Potential-Programmed Operando Ensembles Govern Nitrate Electroreduction
Xue-Chun Jiang, Jia-Lan Chen, Wei-Xue Li, and Jin-Xun Liu

TL;DR
This study uses multiscale modeling and machine learning to reveal how potential-driven ensembles of interfacial motifs govern nitrate electroreduction on copper, identifying key microenvironments and charge redistribution as catalysts for high activity and selectivity.
Contribution
It introduces a coverage-aware modeling framework that characterizes the dynamic interfacial ensembles and links interfacial charge to catalytic activity, advancing understanding of operando electrocatalysis.
Findings
Identified a potential-gated ensemble of 34 interconverting motifs.
Peak activity at -0.70 V with nearly 100% Faradaic efficiency.
Unified kinetic descriptor based on excess charge on Cu atoms.
Abstract
Electrocatalyst surfaces continuously reorganize on the timescale of catalytic turnover, obscuring the identification of active sites under operando conditions and hindering rational catalyst design. Here, we resolve the operando Cu(111) electrolyte interface for nitrate-to-ammonia electroreduction (NO3RR) via a multiscale modeling framework accelerated by a coverage-aware machine-learning potential. Rather than a single "average coverage" site, the working interface is a potential-gated statistical ensemble of 34 interconverting adsorbate motifs between -0.10 and -1.00 V (vs. SHE). Potential-driven shifts in motif populations produce a volcano-shaped activity trend peaking at -0.70 V, where the site-normalized turnover frequency reaches 0.015 s-1 with nearly 100% Faradaic efficiency to ammonia. The activation barriers across >150 transition states collapse into a single linear…
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Taxonomy
TopicsAmmonia Synthesis and Nitrogen Reduction · CO2 Reduction Techniques and Catalysts · Electrocatalysts for Energy Conversion
