ZnO–Carbon Photocatalysis Dataset Generated via Langmuir–Hinshelwood Model

This dataset contains synthetic data generated through numerical solutions of the Langmuir–Hinshelwood (L–H) kinetic model to simulate photocatalytic degradation processes using ZnO–carbon nanocomposites. The dataset covers a broad range of physicochemical parameters, including initial pollutant concentration, pH, temperature, irradiation time, catalyst particle size, bandgap energy, absorbance, reaction rate constant, catalyst loading, and light intensity. The Langmuir–Hinshelwood model was selected because it accounts for both adsorption equilibrium and surface reaction kinetics, making it well-suited for modeling heterogeneous photocatalytic systems. This dataset is intended to support data-driven modeling, optimization studies, and machine learning applications in photocatalysis, particularly for predicting degradation efficiency under varying operational conditions. The dataset is associated with a study on a hybrid BBDML framework developed for photocatalytic performance prediction and optimization. All data points are synthetically generated but constrained within experimentally reported ranges to ensure realistic representation of photocatalytic behavior.本数据集包含通过朗缪尔-欣谢尔伍德（Langmuir–Hinshelwood，L–H）动力学模型的数值求解所生成的合成数据，用于模拟氧化锌-碳纳米复合材料（ZnO–carbon nanocomposites）的光催化降解过程。 本数据集涵盖了丰富的物理化学参数，包括污染物初始浓度、pH值、温度、辐照时间、催化剂粒径、带隙能、吸光度、反应速率常数、催化剂投加量以及光强。 之所以选择朗缪尔-欣谢尔伍德模型，是因为该模型同时兼顾吸附平衡与表面反应动力学，非常适用于非均相光催化体系的建模。 本数据集旨在为光催化领域的数据驱动建模、优化研究及机器学习应用提供支撑，尤其可用于预测不同运行条件下的降解效率。 本数据集与一项针对用于光催化性能预测与优化的混合式BBDML框架的开发研究相关联。 所有数据点均为合成生成，但均约束于已发表的实验数据范围内，以确保其能够真实反映光催化行为的实际特征。