Exercícios-Fis-Qui2-Eq-Fases.ipynb

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   "outputs": [],
   "source": [
    "# Tudo começa com a importação dos módulos necessários para fazer as contas\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bwrS2o8qWK4k"
   },
   "source": [
    "# Exercício 1) \n",
    "Use os dados abaixo de temperatura-pressão de vapor para determinar $\\Delta_{sub} Hº$, $\\Delta_{vap} Hº$ e $\\Delta_{fus} Hº$ para a água a 0ºC:\n",
    "\n",
    "Gelo\n",
    "\n",
    "|   |   |   |   |   |   |   |\n",
    "|---|---|---|---|---|---|---|\n",
    "|p/torr | 3.880 | 4.045 | 4.217 | 4.395 | 4.579\n",
    "|T/ºC | -2.0 | -1.5 | -1.0 | -0.5 | 0.0\n",
    "\n",
    "Água\n",
    "\n",
    "|   |   |   |   |   |   |   |\n",
    "|---|---|---|---|---|---|---|\n",
    "|p/torr | 4.579 | 4.750 | 4.926 | 5.107 | 5.294\n",
    "|T/ºC | 0.0 | 0.5 | 1.0 | 1.5 | 2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "a7IXD1xzYDyM"
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   "source": [
    "### Solução:\n",
    "De acordo com a equação de Clausius-Clayperon ($ \\ln(\\frac{p_2}{p_1}) = - \\frac{\\Delta_{vap} Hº}{R} \\times (\\frac{1}{T_2}-\\frac{1}{T_2})$, um gráfico de ln(P) contra 1/T deve ser linear e apresentar coeficiente angular igual a - $\\frac{\\Delta Hº}{R}$.\n",
    "Resolvendo inicialmente para o gelo:"
   ]
  },
  {
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   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
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     "elapsed": 801,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeficiente angular = -6129.91617\n",
      "coeficiente linear =  23.97549\n",
      "entalpia de sublimação (J/K*mol)=  50964.12304\n"
     ]
    }
   ],
   "source": [
    "# Definindo os vetores que serão plotados\n",
    "# Resolvendo para o gelo\n",
    "\n",
    "p = np.array([  3.880 , 4.045 , 4.217 , 4.395 , 4.579])\n",
    "t_celsius = np.array([ -2.0 , -1.5 , -1.0 , -0.5 , 0.0]) \n",
    "T = 273 + t_celsius\n",
    "\n",
    "lnP = np.log(p)\n",
    "iT = 1/T\n",
    "\n",
    "\n",
    "deg = 1\n",
    "z = np.polyfit(iT, lnP, deg)\n",
    "y = np.poly1d(z)\n",
    "\n",
    "DH_sub = -8.314*z[0] # calculando deltaH de sublimação\n",
    "\n",
    "print('coeficiente angular = ' + format(z[0] , ' 6.5f'))  \n",
    "print('coeficiente linear = ' + format(z[1] , ' 6.5f'))  \n",
    "print('entalpia de sublimação (J/K*mol)= ' + format(DH_sub , ' 6.5f'))  "
   ]
  },
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    "colab_type": "code",
    "executionInfo": {
     "elapsed": 767,
     "status": "ok",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
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     "output_type": "execute_result"
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iT,lnP,'*')\n",
    "plt.plot(iT,y(iT), color='blue', linewidth=1)\n",
    "plt.xlabel('1/Temperatura (1/K)')\n",
    "plt.ylabel('ln(P)(torr)')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 69
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 641,
     "status": "ok",
     "timestamp": 1597760679381,
     "user": {
      "displayName": "Leonardo Baptista",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUvq2V3SOxhV7JYWonqnLC2okceWVQL5__JHI3sg=s64",
      "userId": "17470973056258090925"
     },
     "user_tz": 180
    },
    "id": "NGN5e9U1aaiP",
