{
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  "name": ""
 },
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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "    Chapter 10 - The rates of reactions"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example E1 - Pg 222"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Calculate the overall rate constant and hence the overall rate law\n",
      "#Initialization of variables\n",
      "%matplotlib inline\n",
      "import math\n",
      "import numpy as np\n",
      "from numpy import linalg\n",
      "import matplotlib\n",
      "from matplotlib import pyplot\n",
      "def fun(x,n):\n",
      "\tfor i in range(0,len(x)):\n",
      "\t\tx[i]=x[i]*math.pow(10,-n)\n",
      "\treturn\n",
      "\n",
      "I=([1., 2., 4., 6.])\n",
      "r1=([1.070, 3.48, 13.9, 31.3])\n",
      "r2=([4.35, 17.4, 69.6, 157])\n",
      "r3=([10.69, 34.7, 138, 313])\n",
      "Ar=([1., 5., 10.])\n",
      "fun(r1,3)\n",
      "fun(I,5)\n",
      "fun(r2,3)\n",
      "fun(r3,3)\n",
      "fun(Ar,3)\n",
      "#calculations\n",
      "def fun1(x):\n",
      "\tfor i in range(0,len(x)):\n",
      "\t\tx[i]=math.log10(x[i])\n",
      "\treturn x\n",
      "\n",
      "logI=fun1(I)\n",
      "logr1=fun1(r1)\n",
      "logr2=fun1(r2)\n",
      "logr3=fun1(r3)\n",
      "#The calculations are approximate.hence the value differs from textbook a bit.\n",
      "x=logI\n",
      "y=logr1\n",
      "A = np.vstack([x, np.ones(len(x))]).T\n",
      "m1, b1 = np.linalg.lstsq(A, y)[0]\n",
      "\n",
      "y=logr2\n",
      "A = np.vstack([x, np.ones(len(x))]).T\n",
      "m2, b2 = np.linalg.lstsq(A, y)[0]\n",
      "\n",
      "y=logr3\n",
      "A = np.vstack([x, np.ones(len(x))]).T\n",
      "m3, b3 = np.linalg.lstsq(A, y)[0]\n",
      "\n",
      "logAr=fun1(Ar)\n",
      "kdash=([b1, b2, b3])\n",
      "pyplot.plot(logAr,kdash)\n",
      "pyplot.xlabel('log(Ar)')\n",
      "pyplot.ylabel('log(kdash)')\n",
      "x=logAr\n",
      "y=kdash\n",
      "\n",
      "A = np.vstack([x, np.ones(len(x))]).T\n",
      "m4, b4 = np.linalg.lstsq(A, y)[0]\n",
      "\n",
      "logk=b4\n",
      "k=math.pow(10,logk)\n",
      "#results\n",
      "print '%s %.1e %s' %(\"Overall rate law is r =\",k,\" [I]^2 [Ar]\")\n",
      "pyplot.show()\n",
      "print '%s' %('The answers in the textbook are rounded and hence a bit different from the code.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "Overall rate law is r = 8.1e+09  [I]^2 [Ar]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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hISKeQkHhAAkJZj/i4YdhxQqoXdvVFYmIOI6mnirAMCAyEmbMgI8+ggcecHVF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\nc+SMsRQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6097fd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The answers in the textbook are rounded and hence a bit different from the code.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example E2 - Pg 224"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#calcualte the rate constant for the given first order reaction\n",
      "#Initialization of variables\n",
      "import math\n",
      "import numpy as np\n",
      "import matplotlib\n",
      "from matplotlib import pyplot\n",
      "t=([0, 1000., 2000., 3000., 4000.])\n",
      "p=[10.20, 5.72, 3.99, 2.78, 1.94]\n",
      "def fun2(x):\n",
      "\tfor i in range(0,len(x)):\n",
      "\t\tx[i]=math.log(x[i])\n",
      "\treturn x\n",
      "lnp=fun2(p)\n",
      "x=t\n",
      "y=lnp\n",
      "#hence the value differs from textbook a bit.\n",
      "A = np.vstack([x, np.ones(len(x))]).T\n",
      "m1, b1 = np.linalg.lstsq(A, y)[0]\n",
      "k= -m1\n",
      "pyplot.plot(x,y)\n",
      "pyplot.xlabel('time t/sec ')\n",
      "pyplot.ylabel('ln(P/Torr)')\n",
      "#Since first order reaction\n",
      "#results\n",
      "print '%s %.2e %s' %(\"rate constant =\",k,\" s^-1\")\n",
      "pyplot.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "rate constant = 4.04e-04  s^-1\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TEVmG1buehBA4d+4czp07h7Nnz6KiosL0+ty5c/W+MJEauLkBTz8tz8Ho3l2ehTFmDHDi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\nRGaxUBARkVksFEREZBYLBRERmfX/emWTHUtSxxAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5d4f970>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example E3 - Pg 230"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Calculate the Activation energy and Arrhenius factor\n",
      "#Initialization of variables\n",
      "import math\n",
      "import numpy as np\n",
      "from numpy import linalg\n",
      "T=[700, 730, 760, 790, 810, 840, 910, 1000]\n",
      "k=[0.011, 0.035, 0.105, 0.343, 0.789, 2.17, 20, 145]\n",
      "#calculations\n",
      "def fun1(x):\n",
      "\tfor i in range(0,len(x)):\n",
      "\t\tx[i]=1000./(x[i])\n",
      "\treturn x\n",
      "x= fun1(T)\n",
      "def fun2(x):\n",
      "\tfor i in range(0,len(x)):\n",
      "\t\tx[i]=math.log(x[i])\n",
      "\treturn x\n",
      "y=fun2(k)\n",
      "A = np.vstack([x, np.ones(len(x))]).T\n",
      "m1, b1 = np.linalg.lstsq(A, y)[0]\n",
      "print '%s' %('from graph')\n",
      "Ea=-m1*8.3145/1000. *1000.\n",
      "A=math.pow(math.e,(b1))\n",
      "#results\n",
      "print '%s %d %s' %(\"Activation energy =\",Ea,\" kJ/mol\")\n",
      "print '%s %.2e %s' %(\"\\n Arrhenius factor =\",A,\"L/ mol s\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "from graph\n",
        "Activation energy = 188  kJ/mol\n",
        "\n",
        " Arrhenius factor = 1.08e+12 L/ mol s\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example I1 - Pg 228"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#calculate the net time required\n",
      "#Initialization of variables\n",
      "import math\n",
      "t=28.4 #min\n",
      "#calculations\n",
      "n=math.log10(8.) / math.log10(2.)\n",
      "time=n*t\n",
      "#results\n",
      "print '%s %.1f %s' %(\"Time required =\",time,\"min\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Time required = 85.2 min\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Example I2 - Pg 230"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#calculate the value of kdash\n",
      "#Initialization of variables\n",
      "import math\n",
      "E=50*1000. #J/mol\n",
      "T1=25+273. #K\n",
      "T2=37+273. #K\n",
      "#calculations\n",
      "ln=E/8.3145 *(1./T1-1./T2)\n",
      "factor=math.pow(math.e,(ln))\n",
      "#results\n",
      "print '%s %.2f %s' %(\"kdash =\",factor,\"k\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "kdash = 2.18 k\n"
       ]
      }
     ],
     "prompt_number": 21
    }
   ],
   "metadata": {}
  }
 ]
}