Five_Questions_in_Chemistry.ipynb

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      "source": [
        "# Scope\n",
        "\n",
        "1. Mathematical Operations, Printing Variables, Using Chempy Library\n",
        "2. Functions\n",
        "3. Plotting\n",
        "4. Fitting\n",
        "5. Conditional Statements"
      ],
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      "cell_type": "markdown",
      "source": [
        "## 1. Mathematical Operations, Printing Variables, Using Chempy Library\n"
      ],
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        "### 1a. Example: SOLUTIONS\n",
        "A 100.0 g sample of ice at 0°C is mixed with 5.00 g of sodium chloride (NaCl). Calculate the change in the freezing point of the ice. Assume that the ice behaves as water and the molal freezing point depression constant (Kf) for water is 1.86 °C·kg/mol. The van't Hoff factor (𝑖) for NaCl is 2."
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        "#!pip install chempy #install chempy if needed - do this once\n",
        "\n",
        "from chempy import Substance\n",
        "\n",
        "# Create an NaCl substance object\n",
        "substance_NaCl = Substance.from_formula('NaCl')\n",
        "\n",
        "# Get molar mass\n",
        "mm_NaCl = substance_NaCl.molar_mass()\n",
        "\n",
        "#define constants:\n",
        "Kf = 1.86 #°C·kg/mol\n",
        "i = 2 #van't Hoff factor\n",
        "mass_NaCl_g = 5.00 #g\n",
        "mass_ice_kg = 100.0/1000 #kg\n",
        "\n",
        "#calculate moles of NaCl\n",
        "moles_NaCl = mass_NaCl_g / mm_NaCl.magnitude\n",
        "#calculate molality of NaCl\n",
        "molality_NaCl = moles_NaCl / mass_ice_kg\n",
        "\n",
        "#calculate freezing point depression with ΔTf = Kf x m x i\n",
        "delta_Tf = Kf * molality_NaCl * i\n",
        "\n",
        "#calculate freezing point:\n",
        "Tf_mixture = 100 - delta_Tf\n",
        "\n",
        "print(\"The freezing point depression of a 100 g ice containing 5 g NaCl is %s °C. The freezing point of the mixture is: %s °C\" %(round(delta_Tf,2), round(Tf_mixture,2)))"
      ],
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          "text": [
            "The freezing point depression of a 100 g ice containing 5 g NaCl is 3.18 °C. The freezing point of the mixture is: 96.82 °C\n"
          ]
        }
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "### 1b. Do it yourself\n",
        "A 150.0 g sample of ice at 0°C is mixed with 8.00 g of calcium chloride (CaCl₂). Calculate the change in the freezing point of the ice. Assume that the ice behaves as water and the molal freezing point depression constant (Kf) for water is 1.86 °C·kg/mol. The van't Hoff factor (𝑖) for CaCl₂ is 3."
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          "text": [
            "The freezing point depression of a 100.0 g ice containing 5.0 g NaCl is 3.18 °C. The freezing point of the mixture is: -3.18 °C\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Functions\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "### 2a. Example: Functionalize the above example on the calculation of the freezing point of ice with the addition of NaCl."
      ],
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        "from chempy import Substance\n",
        "\n",
        "def calculate_freezing_point_depression(salt_formula, mass_salt_g, mass_solvent_g, Kf=1.86, i=2):\n",
        "    \"\"\"\n",
        "    Calculate the freezing point depression of a solution.\n",
        "\n",
        "    Parameters:\n",
        "    - salt_formula: str, the chemical formula of the salt (e.g., 'NaCl', 'CaCl2')\n",
        "    - mass_salt_g: float, mass of the salt in grams\n",
        "    - mass_solvent_g: float, mass of the solvent in grams\n",
        "    - Kf: float, molal freezing point depression constant (default is 1.86 °C·kg/mol for water)\n",
        "    - i: int, van't Hoff factor (default is 2 for NaCl, 3 for CaCl2)\n",
        "\n",
        "    Returns:\n",
        "    - delta_Tf: float, freezing point depression in °C\n",
        "    - Tf_mixture: float, new freezing point of the mixture in °C\n",
        "    \"\"\"\n",
        "\n",
        "    # Create a substance object for the salt\n",
        "    substance = Substance.from_formula(salt_formula)\n",
        "\n",
        "    # Get molar mass of the salt\n",
        "    mm_salt = substance.molar_mass()\n",
