{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Specific surface area determination\n", "\n", "## BET surface area\n", "\n", "Let's do a calculation of the BET area for these samples. First, make sure the data is imported by running the previous 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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "pyGAPS attempts to calculate the applicable BET region on its own by using the\n", "Rouquerol rules. The function is `pygaps.characterisation.area_BET`, where we\n", "set the `verbose` parameter to obtain a printed output of results and plots." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MCM-41\n", "BET area: a = 358.5 m2/g\n", "The BET constant is: C = 129.2\n", "Minimum pressure point is 0.0337 and maximum is 0.286\n", "Statistical monolayer at: n = 0.00368 mol/g\n", "The slope of the BET fit: s = 270\n", "The intercept of the BET fit: i = 2.11\n", "\n" ] }, { "data": { "image/png": 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" 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "isotherm = next(i for i in isotherms_n2_77k if i.material == 'MCM-41')\n", "print(isotherm.material)\n", "results = pgc.area_BET(isotherm, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks that the correlation is reasonably good. A warning is emitted if this\n", "is not the case. We can also restrict the pressure range manually to see what\n", "difference it would make, by using the `limits` parameter." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BET area: a = 361.7 m2/g\n", "The BET constant is: C = 111.3\n", "Minimum pressure point is 0.0513 and maximum is 0.194\n", "Statistical monolayer at: n = 0.00371 mol/g\n", "The slope of the BET fit: s = 267\n", "The intercept of the BET fit: i = 2.42\n", "\n" ] }, { "data": { "image/png": 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" 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "results = pgc.area_BET(isotherm, p_limits=(0.05, 0.2), verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's do the analysis on all of the nitrogen samples. We'll assume the\n", "framework makes a reasonably accurate choice for the applicable range. The\n", "function returns a dictionary with all the calculated parameters, and we'll\n", "print the BET area from there." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n",
       "[\n",
       "    (<pygaps.Material 'MCM-41'>, '358.53'),\n",
       "    (<pygaps.Material 'NaY'>, '699.23'),\n",
       "    (<pygaps.Material 'SiO2'>, '210.39'),\n",
       "    (<pygaps.Material 'Takeda 5A'>, '1109.80'),\n",
       "    (<pygaps.Material 'UiO-66(Zr)'>, '1275.36')\n",
       "]\n",
       "
\n" ], "text/plain": [ "\n", "\u001b[1m[\u001b[0m\n", " \u001b[1m(\u001b[0m\u001b[1m<\u001b[0m\u001b[1;95mpygaps.Material\u001b[0m\u001b[39m \u001b[0m\u001b[32m'MCM-41'\u001b[0m\u001b[1m>\u001b[0m, \u001b[32m'358.53'\u001b[0m\u001b[1m)\u001b[0m,\n", " \u001b[1m(\u001b[0m\u001b[1m<\u001b[0m\u001b[1;95mpygaps.Material\u001b[0m\u001b[39m \u001b[0m\u001b[32m'NaY'\u001b[0m\u001b[1m>\u001b[0m, \u001b[32m'699.23'\u001b[0m\u001b[1m)\u001b[0m,\n", " \u001b[1m(\u001b[0m\u001b[1m<\u001b[0m\u001b[1;95mpygaps.Material\u001b[0m\u001b[39m \u001b[0m\u001b[32m'SiO2'\u001b[0m\u001b[1m>\u001b[0m, \u001b[32m'210.39'\u001b[0m\u001b[1m)\u001b[0m,\n", " \u001b[1m(\u001b[0m\u001b[1m<\u001b[0m\u001b[1;95mpygaps.Material\u001b[0m\u001b[39m \u001b[0m\u001b[32m'Takeda 5A'\u001b[0m\u001b[1m>\u001b[0m, \u001b[32m'1109.80'\u001b[0m\u001b[1m)\u001b[0m,\n", " \u001b[1m(\u001b[0m\u001b[1m<\u001b[0m\u001b[1;95mpygaps.Material\u001b[0m\u001b[39m \u001b[0m\u001b[32m'UiO-66\u001b[0m\u001b[32m(\u001b[0m\u001b[32mZr\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m\u001b[1m>\u001b[0m, \u001b[32m'1275.36'\u001b[0m\u001b[1m)\u001b[0m\n", "\u001b[1m]\u001b[0m\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results = []\n", "for isotherm in isotherms_n2_77k:\n", " results.append((isotherm.material, pgc.area_BET(isotherm)))\n", "\n", "[(x, f\"{y['area']:.2f}\") for (x, y) in results]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also have isotherms which were measured with $CO_2$ at room temperature.\n", "While there's no guarantee that the BET method is still applicable with this\n", "adsorbate and temperature, we can still attempt to perform the calculations.\n", "\n", "It just happens that the isotherms were recorded on the same Takeda 5A carbon\n", "sample (see $N_2$ results above). Let's see how the $CO_2$ BET surface area\n", "looks in comparison." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Takeda 5A\n", "BET area: a = 739.1 m2/g\n", "The BET constant is: C = 31.7\n", "Minimum pressure point is 0.0369 and maximum is 0.285\n", "Statistical monolayer at: n = 0.00722 mol/g\n", "The slope of the BET fit: s = 134\n", "The intercept of the BET fit: i = 4.36\n", "\n" ] }, { "data": { "image/png": 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ZMWPG8OSTT7JhwwZ69+5NcnIyb731FjNmzCAyMrLE4n9KVTdZWVm8++67DB8+nP/7v/9zdTiuU1pZWU9+OFNmvGvXrhc93njjDRERycrKKnH9/PnzRUQkLS3tonWXcvjwYfH29pbExEQpLCyU6OhoGTZsmFgsFlm+fLn06dNHRETGjh0rU6ZMERGRtWvXSps2bURE5LHHHrOXDl+5cqUAkpaWJnv37pXevXtLXl6eiIg8/PDDsnDhQhE5v8z3hX3funWriFjLhN99990iIjJ58mSZPn36JfuiVFVlsVhk1apVMmrUKLFYLCLy+y0BqjoclBnXQ0+VqHHjxoSHhwPW2k89evTAGEN4eLi9NPfmzZv52Da8vfXWW0lPTyczM5ONGzfyySefAHD33XdTu3ZtANauXcu2bdto164dAOfOnSM4OLiSe6aU5/vxxx954okn+OqrrwgLCyMtLY3g4ODzbglQXVXbRLFhw4ZS1/n7+ztcX69ePYfrS1O8HLeXl5f9vZeXl700t5RwpXxRCfCSypSLCEOGDNEie0pdoczMTCZOnMjbb7/NNddcw4wZM3jkkUfw9fV1dWhuQ+co3EyXLl14//33AWsyq1evHjVr1jxv+Zdffsnp06cBa/nyZcuWkZqaCljnOIrftOhyFC8LrlR14e3tzcqVKxkzZgxJSUk8/vjjmiQuoInCzUyZMoWEhAQiIiKYMGECCxcuBGDy5Mls3LiR6Oho1qxZQ6NGjQBo2bIl06ZN4/bbbyciIoKePXty/PjxK/rse+65h08//VQns1WVJiIsX76cu+66i7y8PAIDA9m3bx9xcXH2O1Cq82lRQKVUtbFz507GjRvHhg0baNmyJStXrqRx48auDssteExRQGOMtzFmhzFmpe19HWPM18aYJNtzbVfHqJTyPGfPnmXEiBFER0eze/du3njjDXbt2qVJwklulSiAvwH7ir2fAKwVkTBgre29Uko5peiIib+/Pz/++CPjxo0jKSmJRx55hBo1qu25PJfNbRKFMeZ64G7gnWKL+wALba8XAn0rOSyllAcSEZYuXUpMTAynTp3Cy8uLTZs28e9//9t+arlyntskCmAm8BRgKbbsWhE5DmB7LvUCAWPMKGNMgjEmIS0trUIDVUq5r4SEBLp06cIDDzxAQUEBKSkpADqCKAO3SBTGmN5Aqohsu9J9iMgcEYkRkZigoKByjE4p5Qny8/MZMmQI7dq14+DBg8yZM4ft27fTsmVLV4fm8dwiUQCdgHuNMcnAR8CtxpjFwAljTH0A23NqZQSzfv16WrRoQVJSksNlSinXK6pt5uPjQ15eHuPHjycpKYmRI0fi7e3t4uiqiNJqe7jqAXQDVtpeTwcm2F5PAF5xZh/O1Hoqzbp168Tf31+MMRIVFSWFhYUlLvMEAQEBrg7BodmzZ9vrUpVmx44dsmrVqkqKSLmr0ydPSsKUKbK9f39JmDJFTp88KYWFhbJ48WJp3Lix7N+/X0TEXp9JXT4c1HpyeWK4KKDzE0VdrGc7Jdme6zizjytNFEUJARBA/P39ZfDgwRcte/XVV69o/5WtIhOFxWKplIQ5f/58efTRRyv8c5T7On3ypCQ3bSo5vr5iMUZyfH3l4wYNpG10tAASHR0tO3bscHWYHs+jEkV5PK40UTRv3lyMMfakUJQYir8HJCQk5LL3ffjwYWnevLk89NBD0qpVK3nwwQfl66+/lptvvlmaNm0qP/zwg4hYK1X26dNHwsPDpUOHDrJr1y4RsVZ2HTZsmHTt2lUaN24sr7/+un3f//73v6VVq1bSqlUrmTFjhn15UaL47bff5NZbb5WoqChp3bq1LF++XEREnnnmGZk5c6a9/cSJE+37feWVVyQmJkbCw8Plueees/ehRYsW8vDDD0tkZKQkJyef18cbbrhBnnrqKWnXrp20a9dOkpKSREQkOTlZbr31VgkPD5dbb71VfvnlF3ufiqrVdu3a1b5tWFiYbNy4UXJzc6Vhw4ZSr149adOmjXz00UeX/XNXni9hyhTJ8fUVAbGADLH9O6wXGCgLFizwmBG+u9NE4aSDBw9KZGSkBAQEXJQciieO9evXX/a+y1pmfPLkydKxY0fJycmRtLQ0qVOnjuTl5UlCQoK0bt1azp49K7/99pu0bNlStm/fLiK/J4r8/HzJzMwUEWuJ9CZNmojFYpHDhw9LVFSUiIgUFhbKjTfeKCdPnpSvvvpKRo4caR813H333fLtt9/K4cOHxRgjW7ZsKbGPN9xwg0ybNk1ERBYuXGgvX967d29ZsGCBiIjMmzfP3tcLE8Xf//53ERFZtWqV9OjRQ0R0RKFEtvfvL9lg/e8KZBLIMyCb773X1aFVKY4ShbtMZruFsLAwEhIS+OMf/4i/v/9F6/39/Zk9ezbdunW7ov0XlRn38vJyWGZ80KBBwPllxsFaXtzPz4969eoRHBzMiRMn2Lx5M/369SMgIIDAwED69+9/UZ0mEWHixIlERERw22238euvv3LixAlCQ0OpW7cuO3bsYM2aNURFRVG3bl3WrFljfx8dHc3+/fvtk/g33HADsbGxpfZxwIAB9uctW7YAsGXLFh588EEABg0axObNm0vctn///gC0bdvW/vNQ1ZvFYmF5YSFNgHW2ZdOAZ3x9uSo62oWRVS96YvEFNm7cyLJly8jOzr5oXU5ODjNnzuQvf/kLXl6Xn2PLWma8+Pbe3t4UFBSU2P5C77//PmlpaWzbtg0fHx9CQ0PJyckBYMSIESxYsICUlBSGDx9uj+Hpp59m9OjR5+0nOTmZgIAAh59VvBR6SWXRHS0v6l9R31T1tnHjRsaNG8f27duJ9PPDv7AQKSwkz8eHlEaNaDJ2rKtDrDZ0RFHM+vXr6d27d4lJAqx/3Rw4cIAZM2ZUWAyllRl31H758uVkZ2eTlZXFp59+yi233HJem8zMTIKDg/Hx8WH9+vXnlSHv168fq1evZuvWrdxxxx0A3HHHHbz77rucPXsWgF9//dVexvxSlixZYn/u2LEjADfffDMfffQRYE1anTt3dmpfoKXPq6tRo0bRtWtX0tLSeP/991n3v//h88wz7OzXjz0TJ/KH+Hhq1a3r6jCrDR1RFPPwww9z7ty585b5+/uTk5ODxWK9YDw7O5tXX32VJ554okJimDJlCsOGDSMiIgJ/f397mfHSREdHM3ToUNq3bw9YRwhRUVHntRk4cCD33HMPMTExREZG0qJFC/s6X19funfvTq1ateznnN9+++3s27fP/h99YGAgixcvduqc9NzcXDp06IDFYuHDDz8EYNasWQwfPpzp06cTFBTE/Pnznf55dO/enZdeeonIyEiefvpp/vSnPzm9rfIsZ86cISAgAG9vb9q3b0/Dhg154okn7IeB206e7OIIq7HSJi88+VFek9n+/v6ycOFCiYqKOm/ZunXrrmj/7qiwsFDatGkjBw8eLPO+SrtHt1KOFBQUyFtvvSVBQUEyd+5cV4dTbaGT2c4pmsyeOnUqISEhrFq1isGDB7N161b7spUrV9K9e3dXh1ou9u7dS9OmTenRowdhYWGuDkdVQ9988w1RUVGMGTOGFi1aEK0T1G5Jb1yklHKJxx9/nNdff53GjRszffp0+vfvX+qJDqriObpxkc5RKKUqzenTp/Hx8SEwMJA777yT+vXr87e//Y2rrrrK1aEpB/TQk1KqwuXn5xMXF0fTpk156aWXAOvZdePHj9ck4QE0USilKtSXX35JREQEjz32GJGRkdx///2uDkldJk0USqkK8+yzz3LXXXdRUFDAZ599xjfffEObNm1cHZa6TDpHoZQqVydPnqSgoICQkBAeeOAB6tSpw6OPPoqvr6+rQ1NXSEcUSqlykZeXx2uvvUbTpk3tF6SGh4czbtw4TRIeThOFUqpMRITPPvuMVq1a8cQTT9CxY0cmTZrk6rBUOdJEoZQqk9dee42+ffvi4+PDF198wZdffqn3qa5idI5CKXXZTpw4QWZmJs2aNeMvf/kLV199NSNHjsTHx8fVoakKoCMKpZTTcnJyePnllwkLC2PkyJEAXHvttTzyyCOaJKowTRRKqUsSEZYtW8ZNN93EhAkT6N69O3PnznV1WKqS6KEnpdQlLVy4kGHDhhEeHs4333xDjx49XB2SqkSaKJRSJTp27BhHjx6lQ4cO9vuADBo0yKn7kqiqRQ89KaXOk52dzfPPP0+zZs0YMmQIFouFq6++mqFDh2qSqKY0USilAOs8xIcffkiLFi147rnnuPPOO/niiy+u6P7wqmrR3wClFACrV6/mwQcfJCgoiG+//ZalS5dy4403ujos5QY0UShVjR05coQVK1YA0KtXL5YvX87WrVvp0qWLiyNT7kQThVLV0NmzZ3n22Wdp3rw5I0aMIDc3F2MMffr00UNN6iL6G6FUNWKxWFiwYAHNmjVj2rRp9OvXj61bt+Ln5+fq0JQb09NjlapGdu7cybBhw+jQoQMff/wxHTt2dHVIygPoiEKpKu7w4cPMmzcPgOjoaDZt2sT333+vSUI5TROFUlXUmTNnmDBhAi1atGDcuHGcOnUKgM6dO+s8hLos+tuiVBVTWFjI3LlzCQsL4+WXX2bAgAHs27ePOnXquDo05aF0jkKpKubYsWM89thjtGvXjlWrVhETE+PqkJSHc4tEYYy5CtgI+GGNaZmITDbG1AGWAKFAMvCAiJx2VZxKuaukpCTmz5tH/6uuwnv3bhY99BA9p06ldr16rg5NVQFlThTGmEIRKWsBmFzgVhE5a4zxATYbY74E+gNrReQlY8wEYAIwvoyfpVSVcfr0aZ5//nni4uLwKyhgSI0aNCsooKWPDylr1mDi46lVt66rw1QerjzmKExZdyBWZ21vfWwPAfoAC23LFwJ9y/pZSlUF+fn5xMXFERYWxsyZM+kdHs6PNWrQPD8fI4JfXh4hR45wKC7O1aGqKqA8EoUUf2OMCTLGNLGNDJxmjPE2xuwEUoGvReQH4FoROQ5gew52sP0oY0yCMSYhLS3tsjuhlCfJyclh2rRpREREsGPHDp4NDaVhQcF5bXzz8/FKTHRRhKoqKbeznmz/Ub8HvACMBhYYY940xjR2ZnsRKRSRSOB6oL0xpvXlfL6IzBGRGBGJCQoKutzwlXJ7+/bt4+GHHyY/P59rrrmGrVu3snbtWtq0aYMlIoK8C25FmufjgyUiwkXRqqqkPE+PTRCRQSIySkSeEpGBwJNc5jyIiGQAG4BewAljTH0A23NqOcarlEdIT0/nscceIzw8nA8//JAff/wRgIYNG2KM9chvk7FjSWnUiFxfX8QYcn19SWnUiCZjx7oydFVFlMschTHmdqC77dlORLJEJMmJHQQZY2rZXl8N3AbsBz4HhtiaDQE+K4d4lfIIBQUFzJw5k6ZNmzJ79mxGjx5NUlISkZGRF7WtVbcuf4iPZ8/Eiezs1489EyfyB53IVuXEiMilWznagTEWYDKwAugvIs9dwT4isE5We2NNXv8RkX8aY+oC/wEaAUeA+0Xk1KX2FxMTIwkJCZcbhlJupbCwkOjoaOrXr8+///1vWrVq5eqQVBVmjNkmIiVedFMeiaIQqA3cDqwFvEQkvUw7LSNNFMpTJSYm8s9//pO5c+dSu3ZtTp8+Te3atV0dlqoGHCWKcpmjEJEzIrIMmAtMM8a8b4zpXh77Vqo6SE1NZfTo0URFRbF+/Xr27NkDoElCuYXyrvV0QEQetk1k31fO+1aqyrFYLLzyyis0bdqUd999l7/+9a8kJSVxyy23uDo0pezKu4RHL2PMKWAXUHCpxkpVd15eXmzatIlu3boxffp0mjdv7uqQlLpIeY8oegH7gE5AkDFm4SXaK1XtbNu2jZ49e3Lo0CEAli5dyueff65JQrmt8rzgrq2IpInIFyIyVUQeFJEhl95Sqerh2LFjDBs2jHbt2rFr1y57orjqqqtcHJlSjpXniKKVMeZ1Y8yrxpg+xhj/cty3Uh7t5ZdfplmzZrz//vv84x//ICkpidtvv/3SGyrlBhzOUdguoGsN7BGRNY7aisgiWwmPukAE8KwxJkBE/lpu0SrlQUTEfuX0z4cO0b5RIx5v2JAGAQFIgU7hKc9xqRFFLLAO6Ozk/pYC/wRGAGs0SajqKj4+nptvvpmNGzeSkZ7O+HXr+PLQIe75+mtav/gimbGxZKS79HIjpZx2qUTxOtAUeM3J/R0QkUdE5EH09FhVDR09epSBAwfSsWNHkpOTyczM5FBcHA2OHsUvL09LgCuP5DBRiEimiCwTkQxbOY1L6WWMecIYcxt6eqyqZl599VWaN2/OJ598wqRJkzh48CD33HMPXomJ+Obnn9dWS4ArT+LUZLYx5mOcu+JaT49V1YrFYsFisQBQo0YN+vTpw/79+5k2bRrXXHONtY2WAFcezqlaT8aYF0Rkku31GyLyaLF15XEr1HKltZ5UZdi8eTPjxo1j7NixDBky5LzJ6+Iy0tPJjI0l5MgRfPPzyfPxIaVRI63uqtxKedR60kNKqtpYv349LVq0ICkpqcRlhw8f5oEHHuCWW27h+PHj1KxZE6DEJAFaAlx5vkuOKIwxbYGjQAzQDmgO5BddTHfhiMIYU09ETlZcyJemIwp1pdavX0/v3r05d+4ckZGRJCQk8O2339qXXXfddaSlpVGjRg2eeuop/vGPfxAQEODqsJUqM0cjCmdqPbUCBgP5wCZguohkO2j/LnDvZUeplIsVJYnsbOuv94EDBxg2bBjLli2zLzt58iTh4eEsX76c66+/3pXhKlVpnJ2jMFgvpAvHet8J+4V0JYwoVojIPRUUr1N0RKGuRIsWLTh48CDF/034+fmRm5t7XruQkBCOHz9e2eEpVaHKY46i6EK6kVz6Qrqy3QlJKRdZsWIFbdq0Oe9Q0oVJwt/fnw8//LCyQ1PKpZxNFJdzIV3JM3pKubmwsDASEhIIDw8vcb2/vz+zZ8+mW7dulRuYUi5WEWc9PV3GmJSqdAUFBWRmZrJx40Z27txZYpucnBxmzpxpv25CqerC6USBkxfSicie8ghMqcry8dKlNL/uOu5s1oy77riDnJycEttZLBYOHDjAjBkzKjlCpVzLqclshzso5YI7Y8x4EXm5TDu/QjqZrZyxb98+/vbXv/L1N99wI5ADHOf8STZ/f39ycnLOG0XoZLaqispjMtuZD1lqjPmP7bEUawVZpdzSBx98QHh4ON9v2sRL3t7sBTYAbYCiG6kUzUkUn+D29/fngw8+cE3QSrlIed64KFNEHrA97ge+Kcd9K1VmeXl59pFAt27deOSRR/j8ttt4ymLBDwgDEoCpQF0/P1atWsXgwYPZunUrU6dOJSQkhJUrV9K9u6NyZ0pVPeV26MkY01hEDhdbXkdETpU5wiugh55UcSLCypUreeKJJwgJCeHbb7+1l9vYNnUqrV98Eb+8PHv7XF9f9kycSNvJk10VslKVrlIOPWE9vGtXlCSMMXpDYOUyiYmJ9OzZk3vvvRcvLy8mTJhw3vomY8eS0qgRub6+iDHk+vqS0qgRTcaOdVHESrkfZ0p4OOslY4wF2AtkAI1tj/eB78rxc5RyyqpVq7j33nv5wx/+wKxZsxgzZgw+F5T7rlW3LsTHsycuDq/ERCwRETQZO1YL9ilVTLme9WSMqQ1EArWBg646VVYPPVVfubm5JCcn07x5c7Kzs3nxxRf5+9//Tp06dVwdmlJuraxFAS/HbKwX5AnWQ1FPlfP+lSqRiPDJJ5/w1FPWX7n9+/fj7+/PtGnTXByZUp6vPOcoALaIyF9EZBDgkmsoVPWzfft2unXrxn333Ye/vz9vvfXWRYeYlFJXrrxHFH1s8xRficjBct63Uhf5/vvv6dy5M3Xr1mX27NmMGDGCGjXK+9daqeqtPEYUxYsADgIOAX80xrxTDvtW6iLnzp3jhx9+ACA2NpZXX32VpKQkxowZo0lCqQpQ5slsd6ST2VWTiLBkyRLGjx9PZmYmR44csd+GVClVNpV1HYVSFeaHH36gU6dODBgwgDp16rB8+XJNEkpVErdJFMaYhsaY9caYfcaYH40xf7Mtr2OM+doYk2R7ru3qWFXlOnjwILGxsRw+fJh58+aRkJCg94RQqhK5TaLAelrtEyJyExALPGqMaQlMANaKSBiw1vZeVXFZWVl88cUXADRr1oz333+fgwcPMnz4cLy9LypWrJSqQG6TKETkuIhst73+Dev9LxoAfYCi+18sBPq6JEBVKSwWC++99x7NmjWjT58+/PrrrwA8+OCDXHPNNS6OTqnqyW0SRXHGmFAgCvgBuFZEjoM1mQDBpWwzyhiTYIxJSEtLq7RYVfn57rvv6NChA4MHD6ZBgwZs2LCBBg0auDospao9t0sUxphA4GPgcRE54+x2IjJHRGJEJCYoKKjiAlQVIjU1lR49enD8+HEWLVpEfHw8nTp1cnVYSincLFEYY3ywJon3ReQT2+ITxpj6tvX1gVRXxafK15kzZ5g/fz4AwcHBrFixggMHDjBo0CC8vNzqV1Opas1t/jUa6w0C5gH7ROS1Yqs+B4bYXg8BPqvs2FT5Sk9N5dl77+XG4GCGDx/Od5s2AdCzZ0/7neSUUu7DbRIF0Anrld23GmN22h53AS8BPY0xSUBP23vloVYsX84tDRsybcUKmufmsqlGDa4fPpyM9HRXh6aUKoXb1DsQkc2cXw6kuB6VGYuqGNnZ2QweOJCaeXl8BDwAmIICco8cYU9cnN5RTik35U4jClUFZWRk8PLLL1NQUIC/vz9vdOjAPuBP/P5XgW9+Pl6JiS6MUinliCYKVSEKCgqYPXs2YWFhPP3002zevBmA5l274u3re17bPB8fLBERrghTKeUETRSq3K1Zs4bIyEgeeeQRWrVqxbZt2+wlN/Qe1Up5HreZo1BVQ2FhIePGjSMnJ4ePP/6Yfv36YT2hzUrvUa2U59Ey46rMTp06xfTp03n66aepWbMmhw4d4vrrr8fPz8/VoSmlnKRlxlWFyM/P5/XXX6dp06a88sorrFu3DoAmTZpoklCqCtFEoS6biLBy5Upat27N448/TkxMDLt27aJv376uDk0pVQF0jkJdNmMMcXFxGGNYuXIld91113nzEEqpqkVHFMopaWlpPProoyQnJwOwaNEidu/ezd13361JQqkqTkcUqkQZ6ekcioujYMcOPszO5t34eLKzs2nfvj2hoaEEB5dY7V0pVQVpolAXyUhPJzM2lp+Tk3m6oIBDwK3+/vxr82bax8a6OjylVCXTQ0/qIofi4gg5coR1BQVcBXwFfFFQgPdXX7k6NKWUC+iIQtmlpKTwzDPP0Pmnn4jOz2c6cBXWXxLRekxKVVs6olCcO3eOF198kbCwMBYtWsSBwEDyfHwI5Pe/JLQek1LVlyaKau7TTz/lpptuYtKkSfTs2ZO9e/cyfuFCrceklLLTQ0/VXFJSErVr12bBggX2wn2A1mNSStlpradq5n//+x9PP/00d911FwMGDCA/Px8vLy+8vb1dHZpSyoW01pMiKyuLKVOm0KxZM5YuXcrx48cB8PHx0SShlHJIDz1VA5999hmPPvoov/76Kw888AAvv/wyoaGhrg5LKeUhNFFUYSKCMYb8/Hzq16/PRx99ROfOnV0dllLKw+gchYcrKrVRfNI58+xZxo8fT0REBBMnTkREEBG8vPRIo1KqZI7mKHRE4cGKSm20PnIE3/x80les4JnXX+ed7Gy8vLyIjo4GrNVetXCfUupKaaLwYIfi4mh95Ah+eXmsAkbk55Ny+jR3RkQwZ9Uqrr/+eleHqJSqAjRReDCvxES88vIACAKaAMsB36ZNNUkopcqNHrT2UIcOHeKJvXsZbZt3aA9sAiJ9fbXUhlKqXGmi8DCZmZk8+eST3HTTTfz36FGCatcmx8cHMYY8LbWhlKoAeujJzZR0FlNR6Yy1a9cyYMAATp48ydChQ3nhhRe42teXH7XUhlKqAunpsW6k6CymENtZTHk+PqQ0aoTPunVc17AhR48eZeTIkbz44ov2M5qUUqo86OmxHqL4WUwAyXl5/P3QIdK6duWHQ4do2LAhq1evdnGUSqnqRuco3IhXYiK++fmcAh4HWgObROgYEIDFYnFtcEqpaktHFC5S0lyEJSKCjZ9/Tr+CAjKBkcAkHx9S77tPC/cppVxGE4ULXHhFde6KFexbuJCgNWu4etEibktO5hmLheZ6FpNSyg24zaEnY8y7xphUY8yeYsvqGGO+NsYk2Z5ruzLG8nIoLo4Q21zEjyLcm5/Pnw4f5viiRVz33/8y/rnnKOzfnz0TJ/KH+Hg9i0kp5VLuNKJYAMQBi4otmwCsFZGXjDETbO/HuyC2K1Laqa5eiYlk5uUxGZgD1ASeA8zu3dSqW5e2kye7NnCllCrGbRKFiGw0xoResLgP0M32eiGwAQ9JFBceXspbuZKUxYshPp6kkBC6AVnAo8BkINDXlz2Rka4MWSmlSuQ2h55Kca2IHAewPQeX1tAYM8oYk2CMSUhLS6u0AEtT/PCSEcE3L4+cX37hUFwcPSZPpv8f/sA2Hx9eN4ZAnYtQSrkxd08UThOROSISIyIxQUFBrg7HfqorwE7gVqBjfj6/JSRQNziYGYcOUTBpEjv79dO5CKWUW3ObQ0+lOGGMqS8ix40x9YFUVwfkLEtEBEdWrGBafj7zgDrAZG9vro6KAtC5CKWUx3D3EcXnwBDb6yHAZy6M5bJc/cc/0rqggIVYL57b4+ND38aNaf63v7k4MqWUujxuM6IwxnyIdeK6njHmf1jneF8C/mOMeQg4Atzvugh/l5GRQXx8PKmpqQQHBxMbG0utWrUQEXbt2kVkZCQtW7fmiWefpc3p04T++iu/asE+pZSH0qKAlykjI4O5c+eSm5tLSEgIKSkp+Pn50bZtW5577jl++OEH9u/fT5MmTSrk85VSqiJoUcByFB8fT25uLqNHjyYoKIhdu3YxZMgQnnrqKYKDg5k9ezahoaGuDlMppcqNJorLlJqaSkhICEFBQZw5c4auXbuSlZXF3XffzQcffEDNmjVdHaJSSpUrTRQOlDQXUa9ePZYtW0afPn0ICgrihRde4Oeff6Znz56aJJRSVZImilJcOBeRkJDAypUr+e6779i5cyc1atSgffv2nD59mpCQEGJjY10dslJKVQhNFKUoPheRnZ3NuHHj+PTTT6lTpw5vvvkmN9xwAydPniQmJsZ+1pNSSlVFmihKUTQXUatWLaKiokhPT6d3797cc889jBo1ytXhKaVUpdFEUQKLxcKBAwfw8/MjIyODefPmERwczKpVq2jUqJGrw1NKqUpV7RPFhRPWhYWFPPvss+zYsYPBgwfz9ttvExISwtatW/Hz89O5CKVUtVOtE0XxCWsvLy9efvll9u7dS4MGDfjwww+54447+OGHH0hNTdW5CKVUtVUtE0XRKGLfvn2cO3eOgQMH0q9fP5KTk+nRowePPfYYffr0AaBXr14ujlYppVyr2iWKolFEdnY2u3fvplmzZnzyySfMmjWLZs2a8cUXX5CZmenqMJVSym24e/XYchcfH8/evXtZsmQJH3/8MXv27CEnJ4ecnBx8fHxISUkhOLjU+yMppVS1U21GFBkZGSxbtozXXnuNffv2ccMNN7BgwQJOnDjBuXPn2LlzJwkJCTphrZRSF6gWiaLocNOcOXM4evQot912G127dqVPnz6cOnWKxYsX4+fnR2RkpE5YK6XUBap0mfH8/HxmzJhBYWEheXl5BAcH0759e9auXcu5c+cICAggJycHPz8/Ro4cqQlCKVVtOSozXmXnKL788ktat27N+PHjWbt2LT4+PqSnp7Nu3Tr+/Oc/ExAQQGFhITExMZoklFLKgSqZKJKSkrjrrrvIyspi4MCBPPLII1gsFu6//35yc3PZtm0bOTk5tGnThl69emmSUEopB6rkHEVWVhYzZsygZs2aWCwWbr31Vg4dOsTSpUvx8vIiMTERf39/nbRWSiknVMkRxU033USLFi3IzMzk2LFjnDp1ipEjR9KqVSvy8/MJCQnRw01KKeWkKjmiOH36NAkJCdSrV48zZ87w3nvvce2113Ly5En8/f3505/+pElCKaWcVCUThYjY72n9888/s3jxYs6dO6f1mpRS6gpUyUTh5eXFl19+ab996XXXXYevr6/WbVJKqStQJRNFYWEhWVlZJCQksGvXLvLz84mJKfH0YKWUUpdQJSezvby8SE9Pp27duuTk5GCM0TOclFLqClXJRBEUFERMTAwBAQEEBATYb2mqlFLq8lXJRCEi9OrVizvvvJOcnBwaNGjg6pCUUspjVck5irS0NN555x1SUlK0GqxSSpVRlUwUAQEB+Pr66umwSilVDqpkoqhZsyaDBw92dRhKKVUlVMk5CqWUUuVHE4VSSimHNFEopZRySBOFUkophzwiURhjehljDhhjfjLGTHB1PEopVZ24faIwxngDbwB3Ai2BAcaYlq6NSimlqg+3TxRAe+AnEflZRPKAj4A+Lo5JKaWqDU+4jqIBcLTY+/8BHS5sZIwZBYyyvc01xuyphNgqSj3gpKuDKCPtg3vQPrgHT+jDDaWt8IREYUpYJhctEJkDzAEwxiSIiMfWFff0+EH74C60D+7B0/vgCYee/gc0LPb+euCYi2JRSqlqxxMSxVYgzBjT2BjjC/wZ+NzFMSmlVLXh9oeeRKTAGDMW+ArwBt4VkR8vsdmcio+sQnl6/KB9cBfaB/fg0X0wIhcd7ldKKaXsPOHQk1JKKRfSRKGUUsohj0oUlyrlYaxm2dYnGmOind22spSxD8nGmN3GmJ3GmITKjfy8GC/VhxbGmC3GmFxjzD8uZ9vKUsY+eMr3MND2O5RojPneGNPG2W0rSxn74CnfQx9b/DuNMQnGmM7Obus2RMQjHlgnsg8BNwK+wC6g5QVt7gK+xHrtRSzwg7PbunsfbOuSgXoe8D0EA+2AF4B/XM627t4HD/sebgZq217f6aH/Hkrsg4d9D4H8Ph8cAex3p+/BmYcnjSicKeXRB1gkVvFALWNMfSe3rQxl6YO7uGQfRCRVRLYC+Ze7bSUpSx/chTN9+F5ETtvexmO9BsmpbStJWfrgLpzpw1mxZQYggN8vGHaX7+GSPClRlFTKo4GTbZzZtjKUpQ9g/QVbY4zZZitZ4gpl+Vl60vfgiCd+Dw9hHaleybYVpSx9AA/6Howx/Ywx+4FVwPDL2dYduP11FMU4U8qjtDZOlQGpBGXpA0AnETlmjAkGvjbG7BeRjeUa4aWV5WfpSd+DIx71PRhjumP9T7bo2LjHfQ8l9AE86HsQkU+BT40xXYDngduc3dYdeNKIwplSHqW1cZcyIGXpAyJS9JwKfIp16FrZyvKz9KTvoVSe9D0YYyKAd4A+IpJ+OdtWgrL0waO+hyK2RNbEGFPvcrd1KVdPkjj7wDr6+RlozO8TP60uaHM3508E/9fZbT2gDwHANcVefw/0csc+FGs7hfMnsz3me3DQB4/5HoBGwE/AzVfafzfugyd9D035fTI7GvjV9u/bLb4Hp/rp6gAu80u5CziI9UyBSbZlY4AxttcG602ODgG7gRhH23pSH7CeGbHL9vjRzfsQgvWvpTNAhu11TQ/7Hkrsg4d9D+8Ap4GdtkeCo209qQ8e9j2Mt8W4E9gCdHa37+FSDy3hoZRSyiFPmqNQSinlApoolFJKOaSJQimllEOaKJRSSjmkiUIppZRDmiiUqmDGmAHGmEmXaDPOGLPdGPOnyopLKWdpolCq4vUCVpe20hgTiLVSbXvgwcoKSilnaaJQqhwYY0KNMfuNMQtt9x5YZozxN8YYIBLYboyZYox5zxizzhiTZIwZWbS57VkvalJuSROFUuWnOTBHRCKwXtH9CBAF7JLfr2yNwFqmpSPwnDHmOhH5DetV+AnAksoPWynHNFEoVX6Oish3tteLsVY67cX5pbE/E5FzInISWI+tkJ2I/EtEokTkg0qNWCknaKJQqvxceOhIgNuBNZdoo5Rb00ShVPlpZIzpaHs9AGsRuBpSrDQ20McYc5Uxpi7QDdhauSEqdfk0UShVfvYBQ4wxiUAd4DDwzQVt/ov1LmfxwPNiu6eCUu7Mk+5wp5S7s4jImKI3xph3sJbJLu6giLjqtp1KXRFNFEpVEBEZ4eoYlCoPej8KpZRSDukchVJKKYc0USillHJIE4VSSimHNFEopZRySBOFUkoph/4fqq4JRb6nnEUAAAAASUVORK5CYII=" 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "isotherm = next(i for i in isotherms_calorimetry if i.material == 'Takeda 5A')\n", "print(isotherm.material)\n", "results = pgc.area_BET(isotherm, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The surface area obtained with carbon dioxide is around 740 $m^2$. Compared to\n", "the nitrogen surface area of 1100 $m^2$, it is much smaller. While the checks\n", "implemented did not find anything wrong, this is likely due to interactions\n", "between carbon dioxide and the carbon surface leading to the breakdown of the\n", "BET theory.