ZnO-Based Photocatalysts: Synergistic Effects of Material Modifications and Machine Learning Optimization

ZnO-based photocatalysts have attracted significant attention for their potential use in advanced oxidation processes for environmental remediation. However, critical challenges, such as rapid charge carrier recombination and narrow light absorption, and poor long-term stability necessitate material modifications to enhance performance. This review provides a comprehensive and critical analysis of recent developments in ZnO-based photocatalysts, including heterojunctions with metal oxides, carbon-based hybrids, metal/non-metal doping, and metal–organic framework materials. Furthermore, emerging trends, such as the integration of atomistic calculations and machine learning (ML) techniques in material design, property prediction, and the optimization of photocatalytic performance, are critically examined. These modern computationally driven approaches provide new insights into band gap engineering, charge transport mechanisms, and the optimization of synthesis parameters, thereby accelerating the discovery of high-performance ZnO-based photocatalysts. However, their practical integration remains limited due to the availability of high-quality datasets and the lack of interdisciplinary methodologies. The review also discusses key research gaps, including emerging environmental applications, as well as stability and scalability challenges, providing a roadmap for future research in data-driven photocatalysis. By evaluating current research, this review aims to provide a foundation for the modification of next-generation ZnO-based photocatalysts for environmental applications.