高校化学

import math

# 体心立方格子 (BCC) の充填率、配位数、格子定数、原子半径
def bcc_properties():
    a = 1  # 格子定数 (任意の単位)
    r = math.sqrt(3) * a / 4  # 原子半径
    volume_atom = 2 * (4 / 3) * math.pi * r**3  # 単位胞内の原子の体積
    volume_unit_cell = a**3  # 単位胞の体積
    packing_fraction = volume_atom / volume_unit_cell  # 充填率
    coordination_number = 8  # 配位数 (BCC)
    return packing_fraction, coordination_number, a, r

# 面心立方格子 (FCC) の充填率、配位数、格子定数、原子半径
def fcc_properties():
    a = 1  # 格子定数 (任意の単位)
    r = math.sqrt(2) * a / 4  # 原子半径
    volume_atom = 4 * (4 / 3) * math.pi * r**3  # 単位胞内の原子の体積
    volume_unit_cell = a**3  # 単位胞の体積
    packing_fraction = volume_atom / volume_unit_cell  # 充填率
    coordination_number = 12  # 配位数 (FCC)
    return packing_fraction, coordination_number, a, r

# 六方最密構造 (HCP) の充填率、配位数、格子定数、原子半径
def hcp_properties():
    a = 1  # 格子定数 (任意の単位)
    r = a / 2  # 原子半径
    c = 2 * math.sqrt(2 / 3) * a  # c 軸の長さ
    volume_atom = 6 * (4 / 3) * math.pi * r**3  # 単位胞内の原子の体積
    volume_unit_cell = (3 * math.sqrt(3) / 2) * a**2 * c  # 単位胞の体積
    packing_fraction = volume_atom / volume_unit_cell  # 充填率
    coordination_number = 12  # 配位数 (HCP)
    return packing_fraction, coordination_number, a, r, c

# 各構造のプロパティを計算
bcc_pf, bcc_cn, bcc_a, bcc_r = bcc_properties()
fcc_pf, fcc_cn, fcc_a, fcc_r = fcc_properties()
hcp_pf, hcp_cn, hcp_a, hcp_r, hcp_c = hcp_properties()

print(f"BCCの充填率: {bcc_pf:.2f}")
print(f"BCCの配位数: {bcc_cn}")
print(f"BCCの格子定数: {bcc_a:.2f}")
print(f"BCCの原子半径: {bcc_r:.2f}")

print(f"FCCの充填率: {fcc_pf:.2f}")
print(f"FCCの配位数: {fcc_cn}")
print(f"FCCの格子定数: {fcc_a:.2f}")
print(f"FCCの原子半径: {fcc_r:.2f}")

print(f"HCPの充填率: {hcp_pf:.2f}")
print(f"HCPの配位数: {hcp_cn}")
print(f"HCPの格子定数: {hcp_a:.2f}")
print(f"HCPの原子半径: {hcp_r:.2f}")
print(f"HCPのc軸長: {hcp_c:.2f}")

import numpy as np
import matplotlib.pyplot as plt

def plot_bcc():
    fig, ax = plt.subplots(figsize=(6, 6))
    
    # BCCの格子定数と原子半径
    a = 1
    r = np.sqrt(3) * a / 4
    
    # BCCの格子を描く
    # 原子の位置
    positions = [
        (0, 0), (0, 1), (1, 0), (1, 1), # 4つの角
        (0.5, 0.5) # 中心
    ]
    
    # 断面図の描画
    for pos in positions:
        circle = plt.Circle(pos, r, color='blue', fill=True, alpha=0.5)
        ax.add_artist(circle)
    
    ax.set_xlim(-0.1, 1.1)
    ax.set_ylim(-0.1, 1.1)
    ax.set_aspect('equal')
    ax.set_title('BCC - Body-Centered Cubic')
    plt.grid(True)
    plt.show()

def plot_fcc():
    fig, ax = plt.subplots(figsize=(6, 6))
    
    # FCCの格子定数と原子半径
    a = 1
    r = np.sqrt(2) * a / 4
    
    # FCCの格子を描く
    # 原子の位置
    positions = [
        (0, 0), (0, 1), (1, 0), (1, 1), # 4つの角
        (0.5, 0), (0, 0.5), (0.5, 0.5) # 面心
    ]
    
    # 断面図の描画
    for pos in positions:
        circle = plt.Circle(pos, r, color='red', fill=True, alpha=0.5)
        ax.add_artist(circle)
    
    ax.set_xlim(-0.1, 1.1)
    ax.set_ylim(-0.1, 1.1)
    ax.set_aspect('equal')
    ax.set_title('FCC - Face-Centered Cubic')
    plt.grid(True)
    plt.show()

def plot_hcp():
    fig, ax = plt.subplots(figsize=(6, 6))
    
    # HCPの格子定数と原子半径
    a = 1
    r = a / 2
    
    # HCPの格子を描く
    # 六角形の原子の位置
    angle = np.linspace(0, 2 * np.pi, 7)
    x_hex = np.cos(angle)
    y_hex = np.sin(angle)
    
    # 六角形の各層の位置
    for offset in [(0, 0), (0.5, np.sqrt(3)/2)]:
        for i in range(6):
            pos = (x_hex[i] + offset[0], y_hex[i] + offset[1])
            circle = plt.Circle(pos, r, color='green', fill=True, alpha=0.5)
            ax.add_artist(circle)
    
    ax.set_xlim(-1.5, 1.5)
    ax.set_ylim(-1.5, 1.5)
    ax.set_aspect('equal')
    ax.set_title('HCP - Hexagonal Close-Packed')
    plt.grid(True)
    plt.show()

# 各構造の断面図を描画
plot_bcc()
plot_fcc()
plot_hcp()

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

def plot_bcc():
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    
    # BCCの頂点
    vertices = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1],
                         [1, 1, 0], [1, 0, 1], [0, 1, 1], [1, 1, 1],
                         [0.5, 0.5, 0.5]])
    
    # エッジのリスト
    edges = [[vertices[i], vertices[j]] for i in range(len(vertices)) for j in range(i + 1, len(vertices))]
    
    for edge in edges:
        ax.plot3D(*zip(*edge), color="b")

    # BCCの切断面
    x = np.linspace(0, 1, 100)
    y = np.linspace(0, 1, 100)
    X, Y = np.meshgrid(x, y)
    Z = 0.5 * np.ones_like(X)  # Z = 0.5の平面

    ax.plot_surface(X, Y, Z, color='cyan', alpha=0.3, edgecolor='none')
    
    ax.set_title("Body-Centered Cubic (BCC) Lattice with Cutting Plane")
    ax.set_xlabel('X')
    ax.set_ylabel('Y')
    ax.set_zlabel('Z')
    plt.show()

def plot_fcc():
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    
    # FCCの頂点
    vertices = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 1],
                         [1, 1, 0], [1, 0, 1], [0, 1, 1], [1, 1, 1],
                         [0.5, 0.5, 0], [0.5, 0, 0.5], [0, 0.5, 0.5]])
    
    # エッジのリスト
    edges = [[vertices[i], vertices[j]] for i in range(len(vertices)) for j in range(i + 1, len(vertices))]
    
    for edge in edges:
        ax.plot3D(*zip(*edge), color="r")

    # FCCの切断面
    x = np.linspace(0, 1, 100)
    y = np.linspace(0, 1, 100)
    X, Y = np.meshgrid(x, y)
    Z = 0.5 * (X + Y)  # Z = 0.5*(X+Y)の平面

    ax.plot_surface(X, Y, Z, color='orange', alpha=0.3, edgecolor='none')
    
    ax.set_title("Face-Centered Cubic (FCC) Lattice with Cutting Plane")
    ax.set_xlabel('X')
    ax.set_ylabel('Y')
    ax.set_zlabel('Z')
    plt.show()

def plot_hcp():
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')
    
    # HCPの頂点
    vertices = np.array([[0, 0, 0], [1, 0, 0], [0.5, np.sqrt(3)/2, 0],
                         [0.5, np.sqrt(3)/6, np.sqrt(2)/3], [1.5, np.sqrt(3)/6, np.sqrt(2)/3],
                         [1, np.sqrt(3)/2, np.sqrt(2)/3], [0, np.sqrt(3)/3, 2*np.sqrt(2)/3],
                         [1, np.sqrt(3)/3, 2*np.sqrt(2)/3], [0.5, np.sqrt(3)/2, 2*np.sqrt(2)/3]])

    # エッジのリスト
    edges = [[vertices[i], vertices[j]] for i in range(len(vertices)) for j in range(i + 1, len(vertices))]
    
    for edge in edges:
        ax.plot3D(*zip(*edge), color="g")

    # HCPの切断面
    x = np.linspace(0, 1.5, 100)
    y = np.linspace(0, np.sqrt(3)/2, 100)
    X, Y = np.meshgrid(x, y)
    Z = (1/np.sqrt(2)) * (X - 0.5*Y)  # Z = (1/sqrt(2))*(X - 0.5*Y)の平面

    ax.plot_surface(X, Y, Z, color='magenta', alpha=0.3, edgecolor='none')
    
    ax.set_title("Hexagonal Close-Packed (HCP) Lattice with Cutting Plane")
    ax.set_xlabel('X')
    ax.set_ylabel('Y')
    ax.set_zlabel('Z')
    plt.show()

# 各格子構造のプロット
plot_bcc()
plot_fcc()
plot_hcp()

import math

# アボガドロ定数
NA = 6.022e23

# 気体定数
R = 8.314  # J/(mol K)

# モル数の計算
def calculate_moles(mass, molar_mass):
    return mass / molar_mass

# 分子の数の計算
def calculate_number_of_molecules(moles):
    return moles * NA

# 理想気体の体積の計算
def calculate_gas_volume(pressure, moles, temperature):
    return (moles * R * temperature) / pressure

# 入力例
mass = 10.0  # g
molar_mass = 18.015  # g/mol (水のモル質量)
pressure = 101325  # Pa (1気圧)
temperature = 298.15  # K (25°C)

