<p>This Overview discusses recent advances in heteroatom-doped graphene (e.g., N, B, F) for 4-nitrophenol (4-NP) remediation, emphasizing how dopant-induced electronic polarization, defects, and adsorption sites govern reactivity. We highlight the two dominant transformation regimes (NaBH<sub>4</sub>-assisted reduction to 4-aminophenol and ROS-driven oxidative degradation/mineralization) and summarize mechanistic evidence linking dopant configuration to charge transfer, ROS generation, and pathway selection. Strategies for performance optimization, including compositing, pore/edge engineering, and surface functional tuning, are critically compared. We further propose benchmarking best practices by standardizing key parameters, including the BH<sub>4</sub>⁻/4-NP ratio, pH, temperature, mixing, and normalized kinetic metrics, to enable fair cross-study comparisons. Recent advances in computational and AI-assisted approaches are also included to illustrate how DFT, machine learning, and data-driven screening can strengthen mechanistic understanding and guide dopant design. Finally, we outline priorities for translation, including stability, regenerability, validation in realistic water matrices, and closer integration of experiment with theory-guided catalyst development.</p> Graphical abstract <p></p>

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Heteroatom-doped graphene catalysts for efficient 4-nitrophenol degradation: Mechanisms, optimization, and future directions for sustainable water remediation

  • Huawen Hu,
  • Yinlei Lin

摘要

This Overview discusses recent advances in heteroatom-doped graphene (e.g., N, B, F) for 4-nitrophenol (4-NP) remediation, emphasizing how dopant-induced electronic polarization, defects, and adsorption sites govern reactivity. We highlight the two dominant transformation regimes (NaBH4-assisted reduction to 4-aminophenol and ROS-driven oxidative degradation/mineralization) and summarize mechanistic evidence linking dopant configuration to charge transfer, ROS generation, and pathway selection. Strategies for performance optimization, including compositing, pore/edge engineering, and surface functional tuning, are critically compared. We further propose benchmarking best practices by standardizing key parameters, including the BH4⁻/4-NP ratio, pH, temperature, mixing, and normalized kinetic metrics, to enable fair cross-study comparisons. Recent advances in computational and AI-assisted approaches are also included to illustrate how DFT, machine learning, and data-driven screening can strengthen mechanistic understanding and guide dopant design. Finally, we outline priorities for translation, including stability, regenerability, validation in realistic water matrices, and closer integration of experiment with theory-guided catalyst development.

Graphical abstract