Self-single-atomization of active sites in electrochemical ammonia synthesis unveiled by machine learning potential

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ID: 320296
2026
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Abstract
Abstract Electrochemical NO reduction reaction (eNORR) has emerged as a promising route for the synthesis of green ammonia. Fe was observed experimentally with great activity and selectivity for eNORR to ammonia. However, we found the electrochemical performance over iron is not due to its intrinsic activity, but from an adaptive capability of self-single-atomization in operando condition. We first performed Grand Canonical Monte Carlo (GCMC) simulations driven by a machine learning potential (MLP), where we explored ∼2.5 million possible active structures and their evolution with increased electrode potential. Microkinetic modelling indicates that Fe surface will be predominantly covered by adsorbed N* species in reaction, resulting in the formation of FeNx motifs by self-single-atomizing. Statistical results indicate that the top sites of isolated FeNx motifs contribute higher activity of ammonia synthesis than bridge and hollow sites. Furthermore, based on the self-single-atomized active sites, we reproduced and rationalized the variation trends of experimental Faradaic efficiency of different products with electrode potential, verifying the reliability of proposed active site herein. Hence, we propose to make use of the self-single-atomization capability of catalysts in working conditions to construct active sites beyond ammonia synthesis.
Reference Key
openalex_W7167792457 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jun Long, C R Yang, Hui Li, Hao Li, J N Xiao
Journal national science review
Year 2026
DOI
10.1093/nsr/nwag417
URL
https://doi.org/10.1093/nsr/nwag417
Keywords Keywords not found

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