    "outputId": "73ddc414-0ebf-4829-ea5a-f0adfb10b447"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coeficiente angular = -5445.28900\n",
      "coeficiente linear =  21.46772\n",
      "entalpia de sublimação (J/K*mol)=  45272.13274\n"
     ]
    }
   ],
   "source": [
    "# Resolvendo para água \n",
    "#Definindo os vetores que serão plotados\n",
    "\n",
    "p = np.array([  4.579 , 4.750 , 4.926 , 5.107 , 5.294])\n",
    "t_celsius = np.array([ 0.0 , 0.5 , 1.0 , 1.5 , 2.0]) \n",
    "T = 273 + t_celsius\n",
    "\n",
    "lnP = np.log(p)\n",
    "iT = 1/T\n",
    "\n",
    "\n",
    "deg = 1\n",
    "z2 = np.polyfit(iT, lnP, deg)\n",
    "y1 = np.poly1d(z2)\n",
    "DH_vap = -8.314*z2[0] # calculando deltaH de vaporização\n",
    "\n",
    "print('coeficiente angular = ' + format(z2[0] , ' 6.5f'))  \n",
    "print('coeficiente linear = ' + format(z2[1] , ' 6.5f'))  \n",
    "print('entalpia de sublimação (J/K*mol)= ' + format(DH_vap , ' 6.5f'))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 856,
     "status": "ok",
     "timestamp": 1597760755416,
     "user": {
      "displayName": "Leonardo Baptista",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUvq2V3SOxhV7JYWonqnLC2okceWVQL5__JHI3sg=s64",
      "userId": "17470973056258090925"
     },
     "user_tz": 180
    },
    "id": "h9ucZ5Qgbqep",
    "outputId": "2055cc17-57a3-4ecc-bb3d-2f4de575e931"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(iT,lnP,'*')\n",
    "plt.plot(iT,y1(iT), color='green', linewidth=1)\n",
    "plt.xlabel('1/Temperatura (1/K)')\n",
    "plt.ylabel('ln(P)(torr)')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JIWAoHfyjKqy"
   },
   "source": [
    "Sabendo que: $ \\Delta_{sub} Hº = \\Delta_{vap} Hº + \\Delta_{fus} Hº$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 644,
     "status": "ok",
     "timestamp": 1597760790149,
     "user": {
      "displayName": "Leonardo Baptista",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUvq2V3SOxhV7JYWonqnLC2okceWVQL5__JHI3sg=s64",
      "userId": "17470973056258090925"
     },
     "user_tz": 180
    },
    "id": "q5G4kcDsjeyg",
    "outputId": "8e665f62-b6e0-4baa-eb95-5e5c948b1c54"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entalpia de fusão (J/K*mol)=  5691.99029\n"
     ]
    }
   ],
   "source": [
    "DH_fus = DH_sub - DH_vap\n",
    "print('entalpia de fusão (J/K*mol)= ' + format(DH_fus , ' 6.5f'))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 726,
     "status": "ok",
     "timestamp": 1597760996393,
     "user": {
      "displayName": "Leonardo Baptista",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgUvq2V3SOxhV7JYWonqnLC2okceWVQL5__JHI3sg=s64",
      "userId": "17470973056258090925"
     },
     "user_tz": 180
    },
    "id": "PSJB23fZmWj7",
    "outputId": "2c0db991-4797-41cd-f5bc-7083b7443747"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show>"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = np.arange(270,276,0.5) #criando um intervalo de temperaturas\n",
    "iT = 1/T\n",
    "plt.plot(T,y1(iT), color='red', linewidth=1,label='solido')\n",
    "plt.plot(T,y(iT), color='green', linewidth=1,label='liquido')\n",
    "plt.xlabel('Temperatura (/K)')\n",
    "plt.ylabel('ln(P)(torr)')\n",
    "plt.legend(handlelength=4,loc='best')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uPOTO4h10Nay"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Exercícios-Fis-Qui2-Eq-Fases.ipynb",
   "provenance": []
  },
  "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}