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

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dc.contributor.authorGao, Qiangen
dc.contributor.authorPillai, Hemanth Somarajanen
dc.contributor.authorHuang, Yangen
dc.contributor.authorLiu, Shikaien
dc.contributor.authorMu, Qingminen
dc.contributor.authorHan, Xueen
dc.contributor.authorYan, Zihaoen
dc.contributor.authorZhou, Huaen
dc.contributor.authorHe, Qianen
dc.contributor.authorXin, Hongliangen
dc.contributor.authorZhu, Huiyuanen
dc.date.accessioned2022-06-28T15:16:13Zen
dc.date.available2022-06-28T15:16:13Zen
dc.date.issued2022-04-29en
dc.description.abstractMachine 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. 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.en
dc.description.notesWe acknowledge the funding support from the U.S. National Science Foundation (NSF) (CHE-2102363). H.Z. acknowldges the NSF CAREER program (CBET-2143710). H.S.P., Q.M., and H.X. acknowledge the NSF CAREER program (CBET-1845531). The computational resource used in this work is provided by the advanced research computing at Virginia Polytechnic Institute and State University. Q.H. would like to acknowledge the support by National Research Foundation (NRF) Singapore, under its NRF Fellowship (NRF-NRFF11-2019-0002). This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. We would like to thank Prof. Sen Zhang and his student Grayson Johnson from the University of Virginia for their help with ATR-SEIRAS.en
dc.description.sponsorshipU.S. National Science Foundation (NSF) [CHE-2102363]; NSF CAREER program [CBET-1845531, CBET-2143710]; National Research Foundation (NRF) Singapore, under its NRF Fellowship [NRF-NRFF11-2019-0002]; DOE Office of Science [DE-AC02-06CH11357]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1038/s41467-022-29926-wen
dc.identifier.eissn2041-1723en
dc.identifier.issue1en
dc.identifier.other2338en
dc.identifier.pmid35487883en
dc.identifier.urihttp://hdl.handle.net/10919/110960en
dc.identifier.volume13en
dc.language.isoenen
dc.publisherNature Portfolioen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectoxygen-reductionen
dc.subjectelectrochemical reductionen
dc.subjectfept nanoparticlesen
dc.subjectoxidationen
dc.subjectcopperen
dc.subjectpdcuen
dc.subjectnanocrystalsen
dc.subjectselectivityen
dc.subjectactivationen
dc.subjectcatalysisen
dc.titleBreaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insightsen
dc.title.serialNature Communicationsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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