# 計算
moles = calculate_moles(mass, molar_mass)
number_of_molecules = calculate_number_of_molecules(moles)
volume = calculate_gas_volume(pressure, moles, temperature)

print(f"質量: {mass} g")
print(f"モル質量: {molar_mass} g/mol")
print(f"モル数: {moles:.4f} mol")
print(f"分子の数: {number_of_molecules:.2e}")
print(f"気体の体積: {volume:.4f} m³")


# 密度と体積から質量を計算
def calculate_mass_from_density_and_volume(density, volume):
    """
    密度と体積から質量を計算する関数
    density: 密度 (g/cm³)
    volume: 体積 (cm³)
    """
    mass = density * volume
    return mass

# 質量パーセント濃度から溶質の質量を計算
def calculate_solute_mass(concentration, solution_mass):
    """
    質量パーセント濃度から溶質の質量を計算する関数
    concentration: 質量パーセント濃度 (%)
    solution_mass: 溶液の質量 (g)
    """
    solute_mass = (concentration / 100) * solution_mass
    return solute_mass

# 入力例
density = 1.0  # g/cm³ (例: 水の密度)
volume = 100.0  # cm³ (例: 水の体積)
concentration = 10.0  # % (例: 溶液の質量パーセント濃度)
solution_mass = 100.0  # g (例: 溶液の質量)

# 計算
mass = calculate_mass_from_density_and_volume(density, volume)
solute_mass = calculate_solute_mass(concentration, solution_mass)

print(f"密度: {density} g/cm³")
print(f"体積: {volume} cm³")
print(f"物質の質量: {mass} g")

print(f"質量パーセント濃度: {concentration} %")
print(f"溶液の質量: {solution_mass} g")
print(f"溶質の質量: {solute_mass} g")

import numpy as np
import matplotlib.pyplot as plt

# pHの計算関数
def calculate_pH(h_concentration):
    """
    水素イオン濃度からpHを計算する関数
    h_concentration: 水素イオン濃度 (mol/L)
    """
    return -np.log10(h_concentration)

# 中和滴定の計算関数
def neutralization_titration(C_acid, V_acid, C_base, V_base):
    """
    中和滴定の計算を行う関数
    C_acid: 酸の濃度 (mol/L)
    V_acid: 酸の体積 (L)
    C_base: 塩基の濃度 (mol/L)
    V_base: 塩基の体積 (L)
    """
    n_acid = C_acid * V_acid
    n_base = C_base * V_base
    
    # 中和点では n_acid = n_base
    if n_base <= n_acid:
        remaining_acid = n_acid - n_base
        h_concentration = remaining_acid / (V_acid + V_base)
        return calculate_pH(h_concentration)
    else:
        excess_base = n_base - n_acid
        oh_concentration = excess_base / (V_acid + V_base)
        return 14 - calculate_pH(oh_concentration)

# 中和滴定曲線のプロット関数
def plot_titration_curve(C_acid, V_acid, C_base, V_base_increment, num_points=100):
    """
    中和滴定曲線をプロットする関数
    C_acid: 酸の濃度 (mol/L)
    V_acid: 酸の体積 (L)
    C_base: 塩基の濃度 (mol/L)
    V_base_increment: 塩基の体積の増加量 (L)
    num_points: プロットする点の数
    """
    V_base_values = np.linspace(0, V_base_increment, num_points)
    pH_values = []
    
    for V_base in V_base_values:
        pH = neutralization_titration(C_acid, V_acid, C_base, V_base)
        pH_values.append(pH)
    
    plt.plot(V_base_values, pH_values)
    plt.xlabel('Volume of base added (L)')
    plt.ylabel('pH')
    plt.title('Titration Curve')
    plt.grid(True)
    plt.show()

# 入力例
C_acid = 0.1  # 酸の濃度 (mol/L)
V_acid = 0.1  # 酸の体積 (L)
C_base = 0.1  # 塩基の濃度 (mol/L)
V_base_increment = 0.2  # 塩基の体積の増加量 (L)

# 中和滴定曲線のプロット
plot_titration_curve(C_acid, V_acid, C_base, V_base_increment)




def calculate_neutralization(na2co3_moles, hcl_moles):
    # 第一段階の反応: Na2CO3 + 2HCl → NaHCO3 + NaCl + H2O
    na2co3_needed_for_first_step = na2co3_moles
    hcl_needed_for_first_step = 2 * na2co3_needed_for_first_step
    
    if hcl_moles >= hcl_needed_for_first_step:
        na2co3_remaining_after_first_step = 0
        hcl_remaining_after_first_step = hcl_moles - hcl_needed_for_first_step
        na_hco3_formed = na2co3_needed_for_first_step
    else:
        na2co3_remaining_after_first_step = na2co3_moles - hcl_moles / 2
        na_hco3_formed = hcl_moles / 2
        hcl_remaining_after_first_step = 0
    
    # 第二段階の反応: NaHCO3 + HCl → NaCl + H2O + CO2
    na_hco3_needed_for_second_step = na_hco3_formed
    hcl_needed_for_second_step = na_hco3_needed_for_second_step
    
    if hcl_remaining_after_first_step >= hcl_needed_for_second_step:
        hcl_remaining_after_second_step = hcl_remaining_after_first_step - hcl_needed_for_second_step
        co2_formed = na_hco3_needed_for_second_step
        na_cl_formed = na_hco3_needed_for_second_step
        h2o_formed = na_hco3_needed_for_second_step
    else:
        na_cl_formed = hcl_remaining_after_first_step
        h2o_formed = hcl_remaining_after_first_step
        co2_formed = hcl_remaining_after_first_step
        hcl_remaining_after_second_step = 0
    
    return {
        'Na2CO3_remaining': na2co3_remaining_after_first_step,
        'HCl_remaining': hcl_remaining_after_second_step,
        'NaHCO3_formed': na_hco3_formed,
        'NaCl_formed': na_cl_formed,
        'H2O_formed': h2o_formed,
        'CO2_formed': co2_formed
    }

# 例として、炭酸ナトリウムと塩酸の初期モル数を設定
na2co3_moles = 1.0  # 例えば 1 mol
hcl_moles = 2.5     # 例えば 2.5 mol

results = calculate_neutralization(na2co3_moles, hcl_moles)
for substance, amount in results.items():
    print(f"{substance}: {amount} mol")

def calculate_enthalpy_change(na2co3_moles, hcl_moles, delta_h1, delta_h2):
    # 第一段階反応に必要なHClの量
    hcl_needed_for_first_step = 2 * na2co3_moles
    
    if hcl_moles >= hcl_needed_for_first_step:
        na2co3_remaining = 0
        hcl_remaining_after_first_step = hcl_moles - hcl_needed_for_first_step
        na_hco3_formed = na2co3_moles
    else:
        na2co3_remaining = na2co3_moles - hcl_moles / 2
        na_hco3_formed = hcl_moles / 2
        hcl_remaining_after_first_step = 0
    
    # 第二段階反応に必要なHClの量
    hcl_needed_for_second_step = na_hco3_formed
    
    if hcl_remaining_after_first_step >= hcl_needed_for_second_step:
        hcl_remaining_after_second_step = hcl_remaining_after_first_step - hcl_needed_for_second_step
        total_enthalpy_change = delta_h1 * na2co3_moles + delta_h2 * na_hco3_formed
    else:
        total_enthalpy_change = delta_h1 * na2co3_moles + delta_h2 * hcl_remaining_after_first_step
        hcl_remaining_after_second_step = 0
    
    return {
        'Na2CO3_remaining': na2co3_remaining,
        'HCl_remaining': hcl_remaining_after_second_step,
        'Total_enthalpy_change': total_enthalpy_change
    }

# 例としてのデータ
na2co3_moles = 1.0  # 1 mol
hcl_moles = 2.5     # 2.5 mol
delta_h1 = -1000    # 第一段階のエンタルピー変化 (例: -1000 kJ/mol)
delta_h2 = -200     # 第二段階のエンタルピー変化 (例: -200 kJ/mol)

results = calculate_enthalpy_change(na2co3_moles, hcl_moles, delta_h1, delta_h2)
for substance, amount in results.items():
    print(f"{substance}: {amount} kJ")

#C H : C O O と H O の 反 応 の平 衡式 

def calculate_acid_dissociation_constant(concentration_acetic_acid, concentration_acetate_ion, concentration_hydronium_ion):
    """
    酢酸と水の反応における酸解離定数 (Ka) を計算する関数
    :param concentration_acetic_acid: 酢酸の平衡時濃度 (M)
    :param concentration_acetate_ion: 酢酸イオンの平衡時濃度 (M)
    :param concentration_hydronium_ion: 水素イオンの平衡時濃度 (M)
    :return: 酸解離定数 (Ka)
    """
    # 酸解離定数 Ka の計算
    ka = (concentration_acetate_ion * concentration_hydronium_ion) / concentration_acetic_acid
    return ka

# 例としてのデータ
concentration_acetic_acid = 0.1       # 酢酸の平衡時濃度 (M)
concentration_acetate_ion = 0.01      # 酢酸イオンの平衡時濃度 (M)
concentration_hydronium_ion = 0.01    # 水素イオンの平衡時濃度 (M)

