{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The $\\alpha_s$ method\n", "\n", "Very similar to the t-plot method, the $\\alpha_s$ method compares an isotherm on\n", "a porous material with one that was taken on a reference non-porous surface. The\n", "reference isotherm should be measured over a wide pressure range, to be able to\n", "compare loading values. First, make sure the data is imported 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": [ "Unfortunately, we don't have a reference isotherm in our data. We are instead\n", "going to be creative and assume that the adsorption on the silica ($SiO_2$\n", "sample) is a good representation of an adsorption on a non-porous version of the\n", "MCM-41 sample. Let's try:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MCM-41\n", "SiO2\n", "ERROR!: A value in x_new is below the interpolation range.\n" ] } ], "source": [ "iso_1 = next(i for i in isotherms_n2_77k if i.material == 'MCM-41')\n", "iso_2 = next(i for i in isotherms_n2_77k if i.material == 'SiO2')\n", "\n", "print(iso_1.material)\n", "print(iso_2.material)\n", "try:\n", " results = pgc.alpha_s(iso_1, reference_isotherm=iso_2, verbose=True)\n", "except Exception as e:\n", " print('ERROR!:', e)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data in our reference isotherm is on a smaller range than that in the\n", "isotherm that we want to calculate! We are going to be creative again and first\n", "model the adsorption behaviour using a `ModelIsotherm`." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Attempting to model using BET.\n", "Model BET success, RMSE is 0.474\n" ] }, { "data": { "image/png": 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import pygaps\n", "\n", "model = pygaps.ModelIsotherm.from_pointisotherm(iso_2, model='BET', verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With our model fitting the data pretty well, we can now try the $\\alpha_s$ method again." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For linear region 0\n", "The slope is 0.004462 and the intercept is 0.001096, with a correlation coefficient of 0.9848\n", "The adsorbed volume is 0.0381 cm3/g and the area is 270.1 m2/g\n", "For linear region 1\n", "The slope is 0.0005994 and the intercept is 0.008443, with a correlation coefficient of 0.9656\n", "The adsorbed volume is 0.293 cm3/g and the area is 36.28 m2/g\n" ] }, { "data": { "image/png": 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" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "results = pgc.alpha_s(iso_1, model, verbose=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results don't look that bad, considering all our assumptions and modelling. \n", "There are other parameters which can be specified for the $\\alpha_s$ function such as:\n", "\n", "- The relative pressure to use as the reducing pressure\n", "- The known area of the reference material. If this is not specified, the BET method is used to calculate the surface area.\n", "- As in the t-plot function, the limits for the straight line selection.\n", "\n", "Read more about the theory in the\n", "[documentation of the module](../reference/characterisation/alphas_plot.rst) and\n", "in the [manual](../manual/characterisation.rst)." ] } ], "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 }