        "\n",
        "    # Convert mass of solvent to kg\n",
        "    mass_solvent_kg = mass_solvent_g / 1000\n",
        "\n",
        "    # Calculate moles of salt\n",
        "    moles_salt = mass_salt_g / mm_salt.magnitude\n",
        "\n",
        "    # Calculate molality of the solution (mol/kg)\n",
        "    molality_salt = moles_salt / mass_solvent_kg\n",
        "\n",
        "    # Calculate the freezing point depression: ΔTf = Kf x m x i\n",
        "    delta_Tf = Kf * molality_salt * i\n",
        "\n",
        "    # Calculate the new freezing point of the mixture\n",
        "    Tf_mixture = 0 - delta_Tf  # since the initial freezing point of water is 0°C\n",
        "\n",
        "    return round(delta_Tf, 2), round(Tf_mixture, 2)\n",
        "\n",
        "\n",
        "# Example usage:\n",
        "salt_formula = 'NaCl'\n",
        "mass_salt_g = 5.00  # grams of NaCl\n",
        "mass_solvent_g = 100.0  # grams of ice\n",
        "\n",
        "delta_Tf, Tf_mixture = calculate_freezing_point_depression(salt_formula, mass_salt_g, mass_solvent_g)\n",
        "\n",
        "print(f\"The freezing point depression of a {mass_solvent_g} g ice containing {mass_salt_g} g {salt_formula} is {delta_Tf} °C. The freezing point of the mixture is: {Tf_mixture} °C\")"
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      "source": [
        "### 2b. Do it yourself: Functionalize the above example on the calculation of the freezing point of ice with the addition of CaCl₂."
      ],
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      "cell_type": "markdown",
      "source": [
        "## 3. Plotting: CHEMICAL KINETICS\n"
      ],
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      "cell_type": "markdown",
      "source": [
        "### 3a. Example\n",
        "\n",
        "Given the following set of calculated values for the reaction rates of the decomposition of hydrogen peroxide, complete the following:\n",
        "\n",
        "a. Generate a table consisting of temperature (T in °C), reaction rate constant (k), and the natural logarithm of the reaction rate constant (ln(k)).\n",
        "b. Plot 1/T (in Kelvin) versus 𝑘 to observe the relationship.\n",
        "\n",
        "Dataset:\n",
        "- Temperature (T, °C): 0.0, 5.55555556, 11.11111111, 16.66666667, 22.22222222, 27.77777778, 33.33333333, 38.88888889, 44.44444444, 50.0\n",
        "\n",
        "- Reaction rate constant (k): 0.0435889, 0.10254397, 0.19861553, 0.29297953, 0.63305889, 1.25410921, 1.8739089, 3.53913917, 3.71370234, 7.28617176"
      ],
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      "cell_type": "code",
      "source": [
        "#(a) Generate table\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Create a DataFrame\n",
        "df = pd.DataFrame({\n",
        "    \"T (C)\": [ 0.0, 5.55555556, 11.11111111, 16.66666667, 22.22222222, 27.77777778, 33.33333333, 38.88888889, 44.44444444, 50.0],\n",
        "    \"k\": [0.0435889, 0.10254397, 0.19861553, 0.29297953, 0.63305889, 1.25410921, 1.8739089, 3.53913917, 3.71370234, 7.28617176]\n",
        "})\n",
        "\n",
        "# Convert temperature from Celsius to Kelvin and calculate 1/T\n",
        "df[\"1/T (K)\"] = 1 / (df[\"T (C)\"] + 273.15)\n",
        "\n",
        "# Calculate the natural logarithm of k\n",
        "df[\"ln_k\"] = np.log(df[\"k\"])\n",
        "\n",
        "# Display the DataFrame\n",
        "df.head()\n"
      ],
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              "       T (C)         k   1/T (K)      ln_k\n",
              "0   0.000000  0.043589  0.003661 -3.132953\n",
              "1   5.555556  0.102544  0.003588 -2.277464\n",
              "2  11.111111  0.198616  0.003518 -1.616384\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-72f223d8-8f40-46de-a845-10086a0cdadc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"T (C)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.82027974376277,\n        \"min\": 0.