材料科学
催化作用
Boosting(机器学习)
氨
选择性
产量(工程)
硝酸盐
法拉第效率
氨生产
密度泛函理论
电催化剂
拓扑(电路)
纳米技术
合理设计
半导体
电荷(物理)
表面状态
动力学
化学工程
电化学
表面电荷
表面改性
选择性催化还原
化学物理
工作(物理)
氧化还原
化学稳定性
载流子
反应中间体
组合化学
电极
作者
Tingting Liang,Jiangnan Lv,Lanfang Wang,Qianwen Yang,Jianlei Shen,Xiaoting Sun,Wanting Rong,Qiqi Dai,Fang Wang,Yang Liu
标识
DOI:10.1002/aenm.202503473
摘要
Abstract Designing efficient catalysts for nitrate reduction reaction (NO 3 − RR) poses a challenge in advancing the selectivity and yield of ammonia (NH 3 ). Unlike conventional catalytic descriptors, topological surface states (TSSs) represent an orthogonal avenue for tailoring catalytic properties, while its role in NO 3 − RR remains unknown. Here, the semimetallic character of Co 3 Sn 2 S 2 , endowed with robust TSSs is leveraged and enhances charge transport characteristics, to establish this system as a prototypical platform for decoding surface state‐governed NO 3 − RR mechanism. The catalyst exhibits exceptional NO 3 − RR performance, achieving a maximum NH 3 Faradaic efficiency of 91.6% at −0.5 V RHE and a high NH 3 yield of 22.4 mg h −1 cm −2 at −0.6 V RHE , while maintaining excellent stability over 200 h in a membrane–electrode assembly electrolyzer, outperforming its semiconductor counterparts. In situ experiments and density functional theory calculations reveal that the TSSs accelerate charge transfer kinetics as well as alleviate the energy barrier for the *NOH → *N step. This work highlights the critical role of TSSs in governing electrocatalytic mechanisms and advances the rational design of high‐performance topological NO 3 − RR catalysts.
科研通智能强力驱动
Strongly Powered by AbleSci AI