边缘掺杂取代基作为一种新兴的原子级策略,可增强 M-N4-C 单原子催化剂在 ORR、OER 和 HER 电催化中的作用。

IF 8 2区 材料科学 Q1 CHEMISTRY, PHYSICAL Nanoscale Horizons Pub Date : 2024-11-18 DOI:10.1039/d4nh00424h
Liang Xie, Wei Zhou, Zhibin Qu, Yuming Huang, Longhao Li, Chaowei Yang, Junfeng Li, Xiaoxiao Meng, Fei Sun, Jihui Gao, Guangbo Zhao
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引用次数: 0

摘要

M-N4-C 单原子催化剂(MN4)因其在原子水平上的高效利用以及在 ORR、OER 和 HER 等电催化反应中的可调特性而备受关注。然而,了解 MN4 的活性起源并提高其性能仍然是一项挑战。边缘掺杂的取代基对 MN4 的活性有着深远的影响,本研究通过研究它们与 MN4 金属中心在 ORR/OER/HER催化(Sub@MN4,Sub = B、N、O、S、CH3、NO2、NH2、OCH3、SO4;M = Fe、Co、Ni、Cu)中的相互作用来探讨这一问题。结果显示,过电势随子和金属中心的不同而变化(0 V 至 1.82 V)。S 和 SO4 基团优化了 FeN4 的峰值 ORR 活性(过电位为 0.48 V),降低了 NiN4 的 OER 过电位(0.48 V 和 0.44 V)。N 则大大降低了 FeN4 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,ΔG*H 和 ΔG*OOH显示出相互可预测性(R2 = 0.92)。Eg 是 Sub@CoN4 的可靠预测因子(ΔG*OOH/ΔG*H,R2 = 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(ΔG*OOH/ΔG*H 的 R2 = 0.955 和 0.943),强调 M-O/M-H 和 d 带中心是关键因素。这项研究阐明了边缘掺杂取代基在 MN4 活性调节中的关键作用,为电催化剂的设计和优化提供了启示。
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Edge-doped substituents as an emerging atomic-level strategy for enhancing M-N4-C single-atom catalysts in electrocatalysis of the ORR, OER, and HER.

M-N4-C single-atom catalysts (MN4) 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 MN4's activity origin and enhancing its performance remains challenging. Edge-doped substituents profoundly affect MN4's activity, explored in this study by investigating their interaction with MN4 metal centers in ORR/OER/HER catalysis (Sub@MN4, Sub = B, N, O, S, CH3, NO2, NH2, OCH3, SO4; M = Fe, Co, Ni, Cu). The results show overpotential variations (0 V to 1.82 V) based on Sub and metal centers. S and SO4 groups optimize FeN4 for peak ORR activity (overpotential at 0.48 V) and reduce OER overpotentials for NiN4 (0.48 V and 0.44 V). N significantly reduces FeN4's HER overpotential (0.09 V). Correlation analysis highlights the metal center's key role, with ΔG*H and ΔG*OOH showing mutual predictability (R2 = 0.92). Eg proves a reliable predictor for Sub@CoN4G*OOHG*H, R2 = 0.96 and 0.72). Machine learning with the KNN model aids catalyst performance prediction (R2 = 0.955 and 0.943 for ΔG*OOHG*H), emphasizing M-O/M-H and the d band center as crucial factors. This study elucidates edge-doped substituents' pivotal role in MN4 activity modulation, offering insights for electrocatalyst design and optimization.

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来源期刊
Nanoscale Horizons
Nanoscale Horizons Materials Science-General Materials Science
CiteScore
16.30
自引率
1.00%
发文量
141
期刊介绍: Nanoscale Horizons stands out as a premier journal for publishing exceptionally high-quality and innovative nanoscience and nanotechnology. The emphasis lies on original research that introduces a new concept or a novel perspective (a conceptual advance), prioritizing this over reporting technological improvements. Nevertheless, outstanding articles showcasing truly groundbreaking developments, including record-breaking performance, may also find a place in the journal. Published work must be of substantial general interest to our broad and diverse readership across the nanoscience and nanotechnology community.
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Back cover "Sweet MOFs": exploring the potential and restraints of integrating carbohydrates with metal-organic frameworks for biomedical applications. Extracellular vesicles of different cellular origin feature distinct biomolecular corona dynamics. Rhodium nanospheres for ultraviolet and visible plasmonics. Nanoscale Horizons Emerging Investigator Series: Dr Mohammad Malakooti, University of Washington, USA.
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