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          44.44444444,\n          5.55555556,\n          27.77777778\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3372367982394886,\n        \"min\": 0.0435889,\n        \"max\": 7.28617176,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3.71370234,\n          0.10254397,\n          1.25410921\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"1/T (K)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.000190399236926995,\n        \"min\": 0.0030945381401825778,\n        \"max\": 0.0036609921288669233,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.0031486696871012815,\n          0.003588016026414368,\n          0.0033230564734740855\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ln_k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6981625182799522,\n        \"min\": -3.132952748195923,\n        \"max\": 1.9859782722860013,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1.3120293143229005,\n          -2.27746359677765,\n          0.22642552773286934\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# (b). Plot data\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create a scatter plot\n",
        "plt.scatter(df[\"1/T (K)\"], df[\"ln_k\"])\n",
        "\n",
        "plt.xlabel(\"1/T (K)\", fontsize=12)\n",
        "plt.ylabel(\"ln(k)\", fontsize=12)\n",
        "plt.title(\"ln(k) vs 1/T\", fontweight=\"bold\", fontsize=14)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "id": "XR8plknbSZ_V",
        "outputId": "73d0fec6-1b5b-4ee4-d92e-6e288174af22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3b. Do it yourself, but this time use the following dataset.\n",
        "\n",
        "- Temperature (T, °C): 0.0, 5.55555556, 11.11111111, 16.66666667, 22.22222222, 27.77777778, 33.33333333, 38.88888889, 44.44444444, 50.0\n",
        "\n",
        "- Reaction rate constant (k): 716.00589111, 724.40605367, 931.30171197, 1769.122-69634, 1790.40353274, 2793.98814359, 2723.77412272, 5753.03377506, 4553.97173516, 9514.18898672\n"
      ],
      "metadata": {
        "id": "fAv56jp2H3CQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OapQ7H6XHmxj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Fitting\n"
      ],
      "metadata": {
        "id": "treyo2WyHQKb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4a. Example: Fit the example plot you have generated in the above to a linear equation and derive the activation energy."
      ],
      "metadata": {
        "id": "3U9v3EcKHuYp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import constants\n",
        "\n",
        "# Fit the data to a linear model: ln(k) = m * (1/T) + b\n",
        "slope, intercept = np.polyfit(df[\"1/T (K)\"], df[\"ln_k\"], 1)\n",
        "Ea = -slope * constants.R  # Activation energy in J/mol\n",
        "\n",
        "# Print the fitted slope\n",
        "print(f\"Fitted equation: ln(k) = {round(slope, 2)} * (1/T) + {round(intercept, 2)}\")\n",
        "print(\"The activation energy is, Ea: %s J/mol\" %(round(Ea,2)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCu9yUbuHnYK",
        "outputId": "6c755bd7-1db8-4720-c3e1-949ecb63b5e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitted equation: ln(k) = -8883.08 * (1/T) + 29.55\n",
            "The activation energy is, Ea: 73858.01 J/mol\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4b. Do it yourself: Fit the plot you have generated in the above to a linear equation and derive the activation energy."
      ],
      "metadata": {
        "id": "MEZaso6wH4l9"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UT-IfHbRHnan"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Conditional Statements"
      ],
      "metadata": {
        "id": "ZlJrT02rHQS2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5a. Example:\n",
        "\n",
        "Given the following chemical kinetics dataset, write a function to determine if the reaction is zeroth, first, or second order.\n",
        "\n",
        "Dataset\n",
        "- time = 0.0, 52.63157895, 105.26315789, 157.89473684, 210.52631579, 263.15789474, 315.78947368, 368.42105263, 421.05263158, 473.68421053, 526.31578947, 578.94736842, 631.57894737, 684.21052632, 736.84210526, 789.47368421, 842.10526316, 894.73684211, 947.36842105, 1000.0\n",
        "\n",
        "- concentration = 1.0, 0.15966387, 0.08675799, 0.05956113, 0.04534606, 0.03660886, 0.03069467, 0.02642559, 0.02319902, 0.02067465, 0.01864573, 0.01697945, 0.01558655, 0.01440485, 0.01338971, 0.01250823, 0.01173564, 0.01105294, 0.0104453, 0.00990099\n"
      ],
      "metadata": {
        "id": "Va7CjxG1Hvtu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "\n",
        "def analyze_reaction(time, concentration):\n",
        "    # Convert the lists to numpy arrays for easy manipulation\n",
        "    time = np.array(time)\n",
        "    concentration = np.array(concentration)\n",
        "\n",
        "    # Calculate the transformations for the three possible reaction orders\n",
