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Recent advancements in integrating metal-free two-dimensional (2D) materials with graphitic carbon nitride (g-C 3 N 4 ) have paved the way for improving photocatalytic efficiency in the degradation of organic pollutants. This review examines the potential of these heterostructures, focusing on their ability to overcome the limitations of traditional metal-based photocatalysts, such as their cost and environmental concerns. The synergistic effects observed in these composites lead to improved charge separation, extended light absorption, and increased catalytic active sites, which are crucial for effective photocatalysis. This review covers a variety of metal-free 2D materials, including graphene, graphene oxide, reduced graphene oxide, boron nitride, and black phosphorus, and demonstrates their effectiveness when combined with g-C 3 N 4 . A comprehensive analysis of recent advancements is provided, together with discussion of challenges related to advanced synthesis strategies and compositional tuning. The integration of density functional theory (DFT) with machine learning (ML)-driven computational tools is also discussed, highlighting their role in predicting optimal band alignments, charge transfer mechanisms, and reaction pathways, accelerating material discovery while reducing experimental trial and error. By combining experimental validation pathways with computational insight, this review presents a structured and mechanism-centered synthesis of metal-free 2D/g-C 3 N 4 heterostructures for pollutant degradation, supported by a transparent comparison with prior literature. This integrative perspective aims to consolidate structure–property–performance relationships and guide the rational development of sustainable, high-performance photocatalysts for environmental remediation.
Geleta et al. (Thu,) studied this question.