{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# t-plot calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another common characterisation method is the t-plot method. 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": [
    "Besides an isotherm, this method requires a so-called thickness function, which\n",
    "empirically describes the thickness of adsorbate layers on a non-porous surface\n",
    "as a function of pressure. It can be specified by the user, otherwise the\n",
    "\"Harkins and Jura\" thickness model is used by default. When the function is\n",
    "called without any other parameters, the framework will attempt to find plateaus\n",
    "in the data and automatically fit them with a straight line.\n",
    "\n",
    "Let's look again at the our MCM-41 pore-controlled glass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCM-41\n",
      "For linear region 1\n",
      "The slope is 0.009732 and the intercept is 0.0002239, with a correlation coefficient of 1\n",
      "The adsorbed volume is 0.00778 cm3/g and the area is 338.2 m2/g\n",
      "For linear region 2\n",
      "The slope is 0.001568 and the intercept is 0.008244, with a correlation coefficient of 0.9993\n",
      "The adsorbed volume is 0.286 cm3/g and the area is 54.5 m2/g\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.t_plot(isotherm, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first line can be attributed to adsorption on the inner pore surface, while\n",
    "the second one is adsorption on the external surface after pore filling. Two\n",
    "values are calculated for each section detected: the adsorbed volume and the\n",
    "specific area. In this case, the area of the first linear region corresponds to\n",
    "the pore area. Compare the specific surface area obtained of 340 $m^2$ with the\n",
    "360 $m^2$ obtained through the BET method previously.\n",
    "\n",
    "In the second region, the adsorbed volume corresponds to the total pore volume\n",
    "and the area is the external surface area of the sample.\n",
    "\n",
    "We can get a better result for the surface area by attempting to have the first\n",
    "linear region at a zero intercept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MCM-41\n",
      "For linear region 1\n",
      "The slope is 0.01003 and the intercept is 0.0001048, with a correlation coefficient of 0.9996\n",
      "The adsorbed volume is 0.00364 cm3/g and the area is 348.4 m2/g\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(isotherm.material)\n",
    "results = pgc.t_plot(\n",
    "    isotherm, \n",
    "    thickness_model='Harkins/Jura', \n",
    "    t_limits=(0.3,0.44), \n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A near perfect match with the BET method. Of course, the method is only this\n",
    "accurate in certain cases, see more info in the\n",
    "[documentation of the module](../reference/characterisation/t_plot.rst). Let's\n",
    "do the calculations for all the nitrogen isotherms, using the same assumption\n",
    "that the first linear region is a good indicator of surface area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "<span style=\"font-weight: bold\">[</span>\n",
       "    <span style=\"font-weight: bold\">(&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">pygaps.Material</span><span style=\"color: #000000; text-decoration-color: #000000\"> </span><span style=\"color: #008000; text-decoration-color: #008000\">'MCM-41'</span><span style=\"font-weight: bold\">&gt;</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'338.20'</span><span style=\"font-weight: bold\">)</span>,\n",
       "    <span style=\"font-weight: bold\">(&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">pygaps.Material</span><span style=\"color: #000000; text-decoration-color: #000000\"> </span><span style=\"color: #008000; text-decoration-color: #008000\">'NaY'</span><span style=\"font-weight: bold\">&gt;</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'199.77'</span><span style=\"font-weight: bold\">)</span>,\n",
       "    <span style=\"font-weight: bold\">(&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">pygaps.Material</span><span style=\"color: #000000; text-decoration-color: #000000\"> </span><span style=\"color: #008000; text-decoration-color: #008000\">'SiO2'</span><span style=\"font-weight: bold\">&gt;</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'249.14'</span><span style=\"font-weight: bold\">)</span>,\n",
       "    <span style=\"font-weight: bold\">(&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">pygaps.Material</span><span style=\"color: #000000; text-decoration-color: #000000\"> </span><span style=\"color: #008000; text-decoration-color: #008000\">'Takeda 5A'</span><span style=\"font-weight: bold\">&gt;</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'99.55'</span><span style=\"font-weight: bold\">)</span>,\n",
       "    <span style=\"font-weight: bold\">(&lt;</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff; font-weight: bold\">pygaps.Material</span><span style=\"color: #000000; text-decoration-color: #000000\"> </span><span style=\"color: #008000; text-decoration-color: #008000\">'UiO-66(Zr)'</span><span style=\"font-weight: bold\">&gt;</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'17.77'</span><span style=\"font-weight: bold\">)</span>\n",
       "<span style=\"font-weight: bold\">]</span>\n",
       "</pre>\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'338.20'\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'199.77'\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'249.14'\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'99.55'\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'17.77'\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.t_plot(isotherm, 'Harkins/Jura')))\n",
    "    \n",
    "[(x, f\"{y['results'][0].get('area'):.2f}\") for (x,y) in results]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that, while we get reasonable values for the silica samples, all the\n",
    "rest are quite different. This is due to a number of factors depending on the\n",
    "material: adsorbate-adsorbent interactions having an effect on the thickness of\n",
    "the layer or simply having a different adsorption mechanism. The t-plot requires\n",
    "careful thought to assign meaning to the calculated values.\n",
    "\n",
    "Since no thickness model can be universal, the framework allows for the\n",
    "thickness model to be substituted with an user-provided function which will be\n",
    "used for the thickness calculation, or even another isotherm, which will be\n",
    "converted into a thickness model.\n",
    "\n",
    "For example, using a carbon black type model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Takeda 5A\n",
      "For linear region 1\n",
      "The slope is 0.0006789 and the intercept is 0.0101, with a correlation coefficient of 0.9939\n",
      "The adsorbed volume is 0.351 cm3/g and the area is 23.59 m2/g\n"
     ]
    },
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def carbon_model(relative_p):\n",
    "    return 0.88*(relative_p**2) + 6.45*relative_p + 2.98\n",
    "\n",
    "isotherm = next(i for i in isotherms_n2_77k if i.material=='Takeda 5A')\n",
    "print(isotherm.material)\n",
    "results = pgc.t_plot(isotherm, thickness_model=carbon_model, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Isotherms which do not use nitrogen can also be used, but one should be careful that the thickness model is well chosen.\n",
    "\n",
    "More information about the functions and their use can be found 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
}