# 酸解離定数を計算
ka = calculate_acid_dissociation_constant(concentration_acetic_acid, concentration_acetate_ion, concentration_hydronium_ion)

print(f"酸解離定数 (Ka): {ka:.2e} M")

import math

def calculate_ka(concentration_acid, concentration_conjugate_base, concentration_hydronium_ion):
    """
    酸解離定数 (Ka) を計算する関数
    :param concentration_acid: 酸の平衡時濃度 (M)
    :param concentration_conjugate_base: 酸の解離生成物の濃度 (M)
    :param concentration_hydronium_ion: 水素イオンの平衡時濃度 (M)
    :return: 酸解離定数 (Ka)
    """
    ka = (concentration_conjugate_base * concentration_hydronium_ion) / concentration_acid
    return ka

def calculate_kb(concentration_base, concentration_conjugate_acid, concentration_hydroxide_ion):
    """
    塩基解離定数 (Kb) を計算する関数
    :param concentration_base: 塩基の平衡時濃度 (M)
    :param concentration_conjugate_acid: 塩基の解離生成物の濃度 (M)
    :param concentration_hydroxide_ion: 水酸化物イオンの平衡時濃度 (M)
    :return: 塩基解離定数 (Kb)
    """
    kb = (concentration_conjugate_acid * concentration_hydroxide_ion) / concentration_base
    return kb

def calculate_ph(concentration_hydronium_ion):
    """
    pH を計算する関数
    :param concentration_hydronium_ion: 水素イオンの濃度 (M)
    :return: pH
    """
    return -math.log10(concentration_hydronium_ion)

def calculate_poh(concentration_hydroxide_ion):
    """
    pOH を計算する関数
    :param concentration_hydroxide_ion: 水酸化物イオンの濃度 (M)
    :return: pOH
    """
    return -math.log10(concentration_hydroxide_ion)

def calculate_ph_and_poh(concentration_hydronium_ion, concentration_hydroxide_ion):
    """
    pH と pOH を計算する関数
    :param concentration_hydronium_ion: 水素イオンの濃度 (M)
    :param concentration_hydroxide_ion: 水酸化物イオンの濃度 (M)
    :return: pH と pOH のタプル
    """
    ph = calculate_ph(concentration_hydronium_ion)
    poh = calculate_poh(concentration_hydroxide_ion)
    return ph, poh

def calculate_kw(concentration_hydronium_ion, concentration_hydroxide_ion):
    """
    水のイオン生成定数 (Kw) を計算する関数
    :param concentration_hydronium_ion: 水素イオンの濃度 (M)
    :param concentration_hydroxide_ion: 水酸化物イオンの濃度 (M)
    :return: 水のイオン生成定数 (Kw)
    """
    return concentration_hydronium_ion * concentration_hydroxide_ion

# 使用例
acetic_acid_concentration = 0.1  # 酢酸の濃度 (M)
acetate_ion_concentration = 0.01  # 酢酸イオンの濃度 (M)
hydronium_ion_concentration = 0.01  # 水素イオンの濃度 (M)
base_concentration = 0.1  # 塩基の濃度 (M)
conjugate_acid_concentration = 0.01  # 塩基の解離生成物の濃度 (M)
hydroxide_ion_concentration = 0.01  # 水酸化物イオンの濃度 (M)

ka = calculate_ka(acetic_acid_concentration, acetate_ion_concentration, hydronium_ion_concentration)
print(f"酸解離定数 (Ka): {ka:.2e} M")

kb = calculate_kb(base_concentration, conjugate_acid_concentration, hydroxide_ion_concentration)
print(f"塩基解離定数 (Kb): {kb:.2e} M")

ph, poh = calculate_ph_and_poh(hydronium_ion_concentration, hydroxide_ion_concentration)
print(f"pH: {ph:.2f}")
print(f"pOH: {poh:.2f}")

kw = calculate_kw(hydronium_ion_concentration, hydroxide_ion_concentration)
print(f"水のイオン生成定数 (Kw): {kw:.2e}")

import math

# ヘスの法則
def total_enthalpy_change(*enthalpies):
    """
    ヘスの法則に基づくエンタルピー変化の計算
    :param enthalpies: 反応のエンタルピー変化
    :return: 総エンタルピー変化
    """
    return sum(enthalpies)

# ガスの状態方程式
def ideal_gas_law(pressure, volume, moles, temperature):
    """
    理想気体の状態方程式を計算する関数
    :param pressure: 圧力 (Pa)
    :param volume: 体積 (m³)
    :param moles: モル数
    :param temperature: 温度 (K)
    :return: 気体定数 (R) の計算
    """
    R = 8.314  # J/(mol·K)
    return (pressure * volume) / (moles * temperature)

# 電気分解の法則
def electrolytic_mass(charge, molar_mass, electrons):
    """
    電気分解による生成物の質量を計算する関数
    :param charge: 電荷量 (C)
    :param molar_mass: モル質量 (g/mol)
    :param electrons: 電気分解に必要な電子の数
    :return: 生成物の質量 (g)
    """
    F = 96485  # C/mol
    return (charge / F) * (molar_mass / electrons)

# 反応速度方程式
def reaction_rate(k, *concentrations):
    """
    反応速度を計算する関数
    :param k: 反応速度定数
    :param concentrations: 各反応物の濃度
    :return: 反応速度
    """
    rate = k
    for concentration in concentrations:
        rate *= concentration
    return rate

# ドルトンの法則
def total_pressure(*partial_pressures):
    """
    ドルトンの法則に基づく全圧の計算
    :param partial_pressures: 各成分気体の部分圧
    :return: 全圧
    """
    return sum(partial_pressures)

# 使用例
enthalpy_changes = [100, -50, 25]  # 例: [反応1, 反応2, 反応3] のエンタルピー変化
print(f"総エンタルピー変化: {total_enthalpy_change(*enthalpy_changes)} kJ")

pressure = 101325  # Pa
volume = 0.1  # m³
moles = 1  # mol
temperature = 298  # K
print(f"理想気体の計算結果: {ideal_gas_law(pressure, volume, moles, temperature):.2f} J/(mol·K)")

charge = 96500  # C
molar_mass = 63.5  # g/mol (例: 銅)
electrons = 2  # 例: 銅の電気分解
print(f"電気分解による生成物の質量: {electrolytic_mass(charge, molar_mass, electrons):.2f} g")

k = 0.1  # 反応速度定数
concentrations = [0.5, 0.2]  # 各反応物の濃度
print(f"反応速度: {reaction_rate(k, *concentrations):.2f} mol/(L·s)")

partial_pressures = [200, 300]  # 各成分気体の部分圧 (Pa)
print(f"全圧: {total_pressure(*partial_pressures)} Pa")

import math

# 反応熱の計算
def reaction_enthalpy_change(standard_enthalpy_products, standard_enthalpy_reactants):
    """
    反応のエンタルピー変化を計算する関数
    :param standard_enthalpy_products: 生成物の標準生成エンタルピー (kJ/mol)
    :param standard_enthalpy_reactants: 反応物の標準生成エンタルピー (kJ/mol)
    :return: 反応のエンタルピー変化 (kJ)
    """
    return sum(standard_enthalpy_products) - sum(standard_enthalpy_reactants)

# ボイルの法則
def boyles_law(pressure1, volume1, pressure2):
    """
    ボイルの法則に基づく体積の計算
    :param pressure1: 初期圧力 (atm)
    :param volume1: 初期体積 (L)
    :param pressure2: 最終圧力 (atm)
    :return: 最終体積 (L)
    """
    return (pressure1 * volume1) / pressure2

# シャルルの法則
def charles_law(volume1, temperature1, temperature2):
    """
    シャルルの法則に基づく体積の計算
    :param volume1: 初期体積 (L)
    :param temperature1: 初期温度 (K)
    :param temperature2: 最終温度 (K)
    :return: 最終体積 (L)
    """
    return (volume1 * temperature2) / temperature1

# 理想気体のモル体積
def ideal_gas_molar_volume():
    """
    理想気体のモル体積 (標準状態) を返す関数
    :return: モル体積 (L/mol)
    """
    return 22.4

# 使用例
standard_enthalpy_products = [0]  # 例: 生成物の標準生成エンタルピー (kJ/mol)
standard_enthalpy_reactants = [-200]  # 例: 反応物の標準生成エンタルピー (kJ/mol)
print(f"反応のエンタルピー変化: {reaction_enthalpy_change(standard_enthalpy_products, standard_enthalpy_reactants):.2f} kJ")

pressure1 = 1.0  # atm
volume1 = 10.0  # L
pressure2 = 2.0  # atm
print(f"最終体積 (ボイルの法則): {boyles_law(pressure1, volume1, pressure2):.2f} L")

volume1 = 10.0  # L
temperature1 = 273  # K
temperature2 = 373  # K
print(f"最終体積 (シャルルの法則): {charles_law(volume1, temperature1, temperature2):.2f} L")

print(f"理想気体のモル体積: {ideal_gas_molar_volume()} L/mol")

import math

# エネルギーと仕事の関係
def work_energy(energy_change):
    """
    エネルギー変化に基づく仕事の計算
    :param energy_change: エネルギーの変化 (J)
    :return: 仕事 (J)
    """
    return energy_change

# ギブズ自由エネルギーの計算
def gibbs_free_energy(enthalpy_change, temperature, entropy_change):
    """
    ギブズ自由エネルギーの計算
    :param enthalpy_change: エンタルピー変化 (J)
    :param temperature: 絶対温度 (K)
    :param entropy_change: エントロピー変化 (J/K)
    :return: ギブズ自由エネルギー変化 (J)
    """
    return enthalpy_change - (temperature * entropy_change)

# アボガドロの法則
def volume_from_moles(moles, molar_volume):
    """
    アボガドロの法則に基づく体積の計算
    :param moles: モル数 (mol)
    :param molar_volume: モル体積 (L/mol)
    :return: 体積 (L)
    """
    return moles * molar_volume