        "    zero_order = concentration\n",
        "    first_order = np.log(concentration)\n",
        "    second_order = 1 / concentration\n",
        "\n",
        "    # Fit the data to a linear model and determine the order of the reaction\n",
        "    # For zeroth-order: y = [A]\n",
        "    slope_zeroth, intercept_zeroth, r_value_zeroth, p_value_zeroth, std_err_zeroth = stats.linregress(time, zero_order)\n",
        "\n",
        "    # For first-order: y = ln([A])\n",
        "    slope_first, intercept_first, r_value_first, p_value_first, std_err_first = stats.linregress(time, first_order)\n",
        "\n",
        "    # For second-order: y = 1/[A]\n",
        "    slope_second, intercept_second, r_value_second, p_value_second, std_err_second = stats.linregress(time, second_order)\n",
        "\n",
        "    # Determine which reaction order fits best\n",
        "    if r_value_zeroth**2 > r_value_first**2 and r_value_zeroth**2 > r_value_second**2:\n",
        "        order = \"Zeroth\"\n",
        "        plt.plot(time, zero_order, label=\"Zeroth Order\")\n",
        "        print(f\"Fitted Zeroth Order Equation: [A] = {slope_zeroth:.4f} * t + {intercept_zeroth:.4f}\")\n",
        "\n",
        "    elif r_value_first**2 > r_value_zeroth**2 and r_value_first**2 > r_value_second**2:\n",
        "        order = \"First\"\n",
        "        plt.plot(time, first_order, label=\"First Order\")\n",
        "        print(f\"Fitted First Order Equation: ln([A]) = {slope_first:.4f} * t + {intercept_first:.4f}\")\n",
        "\n",
        "    else:\n",
        "        order = \"Second\"\n",
        "        plt.scatter(time, second_order, label=\"Second Order\")\n",
        "        print(f\"Fitted Second Order Equation: 1/[A] = {slope_second:.4f} * t + {intercept_second:.4f}\")\n",
        "\n",
        "    # Plotting the data\n",
        "    plt.xlabel(\"Time (s)\", fontsize=12)\n",
        "    plt.ylabel(\"Transformed Concentration\", fontsize=12)\n",
        "    plt.title(f\"Transformation for {order} Order Reaction\", fontweight=\"bold\", fontsize=14)\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "\n",
        "# Check order of reaction\n",
        "time = [0.0, 52.63157895, 105.26315789, 157.89473684, 210.52631579,\n",
        "        263.15789474, 315.78947368, 368.42105263, 421.05263158,\n",
        "        473.68421053, 526.31578947, 578.94736842, 631.57894737,\n",
        "        684.21052632, 736.84210526, 789.47368421, 842.10526316,\n",
        "        894.73684211, 947.36842105, 1000.0]\n",
        "\n",
        "concentration = [1.0, 0.15966387, 0.08675799, 0.05956113, 0.04534606,\n",
        "                 0.03660886, 0.03069467, 0.02642559, 0.02319902, 0.02067465,\n",
        "                 0.01864573, 0.01697945, 0.01558655, 0.01440485, 0.01338971,\n",
        "                 0.01250823, 0.01173564, 0.01105294, 0.0104453, 0.00990099]\n",
        "\n",
        "# Call the function to analyze the reaction and plot the results\n",
        "analyze_reaction(time, concentration)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "VUe6FQT5Y93o",
        "outputId": "c5704e59-f842-4167-d160-c678d8f09848"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitted Second Order Equation: 1/[A] = 0.1000 * t + 1.0000\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5b. Do it yourself\n",
        "\n",
        "Given the following chemical kinetics dataset, write a function to determine if the reaction is zeroth, first, or second order.\n",
        "\n",
        "Dataset\n",
        "- time = 0.0, 52.63157895, 105.26315789, 157.89473684, 210.52631579, 263.15789474, 315.78947368, 368.42105263, 421.05263158, 473.68421053, 526.31578947, 578.94736842, 631.57894737, 684.21052632, 736.84210526, 789.47368421, 842.10526316, 894.73684211, 947.36842105, 1000.0\n",
        "\n",
        "- concentration = 1.00000000e+00, 5.17892437e-03, 2.68212576e-05, 1.38905265e-07, 7.19379861e-10, 3.72561389e-12, 1.92946726e-14, 9.99256501e-17, 5.17507385e-19, 2.68013161e-21, 1.38801989e-23, 7.18845003e-26, 3.72284391e-28, 1.92803270e-30, 9.98513555e-33, 5.17122619e-35, 2.67813893e-37, 1.38698790e-39, 7.18310543e-42, 3.72007598e-44\n"
      ],
      "metadata": {
        "id": "a-ohPZl6H6RV"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JtOESFdcHoAV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {
        "id": "IvvFMXMswkfg"
      }
    }
  ]
}