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Two-dimensional (2D) heterostructures applied in environmental remediation and solar energy conversion are constrained by a critical disconnect: mechanistic interpretations are frequently fragmented, charge-transfer pathway assignments often rely on circumstantial rather than definitive evidence, and performance metrics reported across laboratories remain largely incomparable due to the absence of standardized normalization protocols. The present review critically examines type-II, Z-scheme, and S-scheme heterojunction architectures through the lens of band alignment energetics, interfacial charge-transfer dynamics, and thermodynamic driving forces for environmentally relevant redox reactions. The analysis demonstrates that type-II configurations systematically attenuate the effective redox potential of both electrons and holes: a trade-off that is quantitatively consequential for multi-electron reactions yet routinely overlooked in performance-driven studies. Furthermore, the distinction between Z-scheme and S-scheme mechanisms, while conceptually established, is experimentally underdetermined in most of the reviewed literature, where built-in electric field formation is rarely verified by in-situ XPS or Kelvin probe force microscopy. Finally, Machine Learning fundamentals, design strategies and future directions are discussed to guide the development of efficient and scalable photocatalytic systems. The review concludes by recommending convergent operando characterization, surface-area-independent performance metrics, and integrated ML–experimental workflows as prerequisites for translating laboratory advances into scalable photocatalytic technologies.
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Rodrigo Teixeira Bento (2026) studied this question.
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