Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights
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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-022-29926-w |
| _version_ | 1817982310000099328 |
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| author | Qiang Gao Hemanth Somarajan Pillai Yang Huang Shikai Liu Qingmin Mu Xue Han Zihao Yan Hua Zhou Qian He Hongliang Xin Huiyuan Zhu |
| author_facet | Qiang Gao Hemanth Somarajan Pillai Yang Huang Shikai Liu Qingmin Mu Xue Han Zihao Yan Hua Zhou Qian He Hongliang Xin Huiyuan Zhu |
| author_sort | Qiang Gao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T23:19:09Z |
| format | Article |
| id | doaj.art-fd6108570708433b95b0ae4d059aedce |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-04-13T23:19:09Z |
| publishDate | 2022-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-fd6108570708433b95b0ae4d059aedce2022-12-22T02:25:18ZengNature PortfolioNature Communications2041-17232022-04-0113111210.1038/s41467-022-29926-wBreaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insightsQiang Gao0Hemanth Somarajan Pillai1Yang Huang2Shikai Liu3Qingmin Mu4Xue Han5Zihao Yan6Hua Zhou7Qian He8Hongliang Xin9Huiyuan Zhu10Department of Chemical Engineering, Virginia Polytechnic Institute and State UniversityDepartment of Chemical Engineering, Virginia Polytechnic Institute and State UniversityDepartment of Chemical Engineering, Virginia Polytechnic Institute and State UniversityDepartment of Materials Science and Engineering, National University of SingaporeDepartment of Chemical Engineering, Virginia Polytechnic Institute and State UniversityDepartment of Chemical Engineering, Virginia Polytechnic Institute and State UniversityDepartment of Chemical Engineering, Virginia Polytechnic Institute and State UniversityX-ray Science Division, Advanced Photon Source, Argonne National LaboratoryDepartment of Materials Science and Engineering, National University of SingaporeDepartment of Chemical Engineering, Virginia Polytechnic Institute and State UniversityDepartment of Chemical Engineering, Virginia Polytechnic Institute and State UniversityMachine 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.https://doi.org/10.1038/s41467-022-29926-w |
| spellingShingle | Qiang Gao Hemanth Somarajan Pillai Yang Huang Shikai Liu Qingmin Mu Xue Han Zihao Yan Hua Zhou Qian He Hongliang Xin Huiyuan Zhu Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights Nature Communications |
| title | Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights |
| title_full | Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights |
| title_fullStr | Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights |
| title_full_unstemmed | Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights |
| title_short | Breaking adsorption-energy scaling limitations of electrocatalytic nitrate reduction on intermetallic CuPd nanocubes by machine-learned insights |
| title_sort | breaking adsorption energy scaling limitations of electrocatalytic nitrate reduction on intermetallic cupd nanocubes by machine learned insights |
| url | https://doi.org/10.1038/s41467-022-29926-w |
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