Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41467-022-29926-w
Title: Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights
Authors: Gao, Qiang
Pillai, Hemanth Somarajan
Huang, Yang
Liu, Shikai 
Mu, Qingmin
Han, Xue
Yan, Zihao
Zhou, Hua
He, Qian 
Xin, Hongliang
Zhu, Huiyuan
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
OXYGEN-REDUCTION
ELECTROCHEMICAL REDUCTION
FEPT NANOPARTICLES
OXIDATION
COPPER
PDCU
NANOCRYSTALS
SELECTIVITY
ACTIVATION
CATALYSIS
Issue Date: 29-Apr-2022
Publisher: NATURE PORTFOLIO
Citation: Gao, Qiang, Pillai, Hemanth Somarajan, Huang, Yang, Liu, Shikai, Mu, Qingmin, Han, Xue, Yan, Zihao, Zhou, Hua, He, Qian, Xin, Hongliang, Zhu, Huiyuan (2022-04-29). Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights. NATURE COMMUNICATIONS 13 (1). ScholarBank@NUS Repository. https://doi.org/10.1038/s41467-022-29926-w
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.
Source Title: NATURE COMMUNICATIONS
URI: https://scholarbank.nus.edu.sg/handle/10635/235660
ISSN: 2041-1723
DOI: 10.1038/s41467-022-29926-w
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