# ヘンリーの法則
def henrys_law(concentration, henry_constant, pressure):
    """
    ヘンリーの法則に基づく溶解度の計算
    :param concentration: 溶解度 (mol/L)
    :param henry_constant: ヘンリー定数 (mol/(L·atm))
    :param pressure: 気体の部分圧 (atm)
    :return: 溶解度 (mol/L)
    """
    return henry_constant * pressure

# オストワルドの希釈法則
def dissociation_degree(acid_dissociation_constant, concentration):
    """
    オストワルドの希釈法則に基づく電離度の計算
    :param acid_dissociation_constant: 酸解離定数 (mol/L)
    :param concentration: 電解質の濃度 (mol/L)
    :return: 電離度
    """
    return acid_dissociation_constant / concentration

# 使用例
energy_change = 500  # J
print(f"仕事: {work_energy(energy_change)} J")

enthalpy_change = -1000  # J
temperature = 298  # K
entropy_change = 10  # J/K
print(f"ギブズ自由エネルギー変化: {gibbs_free_energy(enthalpy_change, temperature, entropy_change)} J")

moles = 2  # mol
molar_volume = 22.4  # L/mol
print(f"体積: {volume_from_moles(moles, molar_volume)} L")

concentration = 0.1  # mol/L
henry_constant = 3.5e-4  # mol/(L·atm)
pressure = 1.0  # atm
print(f"溶解度: {henrys_law(concentration, henry_constant, pressure)} mol/L")

acid_dissociation_constant = 1e-5  # mol/L
concentration = 0.01  # mol/L
print(f"電離度: {dissociation_degree(acid_dissociation_constant, concentration)}")

import math

# 反応速度の計算
def reaction_rate(k, concentration_A, concentration_B, order_A, order_B):
    """
    反応速度を計算する関数
    :param k: 反応速度定数
    :param concentration_A: 反応物Aの濃度 (mol/L)
    :param concentration_B: 反応物Bの濃度 (mol/L)
    :param order_A: 反応物Aの反応次数
    :param order_B: 反応物Bの反応次数
    :return: 反応速度
    """
    return k * (concentration_A ** order_A) * (concentration_B ** order_B)

# 半減期の計算
def half_life(order, rate_constant, initial_concentration=None):
    """
    半減期を計算する関数
    :param order: 反応の次数
    :param rate_constant: 反応速度定数 (s^-1)
    :param initial_concentration: 初期濃度 (mol/L) (二次反応用)
    :return: 半減期 (s)
    """
    if order == 1:
        return math.log(2) / rate_constant
    elif order == 2:
        return 1 / (rate_constant * initial_concentration)
    else:
        raise ValueError("Unsupported reaction order")

# ファラデーの法則
def faradays_law(charge, molar_mass, electrons):
    """
    ファラデーの法則に基づく生成物の質量の計算
    :param charge: 電荷量 (C)
    :param molar_mass: モル質量 (g/mol)
    :param electrons: 電子の数
    :return: 生成物の質量 (g)
    """
    F = 96485  # ファラデー定数 (C/mol)
    return (charge / F) * molar_mass / electrons

# ヘスの法則
def hess_law(enthalpy_changes):
    """
    ヘスの法則に基づくエンタルピー変化の計算
    :param enthalpy_changes: 各ステップのエンタルピー変化 (kJ)
    :return: 全体のエンタルピー変化 (kJ)
    """
    return sum(enthalpy_changes)

# 使用例
k = 0.1  # 反応速度定数
concentration_A = 0.5  # mol/L
concentration_B = 0.5  # mol/L
order_A = 1
order_B = 1
print(f"反応速度: {reaction_rate(k, concentration_A, concentration_B, order_A, order_B)} mol/L/s")

order = 1
rate_constant = 0.02  # s^-1
print(f"一次反応の半減期: {half_life(order, rate_constant)} s")

charge = 96500  # C
molar_mass = 63.5  # g/mol (Cu)
electrons = 2
print(f"生成物の質量 (ファラデーの法則): {faradays_law(charge, molar_mass, electrons)} g")

enthalpy_changes = [-200, 50, 10]  # 各ステップのエンタルピー変化 (kJ)
print(f"全体のエンタルピー変化 (ヘスの法則): {hess_law(enthalpy_changes)} kJ")

import math

# エンタルピー変化の計算
def enthalpy_change(delta_e, pressure, delta_v):
    """
    エンタルピー変化を計算する関数
    :param delta_e: 内部エネルギー変化 (J)
    :param pressure: 圧力 (Pa)
    :param delta_v: 体積変化 (m³)
    :return: エンタルピー変化 (J)
    """
    return delta_e + (pressure * delta_v)

# 平衡定数の計算
def equilibrium_constant(concentrations, coefficients):
    """
    平衡定数を計算する関数
    :param concentrations: 各成分の濃度の辞書
    :param coefficients: 反応物と生成物の係数の辞書
    :return: 平衡定数
    """
    numerator = 1
    denominator = 1
    for species, coef in coefficients.items():
        if coef > 0:  # 生成物
            numerator *= concentrations[species] ** coef
        else:  # 反応物
            denominator *= concentrations[species] ** abs(coef)
    return numerator / denominator

# Henderson-Hasselbalchの式
def henderson_hasselbalch(pKa, concentration_A_minus, concentration_HA):
    """
    Henderson-Hasselbalchの式に基づくpHの計算
    :param pKa: 酸のpKa
    :param concentration_A_minus: 塩基の濃度 (mol/L)
    :param concentration_HA: 酸の濃度 (mol/L)
    :return: pH
    """
    return pKa + math.log10(concentration_A_minus / concentration_HA)

# 蒸気圧降下の計算
def raoult_law(mole_fraction_solvent, vapor_pressure_pure_solvent):
    """
    ラウールの法則に基づく蒸気圧降下の計算
    :param mole_fraction_solvent: 溶媒のモル分率
    :param vapor_pressure_pure_solvent: 純粋な溶媒の蒸気圧 (Pa)
    :return: 蒸気圧降下 (Pa)
    """
    return mole_fraction_solvent * vapor_pressure_pure_solvent

# 使用例
delta_e = 500  # J
pressure = 1000  # Pa
delta_v = 0.01  # m³
print(f"エンタルピー変化: {enthalpy_change(delta_e, pressure, delta_v)} J")

concentrations = {'C': 0.1, 'D': 0.2, 'A': 0.3, 'B': 0.4}
coefficients = {'C': 2, 'D': 3, 'A': -1, 'B': -2}
print(f"平衡定数: {equilibrium_constant(concentrations, coefficients)}")

pKa = 4.75
concentration_A_minus = 0.1  # mol/L
concentration_HA = 0.05  # mol/L
print(f"pH: {henderson_hasselbalch(pKa, concentration_A_minus, concentration_HA)}")

mole_fraction_solvent = 0.8
vapor_pressure_pure_solvent = 760  # Pa
print(f"蒸気圧降下: {raoult_law(mole_fraction_solvent, vapor_pressure_pure_solvent)} Pa")

import math

# ミンコフスキーの法則（DNA複製速度）
def dna_replication_speed(L, t):
    """
    DNA複製の速度を計算する
    :param L: 複製するDNAの長さ (bp)
    :param t: 複製にかかる時間 (s)
    :return: 複製速度 (bp/s)
    """
    return L / t

# ミンコフスキーの法則（生物学的変化率）
def biological_change_rate(delta_N, delta_t):
    """
    生物学的変化率を計算する
    :param delta_N: 特性の変化量
    :param delta_t: 時間の経過 (時間)
    :return: 変化率 (個体数/時間)
    """
    return delta_N / delta_t

# ポピュレーション増殖のロジスティックモデル
def logistic_growth(N0, K, r, t):
    """
    ポピュレーションのロジスティック成長を計算する
    :param N0: 初期個体数
    :param K: 環境収容力
    :param r: 成長率
    :param t: 時間
    :return: 時間 t における個体数
    """
    return K / (1 + ((K - N0) / N0) * math.exp(-r * t))

# 酵素反応のミハエリス・メンテン式
def michaelis_menten(V_max, S, K_m):
    """
    ミハエリス・メンテン式を用いて反応速度を計算する
    :param V_max: 最大反応速度
    :param S: 基質濃度
    :param K_m: ミハエリス定数
    :return: 反応速度
    """
    return (V_max * S) / (K_m + S)

# 呼吸商（RQ）
def respiratory_quotient(CO2_produced, O2_consumed):
    """
    呼吸商を計算する
    :param CO2_produced: 二酸化炭素の発生量
    :param O2_consumed: 酸素の消費量
    :return: 呼吸商
    """
    return CO2_produced / O2_consumed

# 使用例
L = 1e6  # DNA長さ (bp)
t = 3600  # 時間 (s)
print(f"DNA複製速度: {dna_replication_speed(L, t)} bp/s")

delta_N = 100  # 特性の変化量
delta_t = 10  # 時間 (時間)
print(f"生物学的変化率: {biological_change_rate(delta_N, delta_t)} 個体数/時間")

N0 = 100  # 初期個体数
K = 1000  # 環境収容力
r = 0.1  # 成長率
t = 5  # 時間
print(f"ロジスティック成長に基づく個体数: {logistic_growth(N0, K, r, t)}")

V_max = 10  # 最大反応速度
S = 5  # 基質濃度
K_m = 2  # ミハエリス定数
print(f"ミハエリス・メンテン式に基づく反応速度: {michaelis_menten(V_max, S, K_m)}")

CO2_produced = 50  # 二酸化炭素の発生量
O2_consumed = 60  # 酸素の消費量
print(f"呼吸商: {respiratory_quotient(CO2_produced, O2_consumed)}")


# ミトーシスと減数分裂の計算
def cell_division_mitosis(divisions):
    """
    ミトーシスの細胞分裂数を計算する
    :param divisions: 分裂回数
    :return: 細胞数
    """
    return 2 ** divisions

def cell_division_meiosis(divisions):
    """
    減数分裂の細胞分裂数を計算する
    :param divisions: 分裂回数
    :return: 細胞数
    """
    return (2 ** divisions) * (2 ** divisions)

