State-of-the-art evolution of g-C3N4-based photocatalytic applications: A critical review

Abstract

g-C₃N₄ is a promising non-metallic photocatalyst recognized for its unique structural and physicochemical properties. Recent reviews have addressed g-C₃N₄-based photocatalysis; however, the rapid progress in big data and artificial intelligence has significantly accelerated the design, synthesis, and optimization of these materials. Machine learning, theoretical simulations, and advanced in-situ characterization techniques have deepened our understanding of their photocatalytic mechanisms. This review critically evaluates advancements in g-C₃N₄-based photocatalysts over the last two to three years, focusing on strategies to improve photogenerated charge separation, expand light absorption, and enhance stability and catalytic efficiency. It discusses cutting-edge in-situ characterization methods alongside machine learning approaches for predicting and optimizing applications in photocatalytic H₂ evolution, CO₂ reduction, pollutant degradation, H₂O₂ production, and nitrogen fixation. Finally, it proposes prospective strategies for further enhancing the performance of g-C₃N₄-based photocatalysts, aiming to guide the design of high-performance two-dimensional carbon-based photocatalysts.