How the Bioinspired Fe2Mo6S8 Chevrel Breaks Electrocatalytic Nitrogen Reduction Scaling Relations

化学 缩放比例 还原(数学) 电催化剂 无机化学 氮气 物理化学 电化学 电极 有机化学 数学 几何学
作者
Nicholas R. Singstock,Charles B. Musgrave
出处
期刊:Journal of the American Chemical Society [American Chemical Society]
卷期号:144 (28): 12800-12806 被引量:78
标识
DOI:10.1021/jacs.2c03661
摘要

The nitrogen reduction reaction (NRR) is a renewable alternative to the energy- and CO 2 -intensive Haber–Bosch NH 3 synthesis process but is severely limited by the low activity and selectivity of studied electrocatalysts. The Chevrel phase Fe 2 Mo 6 S 8 has a surface Fe–S–Mo coordination environment that mimics the nitrogenase FeMo-cofactor and was recently shown to provide state-of-the-art activity and selectivity for NRR. Here, we elucidate the previously unknown NRR mechanism on Fe 2 Mo 6 S 8 via grand-canonical density functional theory (GC-DFT) that realistically models solvated and biased surfaces. Fe sites of Fe 2 Mo 6 S 8 selectively stabilize the key *NNH intermediate via a narrow band of free-atom-like surface d -states that selectively hybridize with p -states of *NNH, which results in Fe sites breaking NRR scaling relationships. These sharp d -states arise from an Fe–S bond dissociation during N 2 adsorption that mimics the mechanism of the nitrogenase FeMo-cofactor. Furthermore, we developed a new GC-DFT-based approach for calculating transition states as a function of bias (GC-NEB) and applied it to produce a microkinetic model for NRR at Fe 2 Mo 6 S 8 that predicts high activity and selectivity, in close agreement with experiments. Our results suggest new design principles that may identify effective NRR electrocatalysts that minimize the barriers for *N 2 protonation and *NH 3 desorption and that may be broadly applied to the rational discovery of stable, multinary electrocatalysts for other reactions where narrow bands of surface d -states can be tuned to selectively stabilize key reaction intermediates and guide selectivity toward a target product. Furthermore, our results highlight the importance of using GC-DFT and GC-NEB to accurately model electrocatalytic reactions.
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