Journal Article

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

Mu, Qingmin, Zhou, Hua, Huang, Yang, Zhu, Huiyuan, Yan, Zihao, Liu, Shikai, Pillai, Hemanth Somarajan, Gao, Qiang, Han, Xue, He, Qian, Xin, Hongliang

2022

Overview

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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 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)