# 遺伝の統計
def genetic_probability(prob_Aa):
    """
    遺伝形質の確率を計算する
    :param prob_Aa: 優性・劣性遺伝子の確率
    :return: 確率
    """
    return prob_Aa

# 呼吸のカロリメトリー
def calorimetry(C, delta_T):
    """
    カロリメトリーを計算する
    :param C: 比熱容量 (J/g·K)
    :param delta_T: 温度変化 (K)
    :return: 発生したエネルギー (J)
    """
    return C * delta_T

# 生態系のバイオマスピラミッド
def biomass_efficiency(biomass_upper, biomass_lower):
    """
    生態系のバイオマス効率を計算する
    :param biomass_upper: 上位トロフのバイオマス
    :param biomass_lower: 下位トロフのバイオマス
    :return: バイオマス効率 (%)
    """
    return (biomass_upper / biomass_lower) * 100

# 酵素の反応速度
def enzyme_reaction_rate(V_max, S, K_m):
    """
    酵素の反応速度を計算する
    :param V_max: 最大反応速度
    :param S: 基質濃度
    :param K_m: ミハエリス定数
    :return: 反応速度
    """
    return (V_max * S) / (K_m + S)

# 使用例
divisions = 3
print(f"ミトーシスの細胞数: {cell_division_mitosis(divisions)}")

divisions = 2
print(f"減数分裂の細胞数: {cell_division_meiosis(divisions)}")

prob_Aa = 0.5
print(f"遺伝形質の確率: {genetic_probability(prob_Aa)}")

C = 4.18  # 比熱容量 (J/g·K)
delta_T = 5  # 温度変化 (K)
print(f"カロリメトリーによるエネルギー: {calorimetry(C, delta_T)} J")

biomass_upper = 50  # 上位トロフのバイオマス
biomass_lower = 200  # 下位トロフのバイオマス
print(f"バイオマス効率: {biomass_efficiency(biomass_upper, biomass_lower)}%")

V_max = 10  # 最大反応速度
S = 5  # 基質濃度
K_m = 2  # ミハエリス定数
print(f"酵素の反応速度: {enzyme_reaction_rate(V_max, S, K_m)}")

import numpy as np
import matplotlib.pyplot as plt

# 定数
k_B = 1.38e-23  # ボルツマン定数 (J/K)

# エネルギーの範囲
E = np.linspace(0, 5e-21, 1000)  # エネルギーの範囲 (J)

# 温度設定 (K)
T1 = 300  # 温度 1 (K)
T2 = 600  # 温度 2 (K)
T3 = 1200  # 温度 3 (K)

# Maxwell-Boltzmann分布の計算
def maxwell_boltzmann(E, T):
    return np.sqrt(2 / np.pi) * (1 / (k_B * T))**(3 / 2) * E**(1 / 2) * np.exp(-E / (k_B * T))

# 分布の計算
f_E_T1 = maxwell_boltzmann(E, T1)
f_E_T2 = maxwell_boltzmann(E, T2)
f_E_T3 = maxwell_boltzmann(E, T3)

# プロット
plt.figure(figsize=(10, 6))
plt.plot(E * 1e21, f_E_T1, label=f'T = {T1} K', color='blue')
plt.plot(E * 1e21, f_E_T2, label=f'T = {T2} K', color='green')
plt.plot(E * 1e21, f_E_T3, label=f'T = {T3} K', color='red')

# ラベルとタイトル
plt.xlabel('Energy (eV)')
plt.ylabel('Probability Density')
plt.title('Energy Distribution of Liquid Molecules')
plt.legend()
plt.grid(True)

plt.show()

import numpy as np
import matplotlib.pyplot as plt

# 定数
R = 8.314  # 気体定数 (J/(mol·K))
delta_H_vap = 40e3  # 蒸発熱 (J/mol)

# 温度範囲 (K)
T = np.linspace(273, 373, 100)  # 0°C ～ 100°C の範囲

# 初期蒸気圧 (常温の蒸気圧を仮定)
T0 = 298  # 初期温度 (K)
P0 = 1e5  # 初期蒸気圧 (Pa)

# 蒸気圧の計算
P = P0 * np.exp((delta_H_vap / R) * (1/T0 - 1/T))

# プロット
plt.figure(figsize=(10, 6))
plt.plot(T, P / 1e5, label='Vapor Pressure Curve')
plt.xlabel('Temperature (K)')
plt.ylabel('Vapor Pressure (bar)')
plt.title('Vapor Pressure Curve')
plt.grid(True)
plt.legend()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# 冷却データ (サンプルデータ)
time = np.linspace(0, 50, 500)  # 時間
temperature = np.piecewise(time, 
                           [time < 20, (time >= 20) & (time < 40), time >= 40], 
                           [lambda t: 100 - 1.5 * t, 
                            lambda t: 70 - 0.5 * (t - 20), 
                            lambda t: 60])

# 定数
Kf = 1.86  # 凝固点降下定数 (°C·kg/mol) for water
i = 1  # ヴァンホッフ因子 (非電解質)
m = np.linspace(0, 1, 100)  # 溶質のモル濃度 (mol/kg)

# 凝固点降下の計算
delta_T_f = i * Kf * m

# プロット
plt.figure(figsize=(12, 6))

# 冷却曲線のプロット
plt.subplot(1, 2, 1)
plt.plot(time, temperature, label='Cooling Curve')
plt.axhline(y=60, color='red', linestyle='--', label='Freezing Point')
plt.xlabel('Time (s)')
plt.ylabel('Temperature (°C)')
plt.title('Cooling Curve')
plt.legend()
plt.grid(True)

# 様固点降下のプロット
plt.subplot(1, 2, 2)
plt.plot(m, delta_T_f, label='Freezing Point Depression')
plt.xlabel('Molality (mol/kg)')
plt.ylabel('Freezing Point Depression (°C)')
plt.title('Freezing Point Depression')
plt.legend()
plt.grid(True)

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Constants
k = 0.1  # Reaction rate constant
t_max = 50  # Simulation time

# Time array
t = np.linspace(0, t_max, 100)

# Initial concentrations
A0 = 1.0  # Initial concentration of hydrogen (H2)
B0 = 1.0  # Initial concentration of iodine (I2)

# Initial concentration of product
C0 = 0.0  # Initial concentration of hydrogen iodide (HI)

# Calculate concentrations
A = A0 * np.exp(-k * t)
B = B0 * np.exp(-k * t)
C = 2 * (A0 - A)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(t, A, label='[H2]')
plt.plot(t, B, label='[I2]')
plt.plot(t, C, label='[HI]')
plt.xlabel('Time')
plt.ylabel('Concentration')
plt.title('Concentration Changes in Hydrogen Iodide Formation Reaction')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Constants
A = 1e13  # Pre-exponential factor (s^-1)
Ea = 25000  # Activation energy (J/mol), just an example value
R = 8.314  # Universal gas constant (J/(mol·K))

# Temperature range
T_min = 300  # Minimum temperature in Kelvin (approximately 27°C)
T_max = 600  # Maximum temperature in Kelvin (approximately 327°C)
temperatures = np.linspace(T_min, T_max, 100)  # Temperature array

# Calculate reaction rates using the Arrhenius equation
reaction_rates = A * np.exp(-Ea / (R * temperatures))

# Normalize the reaction rates at 356°C (629 K) to 1
T_ref = 356 + 273.15  # Reference temperature in Kelvin
reaction_rates_ref = A * np.exp(-Ea / (R * T_ref))
normalized_rates = reaction_rates / reaction_rates_ref

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(temperatures - 273.15, normalized_rates, label='Normalized Reaction Rate')
plt.xlabel('Temperature (°C)')
plt.ylabel('Normalized Reaction Rate')
plt.title('Temperature Dependence of the Reaction Rate')
plt.legend()
plt.grid(True)
plt.show()

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

def plot_molecule_3d(molecule):
    fig = plt.figure()
    ax = fig.add_subplot(111, projection='3d')

    if molecule == 'H2O':
        # H2Oの折れ線形
        O = np.array([0, 0, 0])
        H1 = np.array([-0.5, 0.5, 0])
        H2 = np.array([0.5, 0.5, 0])
        ax.plot([O[0], H1[0]], [O[1], H1[1]], [O[2], H1[2]], 'ro-')
        ax.plot([O[0], H2[0]], [O[1], H2[1]], [O[2], H2[2]], 'ro-')
        ax.text(O[0], O[1], O[2], 'O', fontsize=12, ha='center', va='center', color='black')
        ax.text(H1[0], H1[1], H1[2], 'H', fontsize=12, ha='center', va='center', color='black')
        ax.text(H2[0], H2[1], H2[2], 'H', fontsize=12, ha='center', va='center', color='black')
        ax.set_title('H2O Molecule')

    elif molecule == 'NH3':
        # NH3の三角錐形
        N = np.array([0, 0, 0])
        H1 = np.array([-0.6, 0.8, 0])
        H2 = np.array([0.6, 0.8, 0])
        H3 = np.array([0, -1, 0])
        ax.plot([N[0], H1[0]], [N[1], H1[1]], [N[2], H1[2]], 'ro-')
        ax.plot([N[0], H2[0]], [N[1], H2[1]], [N[2], H2[2]], 'ro-')
        ax.plot([N[0], H3[0]], [N[1], H3[1]], [N[2], H3[2]], 'ro-')
        ax.text(N[0], N[1], N[2], 'N', fontsize=12, ha='center', va='center', color='black')
        ax.text(H1[0], H1[1], H1[2], 'H', fontsize=12, ha='center', va='center', color='black')
        ax.text(H2[0], H2[1], H2[2], 'H', fontsize=12, ha='center', va='center', color='black')
        ax.text(H3[0], H3[1], H3[2], 'H', fontsize=12, ha='center', va='center', color='black')
        ax.set_title('NH3 Molecule')

