{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Métallisation de plastiques par colloïdes à base de palladium\n", "\n", "La métallisation de plastique peut-être réalisée de façon industrielle après adhésion sur le plastique de colloïdes à base de palladium (procédé FUTURON ). Cette étude consiste à étudier la stabilité des particules en suspension puis estimer la faculté à l’adhésion de ces particules sur une plaque de plastique. \n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "#CONSTANTES\n", "\n", "#Nombre d'Avogadro\n", "avo=6.02214076e23\n", "\n", "#Constante de Boltzmann\n", "k=1.38064852e-23 #m2 kg s-2 K-1 ou J/K\n", "T=298.13\n", "kT=k*T #énergie thermique par molécule\n", "RGP=k*avo\n", "\n", "#Charge de l'électron\n", "e=1.602e-19\n", "F=e*avo\n", "\n", "#Permittivité électrique de l'eau\n", "eps=8.85418782e-12*78.5 #m-3 kg-1 s4 A2\n", "\n", "#Viscosité (dynamique) de l'eau à 20°C\n", "mu=1e-3 #Pa.s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$Log_{10}(W)=-2.06 10^{+9} \\frac{a\\Upsilon^2}{z^2}Log_{10}c+b$$\n", "\n", "Si la ccc est connu on peut déterminer b (puisque la ccc correspond à une stabilité de 1) :\n", "\n", "$$b=2.06 10^{+9} \\frac{a\\Upsilon^2}{z^2}Log_{10}ccc$$\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3)\tCalculer la valeur du paramètre b " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-1.2594435785440277\n", "b= -1.2594435785440277\n" ] } ], "source": [ "zeta_c=-0.05 #V\n", "z=1\n", "a=3e-9\n", "ccc=0.1\n", "upsilon=np.tanh(z*e*zeta_c/(4*kT))\n", "pente=-2.06e+9*a*upsilon**2/(z**2)\n", "b=np.log10(ccc)*(-pente)\n", "print (pente)\n", "print ('b=',b)\n", "\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I=0.05 M V/kT @h=1 nm 0.8946742225630204\n", "I=0.05 M Vmax/kT 0.9250214120196755\n", "I=0.01 M V/kT @h=1 nm 1.9520060102267032\n", "I=0.01 M Vmax/kT 1.960037850535986\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "zeta_p=-0.015\n", "upsilon1=np.tanh(z*e*zeta_c/(4*kT))\n", "upsilon2=np.tanh(z*e*zeta_p/(4*kT))\n", "\n", "Hamaker=1e-20\n", "Ld=np.sqrt(eps*RGP*T/(2*F*F*I*1000))\n", "def V(h):\n", " V=4*np.pi*a*eps*((4*RGP*T/(F))**2)*upsilon1*upsilon2*np.exp(-h/Ld)-Hamaker*a/(6*h)\n", " return V\n", "h=np.linspace(1e-10,1e-8,100)\n", "\n", "I=0.05\n", "Ld=np.sqrt(eps*RGP*T/(2*F*F*I*1000))\n", "plt.plot(h,V(h)/kT)\n", "print ('I=0.05 M V/kT @h=1 nm', V(1e-9)/kT )\n", "print ('I=0.05 M Vmax/kT ', max(V(h)/kT))\n", "I=0.01\n", "Ld=np.sqrt(eps*RGP*T/(2*F*F*I*1000))\n", "plt.plot(h,V(h)/kT)\n", "print ('I=0.01 M V/kT @h=1 nm', V(1e-9)/kT )\n", "print ('I=0.01 M Vmax/kT ', max(V(h)/kT))\n", "plt.ylim(-1,)\n", "plt.xlabel('h (m)')\n", "plt.ylabel('V/kT')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }