Computational Modeling and Experimental Approaches for Understanding the Mechanisms of [FeFe]‐Hydrogenase
Abstract Learning from nature has emerged as a promising strategy for catalyst development, wherein the remarkable performance of catalysts selected by nature over billions of years of evolution serves as a basis for the creative design of high‐performance catalysts. Hydrogenases, with their excepti...
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| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Wiley
2025-06-01
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| coleção: | Advanced Science |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1002/advs.202408297 |
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