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边缘掺杂取代基作为一种新兴的原子级策略,用于增强M-N-C单原子催化剂在氧还原反应(ORR)、析氧反应(OER)和析氢反应(HER)中的电催化性能。

Edge-doped substituents as an emerging atomic-level strategy for enhancing M-N-C single-atom catalysts in electrocatalysis of the ORR, OER, and HER.

作者信息

Xie Liang, Zhou Wei, Qu Zhibin, Huang Yuming, Li Longhao, Yang Chaowei, Li Junfeng, Meng Xiaoxiao, Sun Fei, Gao Jihui, Zhao Guangbo

机构信息

School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, P. R. China.

出版信息

Nanoscale Horiz. 2025 Jan 27;10(2):322-335. doi: 10.1039/d4nh00424h.

DOI:10.1039/d4nh00424h
PMID:39552526
Abstract

M-N-C single-atom catalysts (MN) have gained attention for their efficient use at the atomic level and adjustable properties in electrocatalytic reactions like the ORR, OER, and HER. Yet, understanding MN's activity origin and enhancing its performance remains challenging. Edge-doped substituents profoundly affect MN's activity, explored in this study by investigating their interaction with MN metal centers in ORR/OER/HER catalysis (Sub@MN, Sub = B, N, O, S, CH, NO, NH, OCH, SO; M = Fe, Co, Ni, Cu). The results show overpotential variations (0 V to 1.82 V) based on Sub and metal centers. S and SO groups optimize FeN for peak ORR activity (overpotential at 0.48 V) and reduce OER overpotentials for NiN (0.48 V and 0.44 V). N significantly reduces FeN's HER overpotential (0.09 V). Correlation analysis highlights the metal center's key role, with Δ and Δ showing mutual predictability ( = 0.92). proves a reliable predictor for Sub@CoN (Δ/Δ, = 0.96 and 0.72). Machine learning with the KNN model aids catalyst performance prediction ( = 0.955 and 0.943 for Δ/Δ), emphasizing M-O/M-H and the d band center as crucial factors. This study elucidates edge-doped substituents' pivotal role in MN activity modulation, offering insights for electrocatalyst design and optimization.

摘要

M-N-C单原子催化剂(MN)因其在原子水平上的高效利用以及在氧还原反应(ORR)、析氧反应(OER)和析氢反应(HER)等电催化反应中可调节的性质而受到关注。然而,理解MN的活性起源并提高其性能仍然具有挑战性。边缘掺杂取代基对MN的活性有深远影响,本研究通过研究它们在ORR/OER/HER催化中与MN金属中心的相互作用进行了探索(Sub@MN,Sub = B、N、O、S、CH、NO、NH、OCH、SO;M = Fe、Co、Ni、Cu)。结果表明,基于取代基和金属中心存在过电位变化(0 V至1.82 V)。S和SO基团优化了FeN的ORR峰值活性(过电位为0.48 V),并降低了NiN的OER过电位(0.48 V和0.44 V)。N显著降低了FeN的HER过电位(0.09 V)。相关性分析突出了金属中心的关键作用,其中Δ和Δ显示出相互可预测性( = 0.92)。 证明是Sub@CoN的可靠预测指标(Δ/Δ, = 0.96和0.72)。使用KNN模型的机器学习有助于催化剂性能预测(Δ/Δ的 = 0.955和0.943),强调M-O/M-H和d带中心是关键因素。本研究阐明了边缘掺杂取代基在MN活性调节中的关键作用,为电催化剂的设计和优化提供了见解。

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