{"id":"doi:10.1038/s41467-022-29926-w","kind":"article","title":"Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights","authors":["Qiang Gao","Hemanth Somarajan Pillai","Yang Huang","Shikai Liu","Qingmin Mu","Xue Han","Zihao Yan","Hua Zhou","Qian He","Hongliang Xin","Huiyuan Zhu"],"author_count":11,"year":2022,"published":"2022-04-29","venue":"Nature Communications","cited_by":420,"doi":"10.1038/s41467-022-29926-w","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41467-022-29926-w","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41467-022-29926-w","updated":null,"pmid":"35487883","pmcid":null,"openalex_id":"W4225114738","pdf_url":"https://www.nature.com/articles/s41467-022-29926-w.pdf","abstract":"Abstract 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.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Ammonia Synthesis and Nitrogen Reduction"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights\" by Qiang Gao, Hemanth Somarajan Pillai, Yang Huang et al., https://doi.org/10.1038/s41467-022-29926-w, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"ai-for-science","name":"AI for Science","domain_slug":"ai"}],"cited_in_dossiers":[]}