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Synthesis and Performance Characterizations of Transition Metal Single Atom Catalyst for Electrochemical CO2 Reduction
Published on: April 10, 2018
Integrating Machine Learning With Constant-Potential Simulation to Unravel Charge-Transfer Mechanisms in
Yufei Xue1, Dushuo Feng2, Yuefei Zhang1
1MOE Key Laboratory for Non-equilibrium Synthesis and Modulation of Condensed Matter, Shaanxi Province Key Laboratory of Advanced Functional Materials and Mesoscopic Physics, School of Physics, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Abstract:
The electrochemical nitrogen reduction reaction (NRR) offers a sustainable approach to ammonia (NH3) synthesis under mild conditions. To achieve scalable NH3 production, discovering high-performance catalysts for the efficient NRR is crucial. For this purpose, the activity mechanisms of functional group-modified carborin/graphene-supported single-atom catalysts were systematically investigated using the grand-canonical fixed-potential method, which simulates operando constant-potential conditions. Among 144 candidates screened, Cr@NO2-carborin/graphene and Cr@CHO-carborin/graphene are identified as the most promising NRR catalysts, with low limiting potentials of -0.220 V for the *N2→*N2H step and -0.245 V for the *NH→*NH2 step, respectively. Furthermore, interpretable machine learning models revealed that the shift in potential of zero charge, induced by intermediate adsorption, serves as the key voltage-responsive descriptor governing charge transfer patterns and influencing the N2 activation. These findings establish a paradigm shift from static electronic descriptors to dynamic interfacial property engineering, offering a universal framework for designing electrocatalysts for multi-electron reactions like NRR.
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