    elif molecule == 'CH4':
        # CH4の正四面体形
        C = np.array([0, 0, 0])
        H1 = np.array([-1, 1, 1])
        H2 = np.array([1, 1, 1])
        H3 = np.array([-1, -1, 1])
        H4 = np.array([1, -1, 1])
        ax.plot([C[0], H1[0]], [C[1], H1[1]], [C[2], H1[2]], 'ro-')
        ax.plot([C[0], H2[0]], [C[1], H2[1]], [C[2], H2[2]], 'ro-')
        ax.plot([C[0], H3[0]], [C[1], H3[1]], [C[2], H3[2]], 'ro-')
        ax.plot([C[0], H4[0]], [C[1], H4[1]], [C[2], H4[2]], 'ro-')
        ax.text(C[0], C[1], C[2], 'C', fontsize=12, ha='center', va='center', color='black')
        ax.text(H1[0], H1[1], H1[2], 'H', fontsize=12, ha='center', va='center', color='black')
        ax.text(H2[0], H2[1], H2[2], 'H', fontsize=12, ha='center', va='center', color='black')
        ax.text(H3[0], H3[1], H3[2], 'H', fontsize=12, ha='center', va='center', color='black')
        ax.text(H4[0], H4[1], H4[2], 'H', fontsize=12, ha='center', va='center', color='black')
        ax.set_title('CH4 Molecule')

    elif molecule == 'CO2':
        # CO2の直線形
        C = np.array([0, 0, 0])
        O1 = np.array([-1.2, 0, 0])
        O2 = np.array([1.2, 0, 0])
        ax.plot([O1[0], C[0]], [O1[1], C[1]], [O1[2], C[2]], 'ro-')
        ax.plot([O2[0], C[0]], [O2[1], C[1]], [O2[2], C[2]], 'ro-')
        ax.text(C[0], C[1], C[2], 'C', fontsize=12, ha='center', va='center', color='black')
        ax.text(O1[0], O1[1], O1[2], 'O', fontsize=12, ha='center', va='center', color='black')
        ax.text(O2[0], O2[1], O2[2], 'O', fontsize=12, ha='center', va='center', color='black')
        ax.set_title('CO2 Molecule')

    ax.set_xlim(-2, 2)
    ax.set_ylim(-2, 2)
    ax.set_zlim(-2, 2)
    ax.set_xlabel('X axis')
    ax.set_ylabel('Y axis')
    ax.set_zlabel('Z axis')
    plt.show()

# 分子の形状を3Dでプロットする例
plot_molecule_3d('H2O')
plot_molecule_3d('NH3')
plot_molecule_3d('CH4')
plot_molecule_3d('CO2')

import matplotlib.pyplot as plt
import numpy as np

# 電気陰性度のデータ（ポーリングの値）
elements = ['F', 'O', 'N', 'Cl', 'C', 'H']
electronegativity = [3.98, 3.44, 3.04, 3.16, 2.55, 2.20]

# データのプロット
plt.figure(figsize=(10, 6))
plt.bar(elements, electronegativity, color=['blue', 'orange', 'green', 'red', 'purple', 'cyan'])
plt.xlabel('Element')
plt.ylabel('Electronegativity')
plt.title('Electronegativity of Elements')
plt.ylim(0, 4.5)
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# 時間の配列を作成
time = np.linspace(0, 10, 100)

# 可逆反応の濃度変化をモデル化
def reversible_reaction(t):
    return np.exp(-0.1 * t)  # 正反応
def reverse_reaction(t):
    return 1 - np.exp(-0.1 * t)  # 逆反応

# グラフをプロット
plt.figure(figsize=(10, 6))
plt.plot(time, reversible_reaction(time), label='Reversible Reaction', color='blue')
plt.plot(time, reverse_reaction(time), label='Reverse Reaction', color='red')
plt.xlabel('Time')
plt.ylabel('Concentration')
plt.title('Concentration vs Time for Reversible and Reverse Reactions')
plt.legend()
plt.grid(True)
plt.show()

# 時間の配列を作成
time = np.linspace(0, 10, 100)

# 初期濃度と反応速度を設定
initial_concentration_H2 = 1.0
initial_concentration_I2 = 1.0
initial_concentration_HI = 0.0

def reaction_progress(t):
    k1 = 0.1  # 正反応速度定数
    k2 = 0.05  # 逆反応速度定数
    H2 = initial_concentration_H2 * np.exp(-k1 * t)  # H2の濃度
    I2 = initial_concentration_I2 * np.exp(-k1 * t)  # I2の濃度
    HI = initial_concentration_HI + (initial_concentration_H2 - H2)  # HIの濃度
    return H2, I2, HI

H2_concentration = []
I2_concentration = []
HI_concentration = []

for t in time:
    H2, I2, HI = reaction_progress(t)
    H2_concentration.append(H2)
    I2_concentration.append(I2)
    HI_concentration.append(HI)

# グラフをプロット
plt.figure(figsize=(10, 6))
plt.plot(time, H2_concentration, label='[H2]', color='blue')
plt.plot(time, I2_concentration, label='[I2]', color='green')
plt.plot(time, HI_concentration, label='[HI]', color='red')
plt.xlabel('Time')
plt.ylabel('Concentration')
plt.title('Concentration of Reactants and Products Over Time')
plt.legend()
plt.grid(True)
plt.show()

# 平衡定数の計算
def equilibrium_constants(T, R):
    K_c = 1.0  # 仮定の平衡定数
    K_p = K_c * (R * T) ** (delta_n)  # Kpの計算
    return K_p

T = np.linspace(300, 1500, 100)  # 温度範囲
R = 0.0821  # 気体定数
delta_n = 1  # 反応物と生成物のモル数の差

K_p_values = equilibrium_constants(T, R)

# グラフをプロット
plt.figure(figsize=(10, 6))
plt.plot(T, K_p_values, label='Kp vs Temperature', color='purple')
plt.xlabel('Temperature (K)')
plt.ylabel('Kp')
plt.title('Pressure Equilibrium Constant vs Temperature')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

# チンダル現象のシミュレーション
def plot_tyndall_effect():
    plt.figure(figsize=(8, 6))
    plt.title('Tyndall Effect')
    plt.xlabel('Distance (arbitrary units)')
    plt.ylabel('Intensity (arbitrary units)')
    
    distance = np.linspace(0, 10, 500)
    intensity = np.exp(-distance**2) * 100  # 光の強度分布のシミュレーション
    plt.plot(distance, intensity, color='blue')
    plt.fill_between(distance, intensity, color='blue', alpha=0.2)
    plt.grid(True)
    plt.show()

# ブラウン運動のシミュレーション
def plot_brownian_motion():
    num_particles = 100
    num_steps = 500
    step_size = 0.1
    
    x = np.zeros((num_particles, num_steps))
    y = np.zeros((num_particles, num_steps))
    
    for i in range(1, num_steps):
        angle = np.random.uniform(0, 2 * np.pi, num_particles)
        x[:, i] = x[:, i-1] + step_size * np.cos(angle)
        y[:, i] = y[:, i-1] + step_size * np.sin(angle)
    
    plt.figure(figsize=(8, 8))
    plt.title('Brownian Motion')
    plt.xlabel('X Position')
    plt.ylabel('Y Position')
    
    for i in range(num_particles):
        plt.plot(x[i], y[i], alpha=0.5)
    
    plt.grid(True)
    plt.show()

# 電気泳動のシミュレーション
def plot_electrophoresis():
    num_particles = 50
    num_steps = 100
    step_size = 0.1
    electric_field = 0.2  # 電場の強さ
    
    x = np.zeros((num_particles, num_steps))
    y = np.zeros((num_particles, num_steps))
    
    for i in range(1, num_steps):
        angle = np.random.uniform(0, 2 * np.pi, num_particles)
        x[:, i] = x[:, i-1] + step_size * np.cos(angle) + electric_field
        y[:, i] = y[:, i-1] + step_size * np.sin(angle)
    
    plt.figure(figsize=(8, 8))
    plt.title('Electrophoresis')
    plt.xlabel('X Position')
    plt.ylabel('Y Position')
    
    for i in range(num_particles):
        plt.plot(x[i], y[i], alpha=0.5)
    
    plt.grid(True)
    plt.show()

# チンダル現象のプロット
plot_tyndall_effect()

# ブラウン運動のプロット
plot_brownian_motion()

# 電気泳動のプロット
plot_electrophoresis()

import numpy as np
import matplotlib.pyplot as plt

# 実在気体の定数
a = 1.0  # 単位: (L^2 atm / mol^2)
b = 0.1  # 単位: L / mol
R = 0.0821  # 気体定数: (L atm / K mol)

# 温度のリスト
temperatures = [273, 300, 350]  # K

# 体積の範囲
V = np.linspace(1, 10, 400)  # L

plt.figure(figsize=(10, 6))

# 各温度での状態方程式をプロット
for T in temperatures:
    n = 1  # モル数
    P = (n * R * T) / (V - n * b) - a * (n**2) / (V**2)  # 状態方程式
    plt.plot(V, P, label=f'T={T} K')

plt.title('Real Gas State Equation')
plt.xlabel('Volume (L)')
plt.ylabel('Pressure (atm)')
plt.legend()
plt.grid(True)
plt.show()

import matplotlib.pyplot as plt

# Bond energies (kJ/mol)
H_H_bond_energy = 432  # kJ/mol for H2 -> 2H
Cl_Cl_bond_energy = 239  # kJ/mol for Cl2 -> 2Cl
H_Cl_bond_energy = 428  # kJ/mol for HCl -> H + Cl

