| Autor: |
Saidi WA; Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States., Shadid W; Department of System and Information Systems, University of North Carolina Charlotte, Charlotte, North Carolina 28223, United States., Veser G; Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States. |
| Jazyk: |
angličtina |
| Zdroj: |
The journal of physical chemistry letters [J Phys Chem Lett] 2021 Jun 03; Vol. 12 (21), pp. 5185-5192. Date of Electronic Publication: 2021 May 26. |
| DOI: |
10.1021/acs.jpclett.1c01242 |
| Abstrakt: |
The successful synthesis of high-entropy alloy (HEA) nanoparticles, a long-sought goal in materials science, opens a new frontier in materials science with applications across catalysis, structural alloys, and energetic materials. Recently, a Co 25 Mo 45 Fe 10 Ni 10 Cu 10 HEA made of earth-abundant elements was shown to have a high catalytic activity for ammonia decomposition, which rivals that of state-of-the-art, but prohibitively expensive, ruthenium catalysts. Using a computational approach based on first-principles calculations in conjunction with data analytics and machine learning, we build a model to rapidly compute the adsorption energy of H, N, and NH x ( x = 1, 2, 3) species on CoMoFeNiCu alloy surfaces with varied alloy compositions and atomic arrangements. We show that the 25/45 Co/Mo ratio identified experimentally as the most active composition for ammonia decomposition increases the likelihood that the surface adsorbs nitrogen equivalently to that of ruthenium while at the same time interacting moderately strongly with intermediates. Our study underscores the importance of computational modeling and machine learning to identify and optimize HEA alloys across their near-infinite materials design space. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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