\n", "\n", "While any kind of adsorbate and temperature (below critical) can be used, result\n", "interpretation is left at the discretion of the user. More info can be found in\n", "the [documentation of the module](../reference/characterisation/area_bet.rst)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Langmuir surface area\n", "\n", "Another common method of calculating specific surface area relies on fitting the\n", "isotherm with a Langmuir model. This model assumes adsorption occurs on active\n", "surface/pore sites, in a single layer. We use the\n", "`pygaps.characterisation.area_langmuir` function, which defaults to a region of\n", "0.1-0.9 p/p0 for the fitting (see\n", "[documentation](../reference/characterisation/area_lang.rst) for details). We\n", "pass the `verbose` parameter to display the results automatically." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MCM-41\n", "The correlation is not linear.\n", "Langmuir area: a = 1409 m2/g\n", "Minimum pressure point is 0.0513 and maximum is 0.859\n", "The Langmuir constant is: K = 2.72\n", "Amount Langmuir monolayer is: n = 0.0144 mol/g\n", "The slope of the Langmuir fit: s = 69.2\n", "The intercept of the Langmuir fit: i = 25.4\n", "\n" ] }, { "data": { "image/png": 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "isotherm = next(i for i in isotherms_n2_77k if i.material == 'MCM-41')\n", "print(isotherm.material)\n", "results = pgc.area_langmuir(isotherm, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The correlation is not very good due to condensation in mesopores of MCM-41,\n", "which is not predicted by the Langmuir model. Due to this, the area calculated\n", "is not realistic. We can manually select a range in the monolayer adsorption\n", "regime for a better fit." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MCM-41\n", "Langmuir area: a = 550.1 m2/g\n", "Minimum pressure point is 0.0513 and maximum is 0.286\n", "The Langmuir constant is: K = 21.5\n", "Amount Langmuir monolayer is: n = 0.00564 mol/g\n", "The slope of the Langmuir fit: s = 177\n", "The intercept of the Langmuir fit: i = 8.24\n", "\n" ] }, { "data": { "image/png": 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(isotherm.material)\n", "results = pgc.area_langmuir(isotherm, p_limits=(0.05, 0.3), verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fit is now better and the calculated area is also realistic. Comparing it to\n", "the BET area obtained previously, we see that it is higher by about 150 m2.\n", "Since adsorption is more likely to occur in monolayers until pore condensation,\n", "this method is not really suited for MCM-41. In general the Langmuir surface\n", "area is not as widely applicable as the BET one.\n", "\n", "Now let's do the analysis on all of the nitrogen samples and compare the\n", "obtained surface areas with the BET ones." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The correlation is not linear.\n", "The correlation is not linear.\n", "The correlation is not linear.\n" ] }, { "data": { "text/html": [ "
Text(0, 0.5, 'BET surface area [m2/g]')\n",
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dO2nXrp222IlIzHDOcfPNN/Pyyy9TpUoVevbs6Xekv6hzJyK+2LFjBy1btmTr1q0sWLBAHTsRiSn33nsvL7/8MnfffXdUdexAnTsR8cHevXtp06YNP/zwA7Nnz+acc87xO5KISMiefPJJnnjiCa6//nqGDh3qd5y/UedORArce++9x9KlS5k6dSrNmzf3O46ISMj++OMPRowYQffu3XnhhRei8u456tyJSIHr3r07Z511FvXq1fM7iohInlSsWJHPPvuM6tWrk5CQ4HecLOlsWREpEBkZGQwcOJD//ve/AOrYiUhMmTt3Lvfffz/OOWrXrk2xYsX8jpQtde5EJOKcc9x55508++yzLFy40O84IiJ58tFHH9GlSxfef/999u/f73ecXKlzJyIR9+ijjzJy5EgGDBjAkCFD/I4jIhKy5cuX065dO2rWrMncuXMpU6aM35Fypc6diETU888/z7///W969+7NyJEjo/LgYxGRrPzwww+0bNmScuXKkZKSwvHHH+93pJCocyciEeOc46OPPqJTp06MGTOGIkVUckQkdqxcuZLixYszf/58atWq5XeckOlsWRGJiIyMDIoUKcKkSZNIS0ujaFGVGxGJDZn1q3PnziQlJVG6dGm/I+WJVqNFJOwWLVrE2WefzaZNm0hISKBEiRJ+RxIRCcmuXbu44IILmDZtGkDMdewggp07MxtnZtvM7NugYW+Z2XLv8bOZLfeG1zaz1KDXXgqa5iwzW2Fma83sOdMBOyJR7YsvvqBDhw4cOHCAkiVL+h3nqKmGiRQ+qampdOjQgS+++IJSpUr5HeeoRXI/yXjgP8DEzAHOucszn5vZCGBX0Pg/OucaZTGfUUA/4FNgNtAKmBP+uCKSXytXrqRVq1ZUrlyZefPmUalSJb8j5cd4VMNECo1Dhw7RtWtXPvzwQyZPnkybNm38jnTUIrblzjm3BNiR1Wvemms3YHJO8zCzqkA559wnzjlHoMh2CnNUEQmDdevW0bJlS4oXL05KSgrVqlXzO1K+qIaJFB4ZGRn07t2b999/n5deeonLL78894mimF/H3F0IbHXOrQkadqKZfWVmH5jZhd6w6sDGoHE2esNEJMqULFmS+vXrk5KSQt26df2OE2mqYSJxxMyoVq0aw4YNo1+/fn7HyTe/Tl/rweFrvFuAWs657WZ2FjDdzE4Fsjo2xWU3UzPrR2D3R0ydsiwSy3bv3k3p0qWpWrUqCxcuLCzXsQt7DVP9EvHHjh07OPbYYxk+fHjc1K8C33JnZkWBLsBbmcOccwecc9u9518CPwL1Cazl1giavAawObt5O+dGO+eaOOeaVK5cORLxRSTI3r17SUpKomfPngBxUxhzEqkapvolUvCGDx/Oqaeeyvr16+OqfvmxW7YFsMo599euCjOrbGYJ3vM6QD3gJ+fcFmCPmTXzjnHpBczwIbOIHOHAgQN06dKFzz//nG7duvkdpyCphonEgVdeeYW77rqLf/7zn1SvHl9HS0TyUiiTgU+ABma20cyu8V7qzt8PQr4I+MbMvgamAjc45zIPZL4RGAOsJbA2rLPMRHyWlpbGFVdcQUpKCmPHjqVLly5+Rwo71TCR+PX2229z/fXX07p1ayZOnEhCQoLfkcLKAidwxZ8mTZq4pUuX+h1DJC7179+fF154gZEjRzJw4EC/4wBgZl8655r4nSMcVL9EIufDDz+kefPmnHPOOSQnJ0fNRYrDWcN0PyARybNevXpRq1atqOnYiYiEqnHjxtx444089NBDUdOxCzd17kQkZF988QVnn302TZs2pWnTpn7HEREJ2ffff0+NGjUoW7Yszz77rN9xIkr3lhWRkDz//PM0bdqUGTN0PoCIxJYffviBf/7zn/Tp08fvKAVCnTsRydVrr73GgAED6NSpE23btvU7johIyDZs2ECLFi0AePzxx31OUzC0W1ZEcjRjxgyuvvpqLr30UiZPnkzRoiobIhIbtm3bRmJiIrt27WLx4sU0aNDA70gFQlVaRLK1efNmunfvzllnncX06dMpWbKk35FERELWt29ffvnlF+bNm0fjxo39jlNg1LkTkWxVq1aN119/nYsvvpiyZcv6HUdEJE+ee+45fvrpJy688MLcR44jOuZORP7mu+++Y/HixQBcdtllVKpUyd9AIiIhOnToEGPGjCEjI4M6der8dbxdYaItdyJymHXr1pGYmEiJEiVYtWoVxYsX9zuSiEhI0tPT6d27N5MnT6Zu3bpccsklfkfyhTp3IvKXLVu2kJiYSGpqKsnJyerYiUjMcM7Rv39/Jk+ezBNPPFFoO3agzp2IeHbs2EFSUhK//vor8+fP57TTTvM7kohIyIYMGcJLL73E3Xffzd133+13HF+pcyciAIwaNYrVq1fz/vvv06xZM7/jiIiEbM2aNYwYMYJ+/foxdOhQv+P4Tp07EQFg8ODBtGnTplBdLkBE4kO9evX47LPP+Mc//oGZ+R3HdzpbVqQQS0tL4/bbb+eXX36hSJEi6tiJSEyZMmUKr7/+OgCNGjUiISHB50TRQZ07kULKOcf111/PyJEjSU5O9juOiEiezJ07lyuvvJJXXnmFjIwMv+NEFXXuRAoh5xx33nkn48aN44EHHqBfv35+RxIRCdnHH39Mly5dOPXUU5k5cyZFiqg7E0zvhkgh9Nhjj/H0009zyy238MADD/gdR0QkZMuXL6dt27bUrFmT5ORkypcv73ekqKPOnUghc+DAAaZPn06vXr145plndPCxiMSUefPmUa5cOVJSUjj++OP9jhOVdLasSCHinKNEiRIsWrSIUqVKaVeGiMQM5xxmxl133cV1111HxYoV/Y4UtVTZRQqJmTNn0rFjR/bv30/ZsmUpWlTrdiISG3777TcuuugivvzySwB17HKhzp1IIbBo0SK6devGr7/+Snp6ut9xRERCtmvXLlq1asXSpUvZt2+f33FiglbdReLc0qVL6dChA3Xr1mXOnDmULVvW70giIiFJTU2lQ4cOfPPNN8yYMYOLLrrI70gxQZ07kTj23Xff0apVK4477jjmzZtHpUqV/I4kIhKSQ4cO0bVrVz788EMmTZpEmzZt/I4UM7RbViSOHTx4kBo1ajB//nyqV6/udxwRkZClpaVhZowaNYru3bv7HSemaMudSBzat28fZcqUoVGjRixbtkxnxYpIzHDOsX//fsqUKcOMGTNUv45CxN4xMxtnZtvM7NugYQ+a2SYzW+492gS9NtjM1prZajNLChp+lpmt8F57znRRLpEc/fHHH5x77rk8+OCDACqMR0k1TMQf9913HxdccAG7d+9W/TpKkXzXxgOtshg+0jnXyHvMBjCzhkB34FRvmhfNLPPuv6OAfkA975HVPEWEwBa7tm3bsnr1ai644AK/48S68aiGiRSop556iscff5ymTZvq5K98iFjnzjm3BNgR4ugdgTedcwecc+uAtUBTM6sKlHPOfeKcc8BEoFNEAovEuAMHDtC5c2c+++wzJk+eTIsWLfyOFNNUw0QK1pgxYxg0aBCXX345L774ou6ekw9+bO/sb2bfeLs8Mq9CWB3YEDTORm9Yde/5kcNFJIhzjquuuoqUlBTGjh1Lly5d/I4Uz1TDRMJs+vTp9OvXj9atWzNx4kQSEhJyn0iyVdCdu1FAXaARsAUY4Q3PqnvuchieJTPrZ2ZLzWzpb7/9ls+oIrHDzOjSpQvPPPMMffr08TtOPItYDVP9ksLszDPPpFevXkydOpXixYv7HSfmFWjnzjm31TmX7pzLAF4BmnovbQRqBo1aA9jsDa+RxfDs5j/aOdfEOdekcuXK4Q0vEoWcc6xatQqA7t27c+utt/qcKL5Fsoapfklh9MMPP5Cenk6tWrUYP348pUuX9jtSXCjQzp13/EmmzkDmWWgzge5mVsLMTiRw0PHnzrktwB4za+adYdYLmFGQmUWi2WOPPcbpp5/+1/0WJbJUw0TCZ/ny5TRt2pR7773X7yhxJ2LXuTOzycDFwHFmthF4ALjYzBoR2C3xM3A9gHNupZm9DXwHpAE3O+cyb4B5I4Gz1koBc7yHSKH3wgsvcP/993PVVVfRuHFjv+PEHdUwkchZs2YNSUlJlC1blptvvtnvOHHHAidwxZ8mTZq4pUuX+h1DJCLeeOMNevbsSYcOHZg6dSrFihXzO5LvzOxL51wTv3OEg+qXxLONGzdy/vnns3//fj788ENOPvlkvyNFhXDWMN2hQiTGLF++nN69e3PJJZfw1ltvqWMnIjEjIyODjh078scff7B48WJ17CJEnTuRGHPGGWcwcuRI+vTpQ8mSJf2OIyISsiJFivDMM8/gnOPMM8/0O07c0n09RGLEl19+yZo1azAzbrnlFl29XURiRmpqKjNnzgTgwgsv5KKLLvI5UXxT504kBnz33XckJSXRu3dv4vU4WRGJT4cOHaJr16507tyZ1atX+x2nUMh2t6yZzQxh+h3OuT7hiyMiR/r5559p2bIlxYoV47XXXtMteUKkGibiv4yMDPr06cP777/PSy+9RIMGDfyOVCjkdMzdKcC1ObxuwAvhjSMiwX799VdatGjBvn37WLJkCXXr1vU7UixRDRPxkXOO/v37M2nSJIYOHcr111/vd6RCI6fO3RDn3Ac5TWxmD4U5j4gEue+++9iyZQvz58/nH//4h99xYo1qmIiPlixZwqhRoxg0aBB3332333EKFV3nTiSK7du3j2+//ZZzzjnH7yhRT9e5E4k+ixYt4uKLL9bhJCEo0Ovcmdks/n6j613AUuBl59yf4QgiIgEHDhzgkUce4e6776Zs2bLq2OWTaphIwZowYQL169fn3HPP5ZJLLvE7TqEUytmyPwF7Cdwk+xVgN7AVqO/9LiJhkp6eTs+ePXnsscdYsGCB33HihWqYSAGZOnUqffv2ZcSIEX5HKdRCuYhxY+dc8AVpZpnZEufcRWa2MlLBRAob5xzXX389U6dO5emnn6ZTp05+R4oXqmEiBSA5OZkrrriCc889l4kTJ/odp1ALZctdZTOrlfmL9/w479eDEUklUsg45xg0aBBjx47l/vvv57bbbvM7UjxRDROJsP/+97906dKFhg0b8t5771G6dGm/IxVqoWy5uwP4yMx+JHDpgBOBm8ysDDAhkuFECovff/+dSZMmccstt/DQQzqBM8xUw0QibOzYsVSvXp3k5GQqVKjgd5xCL9uzZc2sqnNui/e8BHAygcK4KhYOQNbZZhJrtmzZQpUqVShSRDeOORpHnmkWyzVM9UtiTVpaGtu3b6dKlSp+R4lZ4TxbNqf/IuPM7FMzewI4F1jpnFse7UVRJJa88cYb3H777WRkZFC1alV17MJLNUwkgjZu3Ei7du3YvHkzRYsWVccuimT7n8Q51xq4GFgMdAY+NbN3zKxf8PErInJ0Zs2aRe/evfnqq684dOiQ33HijmqYSOT8/vvvtGzZkiVLlrB161a/48gRcjzmzlvDnes9MLMTgdbAf8zsBOdc08hHFIk/ixcvpmvXrjRu3JiZM2dSokQJvyPFJdUwkfDbvXs3rVq1Yt26dSQnJ9O4cWO/I8kRQjmh4i/OuXXAi8CLZlY8MpFE4tvSpUvp0KEDdevWZc6cOZQtW9bvSIWGaphI/qSmptKhQwe+/vprpk+fzkUXXZT7RFLgst0ta2Y1zexNM/vQzO41s2JBr013zukSAiJHYfPmzVSrVo158+Zx3HHH5T6BHBXVMJHw2717N3/88QcTJ06kbdu2fseRbOR4QgWBY1VuAaoCH5hZJe+1/4twLpG4c/BgoC/RoUMHVqxYQfXq1X1OFPdUw0TCJCMjg7S0NKpUqcLSpUvp0aOH35EkBzl17io7517yzi67hcCujCVmVpe/36dRRHLw66+/csYZZzB58mQAihUrlssUEgaqYSJh4JzjlltuoVu3bqSlpal+xYCcOnfFzKxk5i/OudeBW4FkAmvBIhKCP/74g6SkJNavX0/t2rX9jlOYqIaJhMH999/Piy++yEknnURCQoLfcSQEOZ1QMQY4B/ggc4Bzbr6ZdQWejHQwkXiwb98+2rVrx6pVq3jvvfc499xz/zbO9K82MTx5NZt3plKtQikGJTWgU2Ptsg0D1TCRfBoxYgSPPfYY1113HcOGDcPMDntd9Ss6Zdu5c86NzGb4V0BixBKJxIlDhw5x2WWX8emnnzJlyhQSE//+tZn+1SYGv7OC1EPpAGzamcrgd1YAqEDmk2qYSP6MHz+eO++8k27dujFq1KgsO3aqX9Ep10uheNeFugWoHTy+c65D5GKJxL6iRYvStGlTunXrRpcuXbIcZ3jy6r8KY6bUQ+kMT16t4hgmqmEiR+fkk0+mR48ejB8/Psvdsapf0SuU69xNB8YCs4CMUGdsZuOAdsA259xp3rDhQHvgIPAjcLVzbqeZ1Qa+B1Z7k3/qnLvBm+YsYDxQCpgN3OqyuyGuSBRwzrF582aqV6/Oww8/nOO4m3em5mm4HJXpqIaJhGzjxo3UqFGDZs2a0axZs2zHU/2KXqHcyPJP59xzzrlFzrkPMh8hTDceaHXEsBTgNOfc6cAPwOCg1350zjXyHjcEDR8F9APqeY8j5ykSNZxzDBo0iDPOOIONGzfmOn61CqXyNFyOimqYSIj++9//0qBBA8aMGZPruKpf0SuUzt2zZvaAmZ1rZmdmPnKbyDm3BNhxxLB5zrk079dPgRo5zcPMqgLlnHOfeGu6E4FOIWQW8cXQoUMZMWIEPXr0COk6doOSGlCq2OG7O0oVS2BQUoNIRSyMVMNEQvDNN9/Qtm1bqlevTvv27XMdX/UreoWyW/YfwFXApfxvl4bzfs+PvsBbQb+faGZfAbuB+5xzHwLVgeDNHxu9YSJRZ9SoUQwZMoSePXvy7LPP/u3g46xkHpeis80iSjVMJBdr1qyhZcuWHHPMMaSkpFClSpVcp1H9il6hdO46A3XCeaseMxsCpAFveIO2ALWcc9u941Omm9mpQFb/HbM9VsXM+hHY/UGtWrXCFVckV8nJydx88820b9+ecePGUaRIKBvFAzo1rq5iGFkxUcNUv8Qv+/fvp2XLlqSnp7N48WL+7/9Cv4GL6ld0CuU/0NdAhXA1aGa9CRykfGXmQcXOuQPOue3e8y8JHKhcn8BabvBujxrA5uzm7Zwb7Zxr4pxrUrly5XBFFsnVRRddxP3338/bb7+tq7dHn5ioYapf4pfSpUvz4IMPMnfuXE4++WS/40gYhLLlrgqwysy+AA5kDjyaywiYWSvgbuCfzrn9QcMrAzucc+lmVofAQcc/Oed2mNkeM2sGfAb0Ap7Pa7sikfLFF19Qr149KlSowEMPPeR3HMmaaphIFnbv3s13331Hs2bN6N27t99xJIxC6dw9cDQzNrPJwMXAcWa20ZvPYKAEkOIdj5R5uYCLgIfNLA1IB25wzmUeyHwj/7uMwBzvIeK7L7/8kubNm9OmTRvefPNNv+NI9lTDRI6QmppKhw4dWLZsGevWraNSpUp+R5Iwsni93FKTJk3c0qVL/Y4hcWrVqlVceOGFlClTho8++ogaNXI8aVIKgJl96Zxr4neOcFD9kkjKvHvOe++9x+uvv84VV1zhdyQhvDUs22PuzOy9EILkOo5IvPnll19ITEwkISGB+fPnq2MXpVTDRP4uIyODvn37MmvWLF544QV17OJUTrtlLzCzmTm8bkDDMOcRiXrXXnste/fu5YMPPuCkk07yO45kTzVM5Aivv/46r7/+Oo899hg33nij33EkQnLq3HUMYfqwXVpAJFaMGzeOLVu2cPrpp/sdRXKmGiZyhJ49e1KuXDk6dgzl6yGxKtvOXYi35xEpFPbt28dLL73EwIEDqVmzJjVr1vQ7kuRCNUzkf8aNG0eLFi2oVasWnTp18juORFjoV1oVKaQOHjzIZZddxl133cXnn3/udxwRkTwZN24c11xzDcOHD/c7ihSQUC6FIlJopaen07NnT5KTkxk7diznnnuu35FEREI2depUrrvuOpKSkhgxYoTfcaSAaMudSDacc1x//fVMmTKFESNG0LdvX78jiYiEbN68eVxxxRU0a9aMadOmUbx4cb8jSQHJdcudmdUDhhI4q6xk5nDnXJ0I5hLx3apVq3jjjTcYMmQIt99+u99x5Ciphklh5Jzj0Ucf5ZRTTuG9996jTJkyfkeSAhTKbtlXCVyZfSRwCXA1Wd8MWySunHLKKXzzzTe63EnsUw2TQsfMmDVrFn/++ScVK1b0O44UsFB2y5Zyzi0gcDeLX5xzDwKXRjaWiH9eeuklXnnlFQDq1auHd5spiV2qYVJorF27lj59+rB//37Kly9PlSpV/I4kPgilc/enmRUB1phZfzPrDBwf4Vwivpg8eTI33XQTs2bNIl5vzVcIqYZJobBp0yZatGjBe++9x6ZNm/yOIz4KZbfsQKA0MAB4hMBujd4RzCTii/fff59evXpx0UUX8dZbb8XdFrvpX21iePJqNu9MpVqFUgxKakCnxtX9jlUQBqIaJnHu999/JzExkR07drBo0SLq1asXsbYKcS2JGbl27pxzXwCYmXPOXR35SCIFb8mSJfzrX/+iUaNGzJw5k1KlSvkdKaymf7WJwe+sIPVQOgCbdqYy+J0VAHFflFXDJN7t2bOH1q1bs27dOubOnctZZ50VsbYKcy2JJbnuljWzc83sO+B77/czzOzFiCcTKUBLly7lxBNPZM6cOZQrV87vOGE3PHn1X8U4U+qhdIYnr/YpUcFRDZN4t2HDBjZv3syUKVP45z//GdG2CnMtiSWhHHP3DJAEbAdwzn0NXBTBTCIFJiMjA4Dbb7+dpUuXctxxx/mcKDI270zN0/A48