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

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...

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Published in	Nature communications Vol. 13; no. 1; pp. 2338 - 12
Main Authors	Gao, Qiang, Pillai, Hemanth Somarajan, Huang, Yang, Liu, Shikai, Mu, Qingmin, Han, Xue, Yan, Zihao, Zhou, Hua, He, Qian, Xin, Hongliang, Zhu, Huiyuan
Format	Journal Article
Language	English
Published	London Nature Publishing Group UK 29.04.2022 Nature Publishing Group Nature Portfolio
Subjects	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)
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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. 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.
AbstractList	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. 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. 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.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. 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. 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.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. 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. The electrochemical nitrate reduction reaction (NO 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 is not directly affected. Among those intermetallics predicted, we synthesize monodisperse ordered B2 CuPd nanocubes that demonstrate high performance for NO RR to ammonia with a Faradaic efficiency of 92.5% at -0.5 V and a yield rate of 6.25 mol h g at -0.6 V . 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.
ArticleNumber	2338
Author	Mu, Qingmin Zhou, Hua Huang, Yang Zhu, Huiyuan Yan, Zihao Liu, Shikai Pillai, Hemanth Somarajan Gao, Qiang Han, Xue He, Qian Xin, Hongliang
Author_xml	– sequence: 1 givenname: Qiang surname: Gao fullname: Gao, Qiang organization: Department of Chemical Engineering, Virginia Polytechnic Institute and State University – sequence: 2 givenname: Hemanth Somarajan surname: Pillai fullname: Pillai, Hemanth Somarajan organization: Department of Chemical Engineering, Virginia Polytechnic Institute and State University – sequence: 3 givenname: Yang surname: Huang fullname: Huang, Yang organization: Department of Chemical Engineering, Virginia Polytechnic Institute and State University – sequence: 4 givenname: Shikai surname: Liu fullname: Liu, Shikai organization: Department of Materials Science and Engineering, National University of Singapore – sequence: 5 givenname: Qingmin surname: Mu fullname: Mu, Qingmin organization: Department of Chemical Engineering, Virginia Polytechnic Institute and State University – sequence: 6 givenname: Xue surname: Han fullname: Han, Xue organization: Department of Chemical Engineering, Virginia Polytechnic Institute and State University – sequence: 7 givenname: Zihao surname: Yan fullname: Yan, Zihao organization: Department of Chemical Engineering, Virginia Polytechnic Institute and State University – sequence: 8 givenname: Hua orcidid: 0000-0001-9642-8674 surname: Zhou fullname: Zhou, Hua organization: X-ray Science Division, Advanced Photon Source, Argonne National Laboratory – sequence: 9 givenname: Qian orcidid: 0000-0003-4891-3581 surname: He fullname: He, Qian email: heqian@nus.edu.sg organization: Department of Materials Science and Engineering, National University of Singapore – sequence: 10 givenname: Hongliang orcidid: 0000-0001-9344-1697 surname: Xin fullname: Xin, Hongliang email: hxin@vt.edu organization: Department of Chemical Engineering, Virginia Polytechnic Institute and State University – sequence: 11 givenname: Huiyuan orcidid: 0000-0002-9962-1661 surname: Zhu fullname: Zhu, Huiyuan email: huiyuanz@vt.edu organization: Department of Chemical Engineering, Virginia Polytechnic Institute and State University
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/35487883$$D View this record in MEDLINE/PubMed https://www.osti.gov/servlets/purl/1896789$$D View this record in Osti.gov
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Snippet	The electrochemical nitrate reduction reaction (NO 3 RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of... The electrochemical nitrate reduction reaction (NO RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of... The electrochemical nitrate reduction reaction (NO3RR) to ammonia is an essential step toward restoring the globally disrupted nitrogen cycle. In search of... Machine learning is a powerful tool for screening electrocatalytic materials. Here, the authors reported a seamless integration of machine-learned physical...
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SubjectTerms	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)
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Title	Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights
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