path: root/Elements_of_Physical_Chemistry/Chapter6.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 6 - The properties of mixtures"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example E1 - Pg 112"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Calculate the mole fraction of glycerine\n",
      "#Initialization of variables\n",
      "m=0.14 #mol/kg\n",
      "w=1. #kg Assume\n",
      "#Calculations\n",
      "ngly=m*w\n",
      "nwater=w*1000 /18.02\n",
      "ntotal=ngly+nwater\n",
      "xgly=ngly/ntotal\n",
      "#results\n",
      "print '%s %.2e' %('Mole fraction of glycerine  is xgly =',xgly)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mole fraction of glycerine  is xgly = 2.52e-03\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example E2 - Pg 114"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#calculate the volume of the mixture\n",
      "#Initialization of variables\n",
      "mE=50. #g\n",
      "mW=50. #g\n",
      "#calculations\n",
      "nE=mE/46.\n",
      "nW=mW/18.\n",
      "ntotal=nE+nW\n",
      "xE=nE/ntotal\n",
      "xW=1-xE\n",
      "print '%s' %('for the observed xE and xW')\n",
      "vE=55 #cc/mol\n",
      "vW=18 #cc/mol\n",
      "V=nE*vE+nW*vW\n",
      "#results\n",
      "print '%s %.1f %s' %('\\n Volume of the mixture =',V,'cm^3 ')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "for the observed xE and xW\n",
        "\n",
        " Volume of the mixture = 109.8 cm^3 \n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example E3 - Pg 121"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Calculate Ka and Kc from the raoults law line \n",
      "#Initialization of variables\n",
      "import matplotlib\n",
      "from matplotlib import pyplot\n",
      "xc=([0, 0.20, 0.40, 0.60, 0.80, 1])\n",
      "pc=([0, 35, 82, 142, 219, 293])\n",
      "pa=([347, 270, 185, 102, 37, 0])\n",
      "#calculations\n",
      "pyplot.plot(xc,pc)\n",
      "pyplot.plot(xc,pa)\n",
      "pyplot.xlabel('Mole fraction xc')\n",
      "pyplot.ylabel('Pressure /Torr')\n",
      "print '%s' %('From the graph it is clear that KA=175 torr and KC=165 torr. They are plotted with Raoults law lines')\n",
      "pyplot.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "From the graph it is clear that KA=175 torr and KC=165 torr. They are plotted with Raoults law lines\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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FryN/xcrSirZL23Lx9kWtQxJCc0lJ0Lmz2qV0+DCMGGF6yaGgJEGIJ8gmREL8bf16aNpU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\nhBAiR/8PUIILe33jvscAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6121270>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example E4 - Pg 123"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Calculate if required concentration can be maintained under normal conditions.\n",
      "#Initialization of variables\n",
      "import math\n",
      "C=4/1000. #g/L\n",
      "MO2=32. #g/mol\n",
      "Mw=18.\n",
      "w=1 #L\n",
      "K=3.3*math.pow(10,7) #torr\n",
      "patm=0.21*760 #torr\n",
      "#calculations\n",
      "nO2=C/MO2\n",
      "nH2O=w*1000. /Mw\n",
      "xO2=nO2/(nO2+nH2O)\n",
      "pO2=xO2*K\n",
      "if(pO2<patm):\n",
      "    print '%s %.1e' %('The required concentration can be maintained under normal conditions and pressure is (Torr)',pO2)\n",
      "else:\n",
      "    print '%s %.e' %('The required concentration cannot be maintained under normal conditions and pressure is (Torr)',pO2)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The required concentration can be maintained under normal conditions and pressure is (Torr) 7.4e+01\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example E5 - Pg 131"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Calculate the molar mass of the enzyme\n",
      "#Initialization of variables\n",
      "%pylab inline\n",
      "import numpy as np\n",
      "from numpy import linalg\n",
      "import matplotlib\n",
      "from matplotlib import pyplot\n",
      "c=([1, 2, 4, 7, 9])\n",
      "hbyc=([0.28, 0.36, 0.503, 0.739, 0.889])\n",
      "R=8.3145 #J/K mol\n",
      "T=298 #K\n",
      "g=9.81 #m/s^2\n",
      "d=0.9998 #g/cm^3\n",
      "#calculations\n",
      "pyplot.plot(c,hbyc)\n",
      "pyplot.xlabel('c')\n",
      "pyplot.ylabel('hbyc')\n",
      "A = np.vstack([c, np.ones(len(c))]).T\n",
      "vector=numpy.linalg.lstsq(A,hbyc)\n",
      "intercept=vector[0]\n",
      "intercept1=intercept[1]/100.\n",
      "M=R*T/(d*g*intercept1)/1000.\n",
      "#results\n",
      "print '%s %d %s' %('Molar mass of the enzyme is close to',M,'kDa')\n",
      "print '%s' %('The answer is a bit different in the textbook due to rounding off error in the textbook')\n",
      "pyplot.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "Molar mass of the enzyme is close to 123 kDa"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "The answer is a bit different in the textbook due to rounding off error in the textbook\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x9caefb0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example E6 - Pg 136"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#calculate how many times the rich phase is more abundant in nitrobenzene\n",
      "#Initialization of variables\n",
      "nB=0.59 #mol\n",
      "nNB=0.41 #mol\n",
      "xN1=0.38\n",
      "xN2=0.74\n",
      "xNm=0.41\n",
      "#calculations\n",
      "print '%s' %('By lever rule')\n",
      "ratio=(xNm-xN1)/(xN2-xNm)\n",
      "percent=ratio*100. +1\n",
      "#results\n",
      "print '%s %d %s' %(\"The rich phase is\",percent,\"times more abundant in nitrobenzene\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "By lever rule\n",
        "The rich phase is 10 times more abundant in nitrobenzene\n"
       ]
      }
     ],
     "prompt_number": 24
    }
   ],
   "metadata": {}
  }
 ]
}