wyqYRKHMutXw4YNWbNmDe3atYt4m4W8lsSMkC5i7JzbcMSg9CxHFIkhv/zyC40aNeKTTz4BoHTp0j4nipxqFbLezZzd8HijGibxJiMjg969ezNkyBCg4OpXYa8lsSKUzt0GMzsPcGZW3MzuxNu9IRKrtm7dSosWLdiwYUOhuLjnoKQGlCqWcNiwUsUSGJTUwKdEBUo1TOKKc44BAwbw+uuvF3j9KuS1JGaEcrbsDcCzQHVgIzAPuDmSoUQiaefOnSQlJbF582ZSUlI4/fTT/Y4UcZkHOhfSM9xUwySuPPDAA7zwwgvccccdDB48uEDbLuS1JGbk2LkzswTgGefclQWURySi9u/fT7t27fjuu++YNWsW5513nt+RCkynxtULXQFWDZN4M3LkSB555BGuueYahg8f7sslmwpjLYk1Oe6Wdc6lA5XNTHcblriQkJBA9erVmTRpEklJSX7HkQhTDZN4U6VKFXr06MHLL78cd9filPAJZbfsz8DHZjYT2Jc50Dn3dKRCiYRbeno6e/bsoUKFCrz55psqioXLz6iGSYzbvn07lSpV4oorrqBHjx6qYZKjUE6o2Ay8541bNughEhOcc9xwww2cf/757N27V0Wx8FENk5iWkpJC7dq1mT9/PoBqmOQqlDtUPFQQQUQiwTnH3XffzZgxY7j33ns55phj/I4kBUw1TGLZJ598QqdOnTjppJMieucJiS+5du7MrDJwF3AqUDJzuHPu0gjmEgmLJ554guHDh3PTTTfx6KOP+h1HfKAaJrFqxYoVtGnThmrVqpGcnEzFihX9jiQxIpTdsm8Aq4ATgYcIHL/yRQQziYTFa6+9xr333ssVV1zB888/r10ZhZdqmMScX3/9lZYtW1KmTBlSUlI44YQT/I4kMSSUzl0l59xY4JBz7gPnXF+gWW4Tmdk4M9tmZt8GDTvWzFLMbI33s2LQa4PNbK2ZrTazpKDhZ5nZCu+150z/oSVELVq04LbbbmP8+PEUKRLSzVgkPqmGScypUqUK/fr1Y968edSuXdvvOBJjQvmPd8j7ucXM2ppZY6BGCNONB1odMeweYIFzrh6wwPsdM2sIdCew26QV8KJ3fSqAUUA/oJ73OHKeIodZtmwZaWlpVK1alaeffppixYr5HUn8pRomMeP333/np59+wsx46KGHaNiwod+RJAaF0rl71MzKA3cAdwJjgNtym8g5twTYccTgjsAE7/kEoFPQ8Dedcwecc+uAtUBTM6sKlHPOfeKcc8DEoGlE/mbJkiWcf/753H///X5HkeihGiYxYc+ePbRu3ZoWLVpw8OBBv+NIDAvlbNn3vKe7gEvy2V4V59wWb75bzOx4b3h14NOg8TZ6ww55z48cLvI3y5Yto3379tSuXZs77rjD7zgSJVTDJBb8+eefdOzYka+++op3332X4sV13W05etFyIFJWx6C4HIZnPROzfma21MyW/vbbb2ELJ9Fv1apVJCUlUbFiRVJSUjjuuOP8jiSFS75rmOpX4XXo0CEuv/xyFi9ezIQJE2jfvr3fkSTGFXTnbqu3mwLv5zZv+EagZtB4NQhceHQjhx8bkzk8S8650c65Js65JpUrVw5rcIle6enpdOnShSJFipCSkkKNGqEcTiVyVCJWw1S/Cq9hw4Yxc+ZMnn/+ea68UrdBlvwr6M7dTKC397w3MCNoeHczK2FmJxI46Phzb/fHHjNr5p1h1itoGhEgcL/YV199leTkZOrVq+d3HIlvqmESdgMHDuSNN97g5ptv9juKxIlcO3dmVsXMxprZHO/3hmZ2TQjTTQY+ARqY2UZvmieARDNbAyR6v+OcWwm8DXwHzAVu9m74DXAjgQOg1wI/AnPyuIwSp3bu3Mkbb7wBwDnnnEOjRo38DSRRSTVMotXEiRPZu3cvxxxzDFdccYXfcSSOWOAErhxGCBTEV4EhzrkzzKwo8JVz7h8FEfBoNWnSxC1dutTvGBIh+/fvp2XLlnz++eesWrWKOnXq+B1JfGZmXzrnmmQxPOZqmOpX/Bs5ciS33347jz/+OIMHD/Y7jkSB7GrY0Qhlt+xxzrm3gQwA51wakJ7zJCKRc/DgQS677DI++eQT3njjDXXsJDeqYRJVxo0bx+23385ll13GXXfd5XcciUO5XgoF2GdmlfDO8DKzZgQuKSBS4NLT0+nZsydz587llVdeoWvXrn5HkuinGiZRY9q0aVx33XW0bNmSN954g4SEhNwnEsmjUDp3txM4WLiumX0MVAb+FdFUItlYuHAhU6ZMYfjw4Vx77bV+x5HYoBomUeHAgQPccccdnHPOObzzzjuUKFHC70gSp0K5iPEyM/sn0IDANZtWO+cO5TKZSEQkJiaydOlSzjrrLL+jSIxQDZNoUaJECRYuXEjFihUpU6aM33EkjoVytuzNwDHOuZXOuW+BY8zspshHE/mfkSNHsnjxYgB17CRPVMPEbytWrOD+++/HOUedOnWoWLGi35EkzoVyQsV1zrmdmb845/4ArotYIpEjvPTSS9x+++1MnDjR7ygSm1TDxDdr166lZcuWvPrqq2zbti33CUTCIJRj7oqYmXk3vcbMEgDd9E4KxOTJk7npppto27YtL7/8st9xJDaphokvNm3aRGJiIocOHWLBggVUqVLF70hSSITSuUsG3jazlwicbXYDgYt0ikTU7Nmz6dWrFxdeeCFTpkyhWLFifkeS2KQaJgVu+/bttGzZkt9//52FCxfSsGFDvyNJIRJK5+5u4HoCV1k3YB6Bq62LRNS7777L6aefzqxZsyhVqpTfcSR2qYZJgVu2bBkbN25k1qxZnH322X7HkUIm1ztUxCpd4T12OecwMzIyMtizZw/ly5f3O5LEgHBe3d1vql+xK7N+Afzxxx86eUJCVqB3qDCzemY21cy+M7OfMh/haFzkSKtXr+bCCy/kl19+oUiRIurYSb6phklBSUtLo2vXrn/d81odO/FLKGfLvgqMAtKAS4CJwGuRDCWF0/r160lMTGTNmjUcPHjQ7zgSP1TDJOIyMjLo27cv06ZN448//vA7jhRyoXTuSjnnFhDYhfuLc+5B4NLIxpLCZtu2bSQmJrJ7927mzZtHvXr1/I4k8UM1TCLKOcfAgQN57bXXeOSRR+jfv7/fkaSQC+WEij/NrAiwxsz6A5uA4yMbSwqTnTt3kpSUxIYNG0hJSeGMM87wO5LEF9UwiagHH3yQ559/nttvv50hQ4b4HUckpC13A4HSwADgLKAn0DuCmaSQOXToECVLluTdd9/l/PPP9zuOxJ+BqIZJhDjnSE9Pp2/fvjz11FN/nUwh4qdst9yZ2WvOuauA85xzXwB7gasLLJnEvczj6ipXrszHH39MkSKhrGuIhEY1TCJt3759lClThkcffZSMjAx17CRq5PTf9Cwz+z+gr5lVNLNjgx8FFVDiU3p6OldddRWdO3cmPT1dHTuJBNUwiZh33nmHk046ie+++w5ANUyiSk7H3L1E4CrudYAvCVz8M5PzhovkmXOOG2+8kbfffpsnn3yShIQEvyNJfFINk4iYP38+PXr04Mwzz6RWrVp+xxH5m2xXNZxzzznnTgHGOefqOOdODHqoKMpRGzx4MK+88gqDBw9m0KBBfseROKUaJpHw6aef0qlTJxo0aMDs2bM55phj/I4k8jc5bkf2zjC7sICySCEwcuRIhg0bxo033shjjz3mdxyJc6phEk6rV6+mTZs2nHDCCcybN08XKZaolWPnzjmXAXxtZtruLGFx8cUX079/f/7zn//o4GOJONUwCaeaNWvSpUsX5s+fzwknnOB3HJFshXKdu6rASjP7HNiXOdA51yFiqSTurFq1ipNPPpnGjRvz/PPP+x1HChfVMMmXLVu2ULp0acqXL8+YMWP8jiOSq1A6dw9FPIXEtdmzZ9OxY0dGjRrFtdde63ccKXxUw+Sobd++nRYtWlC5cmUWLVqkPQ4SE3Lt3DnnPiiIIBKfPvzwQy677DJOP/10unXr5nccKYRUw+Ro7dmzh9atW/Pjjz/ywgsvqGMnMSPXzp2Z7SFw2QCA4kAxYJ9zrlwkg0nsW7ZsGe3ataN27drMnTuXcuX0JyMFTzVMjsaff/5Jx44dWbZsGe+++y4XX3yx35FEQhbKlruywb+bWSeg6dE2aGYNgLeCBtUB/g1UAK4DfvOG3+ucm+1NMxi4BkgHBjjnko+2fSkYe/bsoU2bNlSoUIF58+ZRuXJlvyNJIaUaJkdj4MCBLFq0iNdee4327dv7HUckT8w5l/tYR05k9qlzrlm+GzdLIHAT73MI3BZor3PuqSPGaQhMJlCMqwHzgfrOufSc5t2kSRO3dOnS/EaUfJgyZQqNGjWiXr16fkeRQsDMvnTONQlx3KiuYapf/lu/fj1LliyhZ8+efkeRQiIvNSw3oeyW7RL0axGgCf/bxZFfzYEfnXO/5HAsQ0fgTefcAWCdma0lUCQ/CVMGCaNt27axcuVKLrnkErp27ep3HBHVMAmZc45p06bRpUsXatWqpY6dxKxQbobXPuiRBOwhUKzCoTuBNdpM/c3sGzMbZ2aZV4esDmwIGmejN0yizM6dO0lKSqJLly7s3LnT7zgimVTDJCQPPvggXbt2ZfLkybmPLBLFQjnm7upINGxmxYEOwGBv0CjgEQJr1I8AI4C+HH4/yL9iZTPPfkA/QPf7K2D79++nffv2rFy5kpkzZ1KhQgW/I4kAsVPDVL/89cwzz/Dwww9z9dVXc8UVV/gdRyRfct1yZ2ZPmlk5MytmZgvM7HczC8e26tbAMufcVgDn3FbnXLp3RflX+N8BzxuBmkHT1QA2ZzVD59xo51wT51wTHcBfcA4ePMhll13Gxx9/zOuvv06rVq38jiTyl1ipYapf/nn11Ve57bbb6NKlC6NHj9YlTyTmhbJbtqVzbjfQjkCRqg+E427vPQjanWFmVYNe6wx86z2fCXQ3sxJmdiJQD/g8DO1LmEycOJG5c+fy8ssv61p2Eo1UwyRbv//+OwMGDCAxMZFJkyZRtGgo1/YXiW6h/BUX8362ASY753bkd63GzEoDicD1QYOfNLNGBHZX/Jz5mnNupZm9DXwHpAE353amrBSsa665hpNOOknXgZJopRom2TruuONYsGABDRs2pESJEn7HEQmLUDp3s8xsFZAK3GRmlYE/89Ooc24/UOmIYVflMP5jwGP5aVPC76mnnqJTp04x27Gb/tUmhievZvPOVKpVKMWgpAZ0aqzj3OOQapj8zWeffcb3339Pnz59aNo075c9VP2QaJbrblnn3D3AuUAT59whYD/hO9NMYtSwYcMYNGgQ48aN8zvKUZn+1SYGv7OCTTtTccCmnakMfmcF07/a5Hc0CTPVMDnSihUraN26NY899hipqal5nl71Q6JdKMfc4Zz7I3M3gnNun3Pu18jGkmj28ssvc88999CjRw8effRRv+McleHJq0k9dPiesdRD6QxPXu1TIokk1TDJ9OOPP9KyZUtKlSpFSkoKpUqVyvM8VD8k2unIUcmTN998kxtvvJG2bdsyYcIEihQJaf0g6mzemfXaenbDRST2bd68mcTERA4dOsSSJUuoXbv20c1H9UOiXLb/mc1MF1qSw2RkZDBq1CguvPBCpkyZQrFixXKfKEpVq5D12np2wyX2qIbJkd577z1+//135syZQ8OGDY96PqofEu1y2uwyvaBCSGwoUqQIs2fPZtasWUe1KyOaDEpqQKliCYcNK1UsgUFJDXxKJBEw3e8AEl369evH6tWrOfvss/M1H9UPiXY5de50FUcB4KuvvuKyyy5jz549lClThnLlyvkdKd86Na7O0C7/oHqFUhhQvUIphnb5h852iy+qYcKff/5J165d+fTTTwGoWrVqLlPkTvVDol1Ox9xVN7PnsnvROTcgAnkkyqxevZqkpCRKlSrFrl27KFu2rN+RwqZT4+oqxvFNNayQS0tLo0ePHkyfPp2OHTvSrFmzsM1b9UOiWU6du1Tgy4IKItFn/fr1JCYmYmakpKRQo0YNvyOJ5IVqWCGWkZHBNddcw/Tp03n++efp2TMcd5wTiQ05de62O+cmFFgSiSrbtm0jMTGR3bt3s3jxYurXr+93JJG8Ug0rpJxz3HbbbUycOJGHH36Y/v37+x1JpEDldMzdwQJLIVFn+/btZGRk8P7779OoUSO/44gcDdWwQio9PZ1NmzZx2223cd999/kdR6TA5bTl7j+ZT8zsfOfcx0G/93fO/SfrySSWHTx4kGLFinHKKafw/fff6ybaEstUwwqhgwcPUrx4cd566y3MjPzeR1gkFuW05e72oOfPH/Fa3whkEZ8dPHiQzp07M2jQIAB17CTWqYYVMhMmTKBJkyZs27aNhISEmL3Iukh+hXoplCNXfbQqFGfS09Pp1asXs2fPpkEDXatJ4oJqWCHy7rvv0rdvX6pUqUL58uX9jiPiq5w6dy6b51n9LjHMOcdNN93EW2+9xbBhw7juuuv8jiQSDqphhcT8+fPp3r07TZs25d1336VEiRJ+RxLxVU773U42s28IrOHW9Z7j/V4n4smkwAwZMoTRo0dzzz33cNddd/kdRyRcVMMKgS+++IJOnTpRv3593n//fY455hi/I4n4LqfO3SkFlkJ8dfbZZzNgwAAef/xxv6OIhJNqWCFQvXp1WrRowahRozj22GP9jiMSFcy50PdOmNlxBK4dFfW7NJo0aeKWLl3qd4yotnHjRl2YWOKGmX3pnGuSyzgxUcNUv3K3ZcsWKleurBO/JG6EUsNCle0xd2bWzMwWm9k7ZtbYzL4FvgW2mlmrcDQu/nnrrbeoW7cu8+fP9zuKSESohsWvzZs3c/7553PDDTf4HUUkKuV2nbt7gfLAQqC1c+5TMzsZmAzMLYB8EgFz5syhZ8+enHvuuZx33nl+xxGJFNWwOLR9+3ZatmzJb7/9Rr9+/fyOIxKVcjpbtqhzbp5zbgrwq3PuUwDn3KqCiSaR8OGHH3LZZZdx+umnM2vWLEqXLu13JJFIUQ2LM3v27KFNmzasXbuWGTNm0LRpU78jiUSlnLbcZQQ9Tz3itag+XkWytn79etq1a0etWrWYO3eurgUl8U41LM5cddVVfPnll0ybNo1LL73U7zgiUSunzt0ZZrabwGUDSnnP8X4vGfFkEnY1a9bk/vvv5/LLL6dy5cp+xxGJNNWwOHPPPffQrVs3Onbs6HcUkaiWbefOOZdQkEEkctavX8/+/fs5+eSTufPOO/2OI1IgVMPiQ0ZGBgsWLCAxMZFmzZrRrFkzvyOJRD3deC/Obdu2jcTERNq3b09aWprfcUREQuac47bbbqNly5Z8+OGHfscRiRm6QFAc27VrF61atWLDhg2kpKToelAiElMeeughnnvuOW677TYuuOACv+OIxAxfttyZ2c9mtsLMlpvZUm/YsWaWYmZrvJ8Vg8YfbGZrzWy1mSX5kTnW7N+/n3bt2vHtt9/yzjvvcP755/sdSSRuqIZF3rPPPstDDz3E1VdfzYgRIzAzvyOJxAw/d8te4pxrFHQ15nuABc65esAC73fMrCHQHTgVaAW8aGY6liYXjz/+OB9//DGvvfYarVrpeq0iEaAaFiHff/89t912G126dGH06NHq2InkUTQdc9cRmOA9nwB0Chr+pnPugHNuHbAW0MWNcjFkyBDef/99Lr/8cr+jiBQWqmFhcsoppzBr1iwmTZqkw0lEjoJfnTsHzDOzL80s8xLjVZxzWwC8n8d7w6sDG4Km3egNkyM45xg5ciQ7d+6kVKlStG7d2u9IIvFKNSwCFi5cyAcffABA27ZtKVGihM+JRGKTX6tE5zvnNpvZ8UCKmeV0xfistsdneQFSr8j2A6hVq1b+U8aYe++9lyeeeIKEhAQGDBjgdxyReBb2GlbY69dnn31Ghw4daNiwIZ999pl2xYrkgy9b7pxzm72f24B3Ceyi2GpmVQG8n9u80TcCNYMmrwFszma+o51zTZxzTQrbRXqffPJJnnjiCW644QZuueUWv+OIxLVI1LDCXL++/fZbWrduzQknnMCMGTPUsRPJpwLv3JlZGTMrm/kcaAl8C8wEenuj9QZmeM9nAt3NrISZnQjUAz4v2NTRbfTo0dx99910796d//znPyqMIhGkGhZeP/30Ey1btqRUqVKkpKRQtWpVvyOJxDw/dstWAd71OiBFgUnOublm9gXwtpldA6wHugI451aa2dvAd0AacLNzLt2H3FEpNTWVoUOH0qZNGyZOnEhCgk7CE4kw1bAwGjVqFAcOHGDJkiWceOKJfscRiQvmXHzeP7tJkyZu6dKlfscoEJs2baJixYqULl3a7ygivjGzL4MuSxLTClP9ysjI4Oeff6ZOnTp+RxHxVThrWDRdCkXy4KOPPmLgwIGkp6dTvXp1dexEJGbs3buXnj17sn79eooUKaKOnUiYqXMXg7766ivatm3LnDlz2L17t99xRERC9ueff9KpUyfefPNNVqxY4Xcckbikq0PGmNWrV5OUlET58uVJSUmhYsWKuU8kIhIF0tLS6NGjBwsWLGDChAm0bdvW70gicUlb7mLIhg0bSExMBGD+/PmF8lpYIhKbMjIyuPbaa5k+fTrPPfccvXr18juSSNxS5y6G/PDDDxw6dIjk5GTq16/vdxwRkZDt2bOHb775hoceekjX4hSJMO2WjQEZGRkUKVKE5s2b89NPP1GqVCm/I4mIhCwjI4Py5cvz8ccfU7JkSb/jiMQ9bbmLcvv376d58+aMHTsWQB07EYkpzz77LO3btyc1NZVSpUrpIusiBUCduyh28OBBunbtygcffMAxxxzjdxwRkTyZMGECAwcOpGTJkhQrVszvOCKFhnbLRqn09HR69+7N7NmzGT16NJdffrnfkUREQjZ9+nSuueYaWrRowaRJkyhaVP9uRAqKttxFIecc/fv3580332TYsGFcd911fkcSEQnZwoULufzyyzn77LN59913KVGihN+RRAoVde6iVK1atbjnnnu46667/I4iIpInFSpU4IILLuD999/XISUiPtB28iizfft2KlWqxODBg/2OIj6a/tUmhievZvPOVKpVKMWgpAZ0alzd71giOcqsX2eeeSYLFiw46vno718kf7TlLoqMHj2aevXq8d133/kdRXw0/atNDH5nBZt2puKATTtTGfzOCqZ/tcnvaCLZ+umnn/jHP/7Bk08+ma/56O9fJP/UuYsSb731FjfccAPNmjXjpJNO8juO+Gh48mpSD6UfNiz1UDrDk1f7lEgkZ5s3b6ZFixYcOHAg37cU09+/SP5pt2wUmDt3LldddRUXXHABU6dOpXjx4n5HEh9t3pmap+EiftqxYwdJSUn89ttvLFiwgFNPPTVf89Pfv0j+acudz5YvX06XLl047bTTmDVrFqVLl/Y7kvisWoWsL1Sd3XARv6Snp9OuXTvWrFnDjBkzaNq0ab7nqb9/kfxT585np5xyCjfccANz586lfPnyfseRKDAoqQGliiUcNqxUsQQGJTXwKZFI1hISErj55pt58803ufTSS8MyT/39i+Sfdsv6ZO3atVSsWJFKlSrx9NNP+x1HokjmWYE6W1CiVVpaGl9//TVnnXUWV155ZVjnrb9/kfxT584HGzZsoHnz5tSpU4eFCxfqXovyN50aV9c/M4lKGRkZXHfddUyaNInvvvuOunXrhr0N/f2L5I92yxaw3377jcTERHbu3MnTTz+tjp2IxAznHHfccQfjx4/n3nvvjUjHTkTyT1vuCtCuXbto1aoVv/zyC/PmzaNx48Z+RxIRCdkjjzzCM888w6233sq///1vv+OISDbUuStAAwYM4JtvvmHGjBlceOGFfscREQnZ/PnzeeCBB+jTp4/2OohEOXXuCtATTzxB165dadOmjd9RRETypHnz5rz66qv07NmTIkV0RI9INNM3NMLS09MZPXo0aWlpVK1alXbt2vkdSUQkZHPmzOGnn37CzOjTpw9Fi2qbgEi0U+cugpxz9O/fn+uvv56ZM2f6HUdEJE8WLlxIp06duOOOO/yOIiJ5UOCdOzOraWaLzOx7M1tpZrd6wx80s01mttx7tAmaZrCZrTWz1WaWVNCZj9aQIUN46aWXuPvuu+nSpYvfcUQkDApLDfv888/p0KED9evXZ+zYsX7HEZE88GP7ehpwh3NumZmVBb40sxTvtZHOuaeCRzazhkB34FSgGjDfzOo75w6/s3SUGT58OEOHDqVfv34MHTrU7zgiEj5xX8O+/fZbWrduTZUqVZg3bx7HHnus35FEJA8KfMudc26Lc26Z93wP8D2Q09UqOwJvOucOOOfWAWuB/N/AMIK2bNnCww8/zOWXX86LL76os8pE4khhqGH//ve/KVGiBCkpKVStWtXvOCKSR74ec2dmtYHGwGfeoP5m9o2ZjTOzit6w6sCGoMk2knMh9V3VqlX55JNPmDhxIgkJCblPICIxKV5r2MSJE1m8eDF16tTxO4qIHAXfOndmdgwwDRjonNsNjALqAo2ALcCIzFGzmNxlM89+ZrbUzJb+9ttv4Q+di+TkZF544QUATjvtNIoXL17gGUSkYIS7hvldv3bs2MHNN9/M3r17OeaYY6hfv36BZxCR8PClc2dmxQgUxTecc+8AOOe2OufSnXMZwCv8b7fFRqBm0OQ1gM1Zzdc5N9o518Q516Ry5cqRW4AsfPzxx3Tu3JkxY8Zw4MCBAm1bRApWJGqYn/Vr7969tGnThjFjxvDNN98UaNsiEn4FfkKFBQ5AGwt875x7Omh4VefcFu/XzsC33vOZwCQze5rAwcj1gM8LMHKuli9fTtu2balZsybJycmUKFHC70hRZfpXmxievJrNO1OpVqEUg5Ia6KbgErPirYYdOHCATp06sXTpUqZOncp5552X7bj6LovEBj/Olj0fuApYYWbLvWH3Aj3MrBGB3RU/A9cDOOdWmtnbwHcEzlK7OZrOMvvhhx9ISkqiXLlypKSkcPzxx/sdKapM/2oTg99ZQeqhwEe2aWcqg99ZAaB/ChKr4qaGpaWlccUVV7BgwQImTJhAp06dsh1X32WR2FHgnTvn3EdkfQzK7BymeQx4LGKh8mHJkiUApKSkUKtWLZ/TRJ/hyav/+meQKfVQOsOTV+sfgsSkeKphmzZt4r///S/PPvssvXr1ynFcfZdFYofuI3OUnHOYGddeey3/+te/qFChgt+RotLmnal5Gi4ikedc4HyO//u//+P7778PqX7puywSO3T7saOwa9cuLr30Uj744AMAdexyUK1CqTwNF5HIe+SRR7jzzjtxzoVcv/RdFokd6tzlUWpqKh06dOCjjz5i3759fseJeoOSGlCq2OHX+itVLIFBSQ18SiRSuD333HM88MADbN++/a8teKHQd1kkdmi3bB4cOnSIrl278uGHHzJp0iTatGmT+0SFXOaxODrDTsR/r732GrfeeiudOnVizJgxFCkS+vq9vssisUOduxBlZGTQp08f3n//fV566SW6d+/ud6SY0alxdf0DEPHZjBkzuPrqq7n00kuZPHkyRYvmvfzruywSG7RbNkTp6YGzxIYOHcr111/vcxoRkbxJT0/nvPPOY/r06ZQsWdLvOCISQdpyF4J9+/ZRpkwZXn/9dQLXLxURiQ2Z9atLly507txZNUykENCWu1w89dRTNG7cmG3btqkoikhMWblyJXXr1uWdd94BUA0TKSTUucvBmDFjGDRoEI0bN6ZSpUp+xxERCdm6deto2bIlZsYZZ5zhdxwRKUDaLZuNKVOm0K9fP1q1asVrr71GQkJC7hOJiESBLVu20KJFC1JTU1myZAl169b1O5KIFCB17rKwaNEirrzySs477zymTZtG8eLF/Y4kIhKSvXv30rJlS7Zu3cqCBQs47bTT/I4kIgVMu2WzcOqpp9KjRw/ee+89Spcu7XccEZGQZZ48MWPGDM455xy/44iID7TlLsiPP/5IzZo1Of7445kwYYLfcUREQnbgwAE2bdpEnTp1eOihh/yOIyI+0pY7z5o1azjvvPO46aab/I4iIpInaWlpXHHFFZx77rns3LnT7zgi4jNtuQM2btxIixYtyMjI4M477/Q7johIyDIyMujXrx/vvPMOzzzzDBUqVPA7koj4rNB37n777TcSExPZuXMnixYt4uSTT/Y7kohISJxz3Hnnnbz66qs88MAD3HrrrX5HEpEoUOg7d927d+fnn38mOTmZM8880+84IiIhmzBhAiNHjmTAgAE88MADfscRkShR6Dt3Tz31FL/++isXXXSR31FERPKkW7du7Nq1i1tuuUV3nxCRvxTKEyoOHTrEtGnTAGjcuDGtW7f2OZGISOjmzJnDrl27KF26NLfeeitFihTKUi4i2Sh0FSEjI4M+ffrwr3/9i88//9zvOCIieTJjxgzat2/Pfffd53cUEYlShapz55yjf//+TJo0iaFDh9K0aVO/I4mIhGzRokVcfvnlnHXWWTz++ON+xxGRKFWoOnf33Xcfo0aN4q677uKee+7xO46ISMg+//xzOnTowEknncTs2bMpW7as35FEJEoVms7d119/zdChQ7nuuut44okn/I4jIhIy5xzXX389lStXZt68eVSqVMnvSCISxQrN2bJnnHEGixcv5vzzz9dZZSISU8yMGTNmcOjQIapVq+Z3HBGJcnG/5W7atGnMmzcPgIsuuoiEhASfE4mIhGbLli089NBDZGRkUKtWLerWret3JBGJATHTuTOzVma22szWmllIB8wlJyfTo0cPhg0bhnMu0hFFRLJ0NPVrx44dJCUlMXz4cNasWRPpiCISR2Kic2dmCcALQGugIdDDzBrmNM3evXvp0qULp556KtOmTdOuWBHxxdHUr4yMDNq2bcvq1auZMWMGDRo0KIioIhInYuWYu6bAWufcTwBm9ibQEfguuwnWrl1LnTp1mDt3rm6kLSJ+Oqr6tW/fPqZOnUrz5s0LKKaIxIuY2HIHVAc2BP2+0RuWrSJFipCSkkKVKlUiGkxEJBd5rl979+5l7NixdO7cOaLBRCQ+xcqWu6z2qf7tIDoz6wf08349ULt27W8jmip7xwG/+9S22ve3/cK87H63H637Lo+qfl199dXfXn311RENlgN9h9R+YWs7GtoPWw2Llc7dRqBm0O81gM1HjuScGw2MBjCzpc65JgUT73B+tq329dkX1vbNbKkf7YYgpuqX3+0X5mUv7O0X5mXPbD9c84qV3bJfAPXM7EQzKw50B2b6nElEJBSqXyJSoGJiy51zLs3M+gPJQAIwzjm30udYIiK5Uv0SkYIWE507AOfcbGB2HiYZHaksUd622tdnX1jb93vZsxVj9cvv9gvzshf29gvzsoe1fdPFfUVERETiR6wccyciIiIiIYi7zt3R3ObnKNqoaWaLzOx7M1tpZrd6wx80s01mttx7tAmaZrCXabWZJeWz/Z/NbIXXxlJv2LFmlmJma7yfFSPUdoOg5VtuZrvNbGAkl93MxpnZNjP7NmhYnpfXzM7y3re1ZvachXjbkmzaH25mq8zsGzN718wqeMNrm1lq0PvwUn7az6btPL/XYV72t4La/tnMlkdo2bP7nhXYZ+8Hi3ANy+F9LZD65c3Plxpmql+ZwwqkfuXQfoHUsGzaLpD65U3nXw1zzsXNg8DByj8CdYDiwNdAwwi0UxU403teFviBwG2FHgTuzGL8hl6WEsCJXsaEfLT/M3DcEcOeBO7xnt8DDItE21m8378C/xfJZQcuAs4Evs3P8gKfA+cSuO7YHKB1PtpvCRT1ng8Lar928HhHzCfP7WfTdp7f63Au+xGvjwD+HaFlz+57VmCffUE/KIAalsP7mue/q3xk+BmfaxiqXxGvXzm0n+f3+2jaz6rtI16PWP3K5bsW8c8/3rbc/XWbH+fcQSDzNj9h5Zzb4pxb5j3fA3xPzlec7wi86Zw74JxbB6z1soZTR2CC93wC0KkA2m4O/Oic+yWXXPlq3zm3BNiRxXxDXl4zqwqUc8594gLflIlB0+S5fefcPOdcmvfrpwSuXZato20/m2XPToEseyZvzbEbMDmneeRj2bP7nhXYZ++DiNewKK1fme0UZA1T/QqIWP3Krv0chHX5/axfXvu+1bB469zl+TY/+WVmtYHGwGfeoP7epu5xQZtaw53LAfPM7EsLXNUeoIpzbgsE/qCA4yPUdrDuHP7FKIhlz5TX5a3uPQ93DoC+BNakMp1oZl+Z2QdmdmFQrnC2n5f3OlLLfiGw1Tm3JmhYRJb9iO9ZNH324VagNcyn+gXRUcNUvwL8qF/gfw0rsPoFBV/D4q1zF9JtfsLWmNkxwDRgoHNuNzAKqAs0ArYQ2OQbiVznO+fOBFoDN5vZRTnFDHPbgZkGLsbaAZjiDSqoZc81WjbtRep9GAKkAW94g7YAtZxzjYHbgUlmVi7M7ef1vY7UZ9CDw/85RmTZs/ieZTtqNu0U9N9gfhRYVh/rF/hcw1S/vMb8qV8QHTWsQOoX+FPD4q1zF9JtfsLBzIoR+LDecM69A+Cc2+qcS3fOZQCv8L/N92HN5Zzb7P3cBrzrtbPV23SbuRl5WyTaDtIaWOac2+plKZBlD5LX5d3I4bse8p3DzHoD7YArvU3leJvTt3vPvyRwzET9cLZ/FO91JJa9KNAFeCsoV9iXPavvGVHw2UdQgdQwP+uX15bfNUz1y6f65c3b1xpWUPXLa8uXGhZvnbsCuc2Pt69+LPC9c+7poOFVg0brDGSeoTMT6G5mJczsRKAegYMjj6btMmZWNvM5gQNjv/Xa6O2N1huYEe62j3DYWk9BLPsR8rS83qbvPWbWzPv8egVNk2dm1gq4G+jgnNsfNLyymSV4z+t47f8Uzvbz+l6He9k9LYBVzrm/dhWEe9mz+57h82cfYRGvYX7WL6+daKhhql8+1S9v3n7XsIjXL28+/tUwl88zjqLtAbQhcEbKj8CQCLVxAYFNot8Ay71HG+A1YIU3fCZQNWiaIV6m1eTjTD0CZ9F97T1WZi4jUAlYAKzxfh4b7raD5lca2A6UDxoWsWUnUIS3AIcIrMFcczTLCzQhUER+BP4DgYt4H2X7awkcG5H5+b/kjXuZ97l8DSwD2uen/WzazvN7Hc5l94aPB244YtxwL3t237MC++z9eBDhGpbD+xrx+uXNy9cahupXgdWvHNovkBqWVdve8PFEuH7l8l2L+OevO1SIiIiIxJF42y0rIiIiUqipcyciIiISR9S5ExEREYkj6tyJiIiIxBF17kRERETiiDp3McTM9vqdISdm9rCZtSiAdi40s5VmttzMSkW6vUgxs3RvGarlYZpEC9yyaYX389IjXh9sZlfmMH1dr82o/luS+BPtf3OqX3mj+hXddCmUGGJme51zx/idI6/MLME5lx6ueQEvAJ85514NxzyPNkd+l+loPk8za0zgfoibzew0INk5Vz3o9UVAN+fcb+FuWyQ/YvVvTvUr23mofkUxbbmLcWbW3sw+s8DNjuebWRVv+IMWuCHzYjP7ycwGBE1zv5mtMrMUM5tsZnd6wxeb2UgzW2Jm35vZ2Wb2jpmtMbNHvXFqm9m3QfO608we9J6PN7N/ec9/NrN/m9lHQNcjMnc1s2/N7GszW+IN62Nm/wka5z0zu9h7vtdbq/4MGAx0A/5tZm+Y2TFmtsDMlnlrgx2D5tHLAjem/trMXvOGVTazaWb2hfc4P4v3tLaZfejNc5mZnecNv9jMFpnZJGCFmSWY2XBvPt+Y2fXeeNlmyuWz3Gtmw7w12vlm1jTo8+sA4Jz7ynm3biJwwc2SZlbCm74cUNw595u3hvupl+1hrelKNFL9Uv1S/YqQ/FzpW4+CfQB7sxhWkf9tgb0WGOE9fxD4L1ACOI7AFdmLEbjK9XKgFFCWwBWy7/SmWQwM857fSuDedVW9eWwkcFXt2sC3Qe3fCTzoPR8P/Mt7/jNwVzbLsQKo7j2v4P3sA/wnaJz3gIu9547A2hxZtFMUKOc9P47AldcNOJXAFb6P81471vs5CbjAe16LwG1hjsxXGijpPa8HLPWeXwzsA070fu8H3Oc9LwEsBU7MLlNun6e3nK295+8C87zP7AxgeRbT/wuYH/R7F+DhoPevh/f8hiza+tvfkh56RPKR1d8cql+qX//7XfUrjI+iSKyrAbxlgXv1FQfWBb32vnPuAHDAzLYBVQjcDmWGcy4VwMxmHTG/zPtYrgBWusA97TCznwjc0HhnHrK9lc3wj4HxZvY28E424wRLJ3Dj5awY8LiZXQRkANUJLOelwFTn3O8Azrkd3vgtgIZmljl9OTMr65zbEzTPYsB/zKyR13b9oNc+d85lvsctgdMz1/aB8gSK6cZsMv2ay3IeBOZ6z1cAB5xzh8xsBYF/Sv9baLNTgWFehkytgMxdPecCnbznk4CncmlbxA+qX6pfmVS/wkidu9j3PPC0c26mtxvgwaDXDgQ9TyfweRs5y5wm44jpM7zp0zh8d37JHOa1L6uBzrkbzOwcoC2w3CtCOc33T5f98SFXApWBs7xC8rM3rRFYkzxSEeDczH8O2bgN2EpgjbMI8Gc2y2TALc655OCJzaxPNplyc8h5q6UEvf/OuQwz++u7amY1CKwZ93LO/Rg0fVPgxhDaEYkWql+qX5lUv8JIx9zFvvLAJu957xDG/whob2YlzewYAgUqL7YCx5tZJe9YiXZ5nB4zq+uc+8w592/gdwJr1D8DjcysiJnVJPBFD0V5YJtXhC4B/s8bvgDoZmaVvDaP9YbPA/oHZWmUzTy3OOcygKuAhGzaTgZuNLNi3rzqm1mZHDLlm5lVAN4HBjvnPg4afiqwKuifyKcEboQN0D1c7YuEmeqX6pfqVwRoy11sKW1mG4N+f5rAmu4UM9tE4AtxYk4zcM59YWYzga+BXwgcZ7Er1ADeF/5h4DMCu1BW5WkJAoabWT0Ca44LvCx481sBfAssC3FebwCzzGwpgWNxVnk5V5rZY8AHZpYOfEXguJgBwAtm9g2Bv/8lBI7pCPYiMM3MugKLyGYNHhhDYHfDMgvsJ/mNwK6ELDOFSX/gJOB+M7vfG9YSaM3/dokADAReN7M7CBTTkD9jkQhR/fo71S/Vr4jQpVAKITM7xjm318xKEygO/ZxzoRYjCRML4+n8ZpZCYDdH5jFGpYFU55wzs+4EDk4OPhNPlxKQmKT6FR1Uv6KbttwVTqPNrCGB4ygmqDD6ZreZLQfauP9dHuCoOOcSjxh0FoGDqo3AQeR9IbBLicDB3Vvz056Ij1S/ooPqVxTTljsRERGROKITKkRERETiiDp3IiIiInFEnTsRERGROKLOnYiIiEgcUedOREREJI6ocyciIiISR/4fl1oVahlJi60AAAAASUVORK5CYII=" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "area_langmuir = []\n", "area_langmuir_lim = []\n", "area_bet = []\n", "for isotherm in isotherms_n2_77k:\n", " area_bet.append(pgc.area_BET(isotherm)['area'])\n", " area_langmuir.append(pgc.area_langmuir(isotherm)['area'])\n", " area_langmuir_lim.append(pgc.area_langmuir(isotherm, p_limits=(0.01, 0.3))['area'])\n", "\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n", "ax1.scatter(area_langmuir, area_bet)\n", "ax2.scatter(area_langmuir_lim, area_bet)\n", "\n", "ax1.set_title('BET v. Langmuir area, full range')\n", "ax2.set_title('BET v. Langmuir area, LP range')\n", "\n", "ax1.plot([0, 2000], [0, 2000], 'k--')\n", "ax2.plot([0, 2000], [0, 2000], 'k--')\n", "\n", "ax1.set_xlim(left=0, right=2000)\n", "ax1.set_ylim(bottom=0, top=2000)\n", "ax2.set_xlim(left=0, right=2000)\n", "ax2.set_ylim(bottom=0, top=2000)\n", "\n", "ax1.set_xlabel('Langmuir surface area [m2/g]')\n", "ax1.set_ylabel('BET surface area [m2/g]')\n", "ax2.set_xlabel('Langmuir surface area [m2/g]')\n", "ax2.set_ylabel('BET surface area [m2/g]')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that some points correspond, while others are not as well correlated. Unless the adsorption isotherm respects the Langmuir model, the calculated surface areas do not match. However, if the Langmuir area is calculated in a low pressure regime, ideally before multilayer adsorption or condensation occurs, the two specific areas are better correlated (right graph).\n", "\n", "\n", "More info can be found in the [documentation of the\n", "module](https://pygaps.readthedocs.io/en/latest/reference/characterisation/area_lang.html)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 2 }