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

• Published in: Nature communications Vol. 13; no. 1; pp. 2338 - 12
• Main Authors: Gao, Qiang, Pillai, Hemanth Somarajan, Huang, Yang, Liu, Shikai, Mu, Qingmin, Han, Xue, Yan, Zihao, Zhou, Hua, He, Qian, Xin, Hongliang, Zhu, Huiyuan
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.04.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 119/118; 140/131; 140/146; 147/137; 639/301/299/886; 639/638/161/886; 639/638/563; Active sites; Adsorbates; Adsorption; Ammonia; Chemical reduction; Electrocatalysts; Electrochemistry; Energy; Humanities and Social Sciences; INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Intermetallic compounds; Learning algorithms; Machine learning; multidisciplinary; Nanocrystals; Nitrate reduction; Nitrates; Nitrogen; Nitrogen cycle; Scaling; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-29926-w

The electrochemical nitrate reduction reaction (NO 3 RR) 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 *NO 3 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO 3 RR to ammonia with a Faradaic efficiency of 92.5% at −0.5 V RHE and a yield rate of 6.25 mol h −1 g −1 at −0.6 V RHE . 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. Machine learning is a powerful tool for screening electrocatalytic materials. Here, the authors reported a seamless integration of machine-learned physical insights with the controlled synthesis of structurally ordered intermetallic nanocrystals and well-defined catalytic sites for efficient nitrate reduction to ammonia.