# Thermochemical equation energy
Q = None  # Unknown value to be calculated

# Energy levels for each step in the reaction
states = ['H2 (gas)', '2H (gas)', 'Cl2 (gas)', '2Cl (gas)', 'HCl (gas)']
energies = [0, -H_H_bond_energy, 0, -Cl_Cl_bond_energy, -H_Cl_bond_energy]

# Calculate Q from the energy balance
# H2 + Cl2 -> 2HCl
# ΔH = (2 × 428) - (432 + 239)
Q = (2 * H_Cl_bond_energy) - (H_H_bond_energy + Cl_Cl_bond_energy)

# Update the final energy state with the calculated Q
energies[-1] = -H_Cl_bond_energy + Q

# Plotting the energy diagram
plt.figure(figsize=(10, 6))
plt.plot(states, energies, marker='o', linestyle='-', color='b')
plt.title('Energy Diagram')
plt.xlabel('State')
plt.ylabel('Energy (kJ/mol)')
plt.grid(True)
plt.xticks(rotation=45)
plt.show()

import matplotlib.pyplot as plt

# Given energies (kJ/mol)
lattice_energy = 772  # kJ/mol for NaCl (solid) = Na+ (gas) + Cl- (gas)
ionization_energy_Na = 496  # kJ/mol for Na (gas) = Na+ (gas) + e-
sublimation_heat_Na = 92  # kJ/mol for Na (solid) = Na (gas)
bond_energy_Cl2 = 244  # kJ/mol for Cl2 (gas) = 2Cl (gas)
electron_affinity_Cl = 349  # kJ/mol for Cl (gas) + e- = Cl- (gas)

# Calculate Q (heat of formation of NaCl)
# Na (solid) + 1/2 Cl2 (gas) = NaCl (solid) + Q (kJ)
Q = ionization_energy_Na + sublimation_heat_Na + (bond_energy_Cl2 / 2) - electron_affinity_Cl - lattice_energy

# Energy levels for each step in the reaction
states = ['Na (solid)', 'Na (gas)', 'Na+ (gas)', 'Cl2 (gas)', '2Cl (gas)', 'NaCl (solid)']
energies = [0, sublimation_heat_Na, sublimation_heat_Na + ionization_energy_Na,
            0, bond_energy_Cl2 / 2, sublimation_heat_Na + ionization_energy_Na + bond_energy_Cl2 / 2 - electron_affinity_Cl - lattice_energy]

# Plotting the energy diagram
plt.figure(figsize=(10, 6))
plt.plot(states, energies, marker='o', linestyle='-', color='b')
plt.title('Energy Diagram')
plt.xlabel('State')
plt.ylabel('Energy (kJ/mol)')
plt.grid(True)
plt.xticks(rotation=45)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Given solubility data
temp_80 = 80  # in Celsius
solubility_80 = 167  # in g per 100g of water
temp_40 = 40  # in Celsius
solubility_40 = 61.3  # in g per 100g of water

# Calculating the amount recrystallized
recrystallized_amount = solubility_80 - solubility_40  # g

# Generating temperature data points
temperature = np.linspace(40, 80, 400)
# Fitting a parabola (2nd degree polynomial) through the given points
coefficients = np.polyfit([40, 80], [solubility_40, solubility_80], 2)
solubility_curve = np.polyval(coefficients, temperature)

# Plotting the solubility curve
plt.figure(figsize=(10, 6))
plt.plot(temperature, solubility_curve, label='Solubility Curve', color='b')
plt.scatter([40, 80], [solubility_40, solubility_80], color='r')

# Adding horizontal lines for initial and final solubility
plt.axhline(y=solubility_80, color='r', linestyle='--', label='Initial solubility at 80°C (167g)')
plt.axhline(y=solubility_40, color='g', linestyle='--', label='Final solubility at 40°C (61.3g)')

# Annotating the recrystallized amount
plt.annotate(f'Recrystallized: {recrystallized_amount}g', 
             xy=(60, solubility_40 + recrystallized_amount/2), 
             xytext=(45, solubility_40 + recrystallized_amount/2 + 20),
             arrowprops=dict(facecolor='black', shrink=0.05))

plt.title('Recrystallization Process')
plt.xlabel('Temperature (°C)')
plt.ylabel('Solubility (g per 100g of water)')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Simulated solubility data for different substances
temperature = np.linspace(0, 100, 11)  # Temperature range from 0°C to 100°C

# Solubility data in g/100g of water for different substances (hypothetical)
potassium_nitrate = np.array([13, 21, 31, 45, 61, 83, 106, 134, 167, 202, 245])
sodium_nitrate = np.array([73, 77, 82, 88, 94, 101, 108, 116, 125, 135, 146])
potassium_chloride = np.array([28, 31, 34, 38, 42, 46, 50, 55, 60, 66, 72])
sodium_chloride = np.array([35, 35.5, 36, 36.5, 37, 37.5, 38, 38.5, 39, 39.5, 40])
lithium_sulfate = np.array([30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50])

# Plotting the solubility curves
plt.figure(figsize=(10, 6))

plt.plot(temperature, potassium_nitrate, 'b-', label='Potassium Nitrate')
plt.plot(temperature, sodium_nitrate, 'r--', label='Sodium Nitrate')
plt.plot(temperature, potassium_chloride, 'g-.', label='Potassium Chloride')
plt.plot(temperature, sodium_chloride, 'y:', label='Sodium Chloride')
plt.plot(temperature, lithium_sulfate, 'c-', label='Lithium Sulfate')

# Adding title and labels
plt.title('Solubility Curves')
plt.xlabel('Temperature (°C)')
plt.ylabel('Solubility (g per 100g of water)')
plt.legend()
plt.grid(True)

# Show the plot
plt.show()

import matplotlib.pyplot as plt

# Problem 1
# KCl solubility at 40°C
solubility_40C = 40  # g/100g water

# (1) Amount of KCl that can still be dissolved at 40°C
dissolved_KCl = 25  # g
remaining_KCl = solubility_40C - dissolved_KCl
print(f"1(1): Remaining KCl that can be dissolved: {remaining_KCl} g")

# (2) Maximum amount of KCl that can dissolve in 250g water at 40°C
water_mass = 250  # g
max_dissolved_KCl_250g = solubility_40C * (water_mass / 100)
print(f"1(2): Maximum amount of KCl in 250g water: {max_dissolved_KCl_250g} g")

# (3) Amount of KCl in 100g of saturated solution at 40°C
# Solubility ratio
x = solubility_40C
saturated_solution_mass = 100  # g
dissolved_KCl_saturated = (x / (100 + x)) * saturated_solution_mass
print(f"1(3): Amount of KCl in 100g of saturated solution: {dissolved_KCl_saturated:.1f} g")

# Problem 2
# Solubility of KCl at different temperatures
solubility_20C = 30  # g/100g water
solubility_60C = 50  # g/100g water

# (1) Temperature at which KCl starts to precipitate when cooling from 60°C
initial_dissolved_KCl = 40  # g in 100g water
if initial_dissolved_KCl > solubility_40C:
    precipitate_temp = 40  # °C
else:
    precipitate_temp = 60  # °C
print(f"2(1): Temperature at which KCl starts to precipitate: {precipitate_temp} °C")

# (2) Amount of KCl precipitated when water evaporates at 40°C
# Remaining KCl after evaporation
evaporated_water_mass = 20  # g
max_dissolved_KCl_evaporated = solubility_40C * (evaporated_water_mass / 100)
print(f"2(2): Amount of KCl precipitated after evaporation: {max_dissolved_KCl_evaporated} g")

# Problem 3
# KNO3 solubility at 40°C and 80°C
KNO3_solubility_40C = 60  # g/100g water
KNO3_solubility_80C = 160  # g/100g water

# Amount of KNO3 precipitated when cooling from 80°C to 40°C
initial_dissolved_KNO3 = 160  # g in 100g water
remaining_KNO3_40C = KNO3_solubility_40C
precipitated_KNO3 = initial_dissolved_KNO3 - remaining_KNO3_40C
print(f"3: Amount of KNO3 precipitated: {precipitated_KNO3} g")

# Problem 4
# CuSO4 solubility at 20°C and 60°C
CuSO4_solubility_20C = 20  # g/100g water
CuSO4_solubility_60C = 40  # g/100g water

# Amount of CuSO4·5H2O precipitated when cooling from 60°C to 20°C
initial_dissolved_CuSO4 = 40  # g in 100g water
remaining_CuSO4_20C = CuSO4_solubility_20C
precipitated_CuSO4 = initial_dissolved_CuSO4 - remaining_CuSO4_20C

# Molecular weights
CuSO4_mw = 160
H2O_mw = 18
CuSO4_5H2O_mw = CuSO4_mw + 5 * H2O_mw

precipitated_CuSO4_5H2O = precipitated_CuSO4 * (CuSO4_5H2O_mw / CuSO4_mw)
print(f"4: Amount of CuSO4·5H2O precipitated: {precipitated_CuSO4_5H2O:.1f} g")

# Plotting the solubility curve
temperatures = [20, 40, 60, 80]
KCl_solubility = [30, 40, 50, 60]  # Hypothetical solubility data for KCl
KNO3_solubility = [20, 60, 100, 160]  # Hypothetical solubility data for KNO3

plt.figure(figsize=(10, 6))

plt.plot(temperatures, KCl_solubility, 'bo-', label='KCl')
plt.plot(temperatures, KNO3_solubility, 'rs-', label='KNO3')

plt.title('Solubility Curves')
plt.xlabel('Temperature (°C)')
plt.ylabel('Solubility (g per 100g of water)')
plt.legend()
plt.grid(True)

plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Parameters
V0 = 50  # Volume of HCl solution (mL)
C0 = 0.1  # Concentration of HCl (M)
C1 = 0.1  # Concentration of NaOH (M)

