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

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

doi:10.1038/s41467-022-29926-w

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

Journal Article · Fri Apr 29 00:00:00 EDT 2022 · Nature Communications

DOI:https://doi.org/10.1038/s41467-022-29926-w· OSTI ID:1896789

Gao, Qiang ^[1]; Pillai, Hemanth Somarajan ^[1]; Huang, Yang ^[1]; Liu, Shikai ^[2]; Mu, Qingmin ^[1]; Han, Xue ^[1]; Yan, Zihao ^[1]; ^[3]; ^[2]; ^[1]; ^[1]

Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
National Univ. of Singapore (Singapore)
Argonne National Lab. (ANL), Argonne, IL (United States). Advanced Photon Source (APS)

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 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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: National Research Foundation (NRF) Singapore; National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1896789

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 13; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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