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Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts

Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven...

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Autores principales: Yao, Yonggang, Liu, Zhenyu, Xie, Pengfei, Huang, Zhennan, Li, Tangyuan, Morris, David, Finfrock, Zou, Zhou, Jihan, Jiao, Miaolun, Gao, Jinlong, Mao, Yimin, Miao, Jianwei, Zhang, Peng, Shahbazian-Yassar, Reza, Wang, Chao, Wang, Guofeng, Hu, Liangbing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7069714/
https://www.ncbi.nlm.nih.gov/pubmed/32201728
http://dx.doi.org/10.1126/sciadv.aaz0510
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author Yao, Yonggang
Liu, Zhenyu
Xie, Pengfei
Huang, Zhennan
Li, Tangyuan
Morris, David
Finfrock, Zou
Zhou, Jihan
Jiao, Miaolun
Gao, Jinlong
Mao, Yimin
Miao, Jianwei
Zhang, Peng
Shahbazian-Yassar, Reza
Wang, Chao
Wang, Guofeng
Hu, Liangbing
author_facet Yao, Yonggang
Liu, Zhenyu
Xie, Pengfei
Huang, Zhennan
Li, Tangyuan
Morris, David
Finfrock, Zou
Zhou, Jihan
Jiao, Miaolun
Gao, Jinlong
Mao, Yimin
Miao, Jianwei
Zhang, Peng
Shahbazian-Yassar, Reza
Wang, Chao
Wang, Guofeng
Hu, Liangbing
author_sort Yao, Yonggang
collection PubMed
description Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven design and synthesis of highly efficient and durable catalyst MEA-NPs. The computational strategy includes prescreening of millions of compositions, prediction of alloy formation by density functional theory calculations, and examination of structural stability by a hybrid Monte Carlo and molecular dynamics method. Selected compositions can be efficiently and rapidly synthesized at high temperature (e.g., 1500 K, 0.5 s) with excellent thermal stability. We applied these MEA-NPs for catalytic NH(3) decomposition and observed outstanding performance due to the synergistic effect of multi-elemental mixing, their small size, and the alloy phase. We anticipate that the computationally aided rational design and rapid synthesis of MEA-NPs are broadly applicable for various catalytic reactions and will accelerate material discovery.
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spelling pubmed-70697142020-03-20 Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts Yao, Yonggang Liu, Zhenyu Xie, Pengfei Huang, Zhennan Li, Tangyuan Morris, David Finfrock, Zou Zhou, Jihan Jiao, Miaolun Gao, Jinlong Mao, Yimin Miao, Jianwei Zhang, Peng Shahbazian-Yassar, Reza Wang, Chao Wang, Guofeng Hu, Liangbing Sci Adv Research Articles Multi-elemental alloy nanoparticles (MEA-NPs) hold great promise for catalyst discovery in a virtually unlimited compositional space. However, rational and controllable synthesize of these intrinsically complex structures remains a challenge. Here, we report the computationally aided, entropy-driven design and synthesis of highly efficient and durable catalyst MEA-NPs. The computational strategy includes prescreening of millions of compositions, prediction of alloy formation by density functional theory calculations, and examination of structural stability by a hybrid Monte Carlo and molecular dynamics method. Selected compositions can be efficiently and rapidly synthesized at high temperature (e.g., 1500 K, 0.5 s) with excellent thermal stability. We applied these MEA-NPs for catalytic NH(3) decomposition and observed outstanding performance due to the synergistic effect of multi-elemental mixing, their small size, and the alloy phase. We anticipate that the computationally aided rational design and rapid synthesis of MEA-NPs are broadly applicable for various catalytic reactions and will accelerate material discovery. American Association for the Advancement of Science 2020-03-13 /pmc/articles/PMC7069714/ /pubmed/32201728 http://dx.doi.org/10.1126/sciadv.aaz0510 Text en Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). http://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (http://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Yao, Yonggang
Liu, Zhenyu
Xie, Pengfei
Huang, Zhennan
Li, Tangyuan
Morris, David
Finfrock, Zou
Zhou, Jihan
Jiao, Miaolun
Gao, Jinlong
Mao, Yimin
Miao, Jianwei
Zhang, Peng
Shahbazian-Yassar, Reza
Wang, Chao
Wang, Guofeng
Hu, Liangbing
Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts
title Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts
title_full Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts
title_fullStr Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts
title_full_unstemmed Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts
title_short Computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts
title_sort computationally aided, entropy-driven synthesis of highly efficient and durable multi-elemental alloy catalysts
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7069714/
https://www.ncbi.nlm.nih.gov/pubmed/32201728
http://dx.doi.org/10.1126/sciadv.aaz0510
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