# Volume range for NaOH addition
V_NaOH = np.linspace(0, 100, 1000)  # mL

# Calculate pH
def calculate_pH(V_NaOH, V0, C0, C1):
    # Moles of HCl and NaOH
    n_HCl = V0 * C0 / 1000  # moles of HCl
    n_NaOH = V_NaOH * C1 / 1000  # moles of NaOH
    
    # Initialize pH array
    pH = np.zeros_like(V_NaOH)
    
    # Acidic solution case
    acidic = n_NaOH <= n_HCl
    pH[acidic] = -np.log10(n_HCl - n_NaOH[acidic])
    
    # Basic solution case
    basic = n_NaOH > n_HCl
    pOH = -np.log10(n_NaOH[basic] - n_HCl)
    pH[basic] = 14 - pOH
    
    return pH

pH_values = calculate_pH(V_NaOH, V0, C0, C1)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(V_NaOH, pH_values, label='Titration Curve', color='b')
plt.axhline(y=7, color='r', linestyle='--', label='Neutral pH (7)')
plt.title('Titration Curve of Strong Acid with Strong Base')
plt.xlabel('Volume of NaOH added (mL)')
plt.ylabel('pH')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Parameters
V0 = 50  # Volume of NH3 solution (mL)
C0 = 0.1  # Concentration of NH3 (M)
C1 = 0.1  # Concentration of HCl (M)
Kb = 1.8e-5  # Base dissociation constant for NH3

# Volume range for HCl addition
V_HCl = np.linspace(0, 100, 1000)  # mL

# Calculate pH
def calculate_pH_weak_base(V_HCl, V0, C0, C1, Kb):
    # Moles of NH3 and HCl
    n_base = V0 * C0 / 1000  # moles of NH3
    n_acid = V_HCl * C1 / 1000  # moles of HCl
    
    # Initialize pH array
    pH = np.zeros_like(V_HCl)
    
    # Determine pH based on the amount of base and acid
    for i in range(len(V_HCl)):
        if n_acid[i] < n_base:
            # Before equivalence point
            C_base = (n_base - n_acid[i]) / (V0 + V_HCl[i])  # Concentration of NH3
            C_conjugate_acid = (n_acid[i] / (V0 + V_HCl[i]))  # Concentration of NH4+
            pOH = 0.5 * (np.log10(Kb * C_base / C_conjugate_acid))
            pH[i] = 14 - pOH
        else:
            # After equivalence point
            excess_acid = n_acid[i] - n_base
            pH[i] = -np.log10(excess_acid / (V0 + V_HCl[i]))
    
    return pH

pH_values = calculate_pH_weak_base(V_HCl, V0, C0, C1, Kb)

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(V_HCl, pH_values, label='Titration Curve', color='b')
plt.axhline(y=7, color='r', linestyle='--', label='Neutral pH (7)')
plt.title('Titration Curve of Weak Base with Strong Acid')
plt.xlabel('Volume of HCl added (mL)')
plt.ylabel('pH')
plt.legend()
plt.grid(True)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# 定数
N2O5_0 = 2.33  # 初期濃度 (mol/L)
k = 0.63  # 反応速度定数 (s^-1)
time = np.linspace(0, 2000, 1000)  # 時間 (秒)

# 濃度の計算
N2O5_concentration = N2O5_0 * np.exp(-k * time)

# 濃度の対数計算 (自然対数)
ln_N2O5 = np.log(N2O5_concentration)

# グラフの作成
plt.figure(figsize=(10, 6))

# 濃度のプロット
plt.subplot(2, 1, 1)
plt.plot(time, N2O5_concentration, label='[N2O5]', color='blue')
plt.title('Concentration of N2O5 vs Time')
plt.xlabel('Time (s)')
plt.ylabel('Concentration [N2O5] (mol/L)')
plt.grid(True)
plt.legend()

# 対数プロット (一次プロット)
plt.subplot(2, 1, 2)
plt.plot(time, ln_N2O5, label='ln[N2O5]', color='green')
plt.title('First-Order Plot (ln[N2O5] vs Time)')
plt.xlabel('Time (s)')
plt.ylabel('ln[N2O5]')
plt.grid(True)
plt.legend()

# グラフの表示
plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# 定数
R = 8.3145  # 気体定数 (J/K/mol)
Ea = 1.24e5  # 活性化エネルギー (J/mol)
A = 1e13  # 頻度因子

# 温度範囲 (K)
T = np.linspace(300, 1500, 100)  # 温度範囲を300Kから1500Kまで

# アレニウス式に基づく反応速度定数の計算
k = A * np.exp(-Ea / (R * T))

# 自然対数を取る
ln_k = np.log(k)

# プロットの作成
plt.figure(figsize=(10, 6))

# 反応速度定数 k のプロット
plt.subplot(2, 1, 1)
plt.plot(T, k, label='Reaction rate constant k', color='blue')
plt.title('Reaction Rate Constant (k) vs Temperature (T)')
plt.xlabel('Temperature (K)')
plt.ylabel('k')
plt.grid(True)
plt.legend()

# ln(k) と 1/T のプロット (線形化されたアレニウス式)
plt.subplot(2, 1, 2)
plt.plot(1/T, ln_k, label='ln(k)', color='green')
plt.title('Linearized Arrhenius Plot (ln(k) vs 1/T)')
plt.xlabel('1/Temperature (1/K)')
plt.ylabel('ln(k)')
plt.grid(True)
plt.legend()

# グラフの表示
plt.tight_layout()
plt.show()

import numpy as np

# 定数
R = 8.3145  # 気体定数 (J/K/mol)
T1 = 273.15  # 温度 T1 (K), 0 deg C
T2 = 318.15  # 温度 T2 (K), 45 deg C
k1 = 1.06e-5  # 速度定数 k1 (s^-1)
k2 = 2.92e-3  # 速度定数 k2 (s^-1)

# log(k2/k1) の計算
log_k_ratio = np.log(k2 / k1)

# 1/T1 - 1/T2 の計算
temp_diff = (1 / T1) - (1 / T2)

# 活性化エネルギー E_a の計算 (J/mol)
Ea = -log_k_ratio * R / temp_diff

# 結果を kJ/mol で表示
Ea_kJ = Ea / 1000  # J/mol -> kJ/mol

print(f"活性化エネルギー (E_a): {Ea_kJ:.2f} kJ/mol")


# 反応物と生成物の濃度 (mol/L)
A_conc = 1.0  # [A] の濃度 (mol/L)
B_conc = 1.0  # [B] の濃度 (mol/L)
C_conc = 2.0  # [C] の濃度 (mol/L)
D_conc = 2.0  # [D] の濃度 (mol/L)

# 係数 (反応式における化学量論係数)
a = 1  # aA の a
b = 1  # bB の b
c = 1  # cC の c
d = 1  # dD の d

# 平衡定数 K の計算
K = (C_conc ** c * D_conc ** d) / (A_conc ** a * B_conc ** b)

# 結果の表示
print(f"平衡定数 K: {K:.2f}")

import math
from scipy.optimize import fsolve

# (1) Calculation of the equilibrium constant K_c
HI_mol = 3.16  # Moles of hydrogen iodide produced at equilibrium [mol]
H2_mol_remaining = 0.42  # Moles of hydrogen remaining at equilibrium [mol]
I2_mol_remaining = 0.42  # Moles of iodine remaining at equilibrium [mol]

# Calculation of the equilibrium constant K_c
Kc = (HI_mol ** 2) / (H2_mol_remaining * I2_mol_remaining)
print(f"(1) Equilibrium constant K_c: {Kc:.2f}")

# (2) Calculation of the amount of hydrogen iodide produced at equilibrium under different initial conditions
# Initial conditions
H2_initial = 5.00  # Initial moles of hydrogen [mol]
I2_initial = 3.50  # Initial moles of iodine [mol]

# Define the equation to find the equilibrium point
def equilibrium(x):
    return (2 * x) ** 2 / ((H2_initial - x) * (I2_initial - x)) - Kc

# Solve for x (half the moles of HI produced) using fsolve
x_solution = fsolve(equilibrium, 1.0)[0]

# Calculate the total moles of hydrogen iodide produced
HI_mol_generated = 2 * x_solution
print(f"(2) Moles of hydrogen iodide produced at equilibrium: {HI_mol_generated:.2f} mol")

import math

# Given constants
Kw = 1.01e-14  # Ion product of water at 25°C in mol^2/L^2
H_plus_concentration = 0.0100  # Concentration of H⁺ ions in mol/L for HCl

# (1) Calculate the concentration of OH⁻ ions using Kw
OH_minus_concentration = Kw / H_plus_concentration
print(f"Concentration of OH⁻ ions: {OH_minus_concentration:.2e} mol/L")

# (2) Calculate the pH
pH = -math.log10(H_plus_concentration)
print(f"pH of the solution: {pH:.2f}")

# (3) Calculate the pOH
pOH = -math.log10(OH_minus_concentration)
print(f"pOH of the solution: {pOH:.2f}")

import math

# Constants
R = 8.314  # Gas constant in J/(mol*K)
F = 96485  # Faraday constant in C/mol
T = 298.15  # Temperature in Kelvin (25°C assumed)
n = 2  # Number of electrons transferred

# Standard EMF of Daniell cell
E_standard = 1.10  # in Volts (for the Zn-Cu system)

# Concentrations of Zn2+ and Cu2+
Zn_concentration = 0.010  # Concentration of Zn2+ in mol/L
Cu_concentration = 1.0  # Concentration of Cu2+ in mol/L

# Nernst equation for the Daniell cell
E = E_standard - (R * T / (n * F)) * math.log(Zn_concentration / Cu_concentration)

print(f"The EMF of the Daniell cell is: {E:.4f} V")