{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Rotating disk electrode\n", "----\n", "\n", "Make plots for this particular case since we also have the theoretical value at steady state to determine which is accurate. For a RDE comparison to DigiElch 7 which includes surface confined reactions see test named ads03.\n", "\n", "For a concentration in solution of $C$, the Levich current is given by\n", "\n", "\\\$\n", "I_L = (0.620) n F A D^{2/3} \\omega^{1/2} \\nu^{-1/6} C\n", "\\\$\n", "where\n", "* $n$ is the number of electrons transfered in a single reaction (mol$^{-1}$)\n", "* $F = 96485.33289$ C/mol is Faraday's constant, $A$ is the area (cm$^2$)\n", "* $D$ is the diffusion coefficient of species $C$ (cm$^2$/s)\n", "* $\\omega$ is the angular rotation rate (rad/s)\n", "* $\\nu$ is the kinematic viscosity (cm$^2$/s) \n", "* $C$ is the analyte concentration (mol/cm$^3$)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# import required python packages\n", "%matplotlib inline\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Current min/mean/max = -9.6369741909e-05 -4.81785811454e-05 -0.0\n", " E_app min/mean/max = 9.3817558973e-14 0.50002499875 1.0\n", " Current at E_rev = -9.6369741909e-05\n" ] } ], "source": [ "# load t,E,i from MECSim debug file\n", "time, eapp, current = np.loadtxt('EC_Model.tvc',usecols=(0,1,2), unpack=True, skiprows=1)\n", "Iss_Sim = current.min()\n", "ndatapoints = eapp.size\n", "Iss_Rev = current[ndatapoints/2]\n", "print 'Current min/mean/max = ',current.min(),current.mean(),current.max()\n", "print ' E_app min/mean/max = ',eapp.min(),eapp.mean(),eapp.max()\n", "print ' Current at E_rev = ',Iss_Rev\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-9.67915187198e-05 1.00437665187\n" ] } ], "source": [ "RDE_radius = 0.178412412\n", "RDE_rotation = 261.79938779914943653855361527329\n", "RDE_viscosity = 1.0e-2\n", "RDE_diffusion = 1.0e-5\n", "RDE_conc = 1.0e-6\n", "RDE_area = np.pi*(RDE_radius**2.0)\n", "F = 96485.33289\n", "ne = 1.0\n", "iLevich = (-0.620*ne*F*RDE_area*(RDE_diffusion**(2.0/3.0))*(RDE_rotation**0.5)\n", " *(RDE_viscosity**(-1.0/6.0))*RDE_conc)\n", "print iLevich, iLevich/current.min()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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Z3axxCQ/tdCmpT5hISOpndfJwMa6xIfUlEwlJ/axOJG6hjNi4D0dsSH3FREJSP6ubNupR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\nQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,6),dpi=100)\n", "plt.rcParams['xtick.major.size'] = 5\n", "plt.rcParams['xtick.major.width'] = 2\n", "plt.rcParams['xtick.minor.size'] = 3\n", "plt.rcParams['xtick.minor.width'] = 2\n", "plt.rcParams['ytick.major.size'] = 5\n", "plt.rcParams['ytick.major.width'] = 2\n", "plt.rcParams['ytick.minor.size'] = 3\n", "plt.rcParams['ytick.minor.width'] = 2\n", "plt.rcParams['axes.linewidth'] = 2\n", "plt.rcParams['lines.linewidth'] = 2\n", "plt.rcParams['xtick.labelsize'] = 14\n", "plt.rcParams['ytick.labelsize'] = 14\n", "plt.ticklabel_format(axis='y', style='sci', scilimits=(-2,2))\n", "plt.plot(eapp,current, 'k', label='A', linewidth=2)\n", "plt.plot([plt.xlim()[0], plt.xlim()[1]],[iLevich, iLevich], 'r')\n", "plt.ylim(1.01*iLevich, plt.ylim()[1])\n", "plt.xlabel('Voltage (V)', fontsize=20)\n", "plt.ylabel('Current (A)', fontsize=20)\n", "plt.savefig('RDETest.png')\n", "plt.savefig('RDETest.ps')\n", "plt.savefig('RDETest.pdf')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The red line above corresponds to the theoretical value from Zhang & Bond 2007 and the black curve is the simulation results from MECSim." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }