Jiang Xue-Chun, Zhao Jian-Wen, Liu Jin-Xun
State Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China.
Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China.
Nanoscale. 2025 Jul 3;17(26):15628-15647. doi: 10.1039/d5nr01682g.
The electrocatalytic reduction of nitrogen oxides (NO), particularly nitrate (NO), nitrite (NO) and nitrogen oxide (NO), to ammonia (NH) represents a sustainable strategy for nitrogen cycle management and pollution mitigation. However, optimizing the efficiency and selectivity for NH production remains challenging because of competing side reactions, complex reaction networks, and the need for precise control over intermediate species. This review provides a comprehensive overview of recent theoretical advancements in the NO electroreduction reaction (NORR), emphasizing mechanistic insights into reaction pathways, key intermediates, and activity-determining descriptors. We highlight the role of computational modeling, from density functional theory (DFT) studies and microkinetic simulations to machine learning-driven approaches, in elucidating active sites, guiding rational catalyst design, and accelerating material discovery. Special attention is given to the emerging synergy between theory and experiment, which bridges idealized models and realistic electrochemical conditions, thereby enabling data-driven catalyst discovery and mechanism-guided design. Finally, we outline the remaining challenges and future directions, emphasizing innovations in computational techniques and scalable catalyst development for sustainable ammonia synthesis and nitrogen waste reduction.
将氮氧化物（NO），特别是硝酸盐（NO₃⁻）、亚硝酸盐（NO₂⁻）和一氧化氮（NO）电催化还原为氨（NH₃）是氮循环管理和污染缓解的可持续策略。然而，由于存在竞争性副反应、复杂的反应网络以及对中间物种进行精确控制的需求，优化氨生产的效率和选择性仍然具有挑战性。本综述全面概述了氮氧化物电还原反应（NORR）的最新理论进展，重点阐述了对反应途径、关键中间体和活性决定描述符的机理见解。我们强调了计算建模的作用，从密度泛函理论（DFT）研究、微观动力学模拟到机器学习驱动的方法，在阐明活性位点、指导合理的催化剂设计以及加速材料发现方面的作用。特别关注理论与实验之间新兴的协同作用，它弥合了理想化模型与实际电化学条件之间的差距，从而实现数据驱动的催化剂发现和机理指导的设计。最后，我们概述了 remaining challenges（原文此处有误，可能是remaining challenges，意为“剩余的挑战”）和未来方向，强调计算技术创新以及用于可持续氨合成和减少氮废物的可扩展催化剂开发。
