Screening metal-organic frameworks for adsorption-driven osmotic heat engines via grand canonical Monte Carlo simulations and machine learning

Adsorption-driven osmotic heat engines offer an alternative way for harvesting low-grade waste heat below 80°C. In this study, we performed a high-throughput computational screening based on grand canonical Monte Carlo simulations to identify the high-performance metal-organic frