Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights

Qiang Gao, Hemanth Somarajan Pillai, Yang Huang, Shikai Liu, Qingmin Mu, Xue Han, Zihao Yan, Hua Zhou, Qian He (), Hongliang Xin () and Huiyuan Zhu ()
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Qiang Gao: Virginia Polytechnic Institute and State University
Hemanth Somarajan Pillai: Virginia Polytechnic Institute and State University
Yang Huang: Virginia Polytechnic Institute and State University
Shikai Liu: National University of Singapore
Qingmin Mu: Virginia Polytechnic Institute and State University
Xue Han: Virginia Polytechnic Institute and State University
Zihao Yan: Virginia Polytechnic Institute and State University
Hua Zhou: Argonne National Laboratory
Qian He: National University of Singapore
Hongliang Xin: Virginia Polytechnic Institute and State University
Huiyuan Zhu: Virginia Polytechnic Institute and State University

Nature Communications, 2022, vol. 13, issue 1, 1-12

Abstract: Abstract The electrochemical nitrate reduction reaction (NO3RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of highly efficient electrocatalysts, tailoring catalytic sites with ligand and strain effects in random alloys is a common approach but remains limited due to the ubiquitous energy-scaling relations. With interpretable machine learning, we unravel a mechanism of breaking adsorption-energy scaling relations through the site-specific Pauli repulsion interactions of the metal d-states with adsorbate frontier orbitals. The non-scaling behavior can be realized on (100)-type sites of ordered B2 intermetallics, in which the orbital overlap between the hollow *N and subsurface metal atoms is significant while the bridge-bidentate *NO3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO3RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 VRHE and a yield rate of 6.25 mol h−1 g−1 at −0.6 VRHE. This study provides machine-learned design rules besides the d-band center metrics, paving the path toward data-driven discovery of catalytic materials beyond linear scaling limitations.

Date: 2022
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DOI: 10.1038/s41467-022-29926-w

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