6 * n^18.1\n" ] }, { "data": { "image/png": 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Modelling method\n", "isotherm = next(i for i in isotherms_calorimetry if i.material == 'HKUST-1(Cu)')\n", "res = pgc.initial_enthalpy_comp(isotherm, 'enthalpy', verbose=True)\n", "plt.show()\n", "\n", "isotherm = next(i for i in isotherms_calorimetry if i.material == 'Takeda 5A')\n", "res = pgc.initial_enthalpy_comp(isotherm, 'enthalpy', verbose=True)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "sci", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" }, "vscode": { "interpreter": { "hash": "bb92cfc77ebe3751b7fb3df8620ceb006227daaf69256df93b17635e0b7c66c1" } } }, "nbformat": 4, "nbformat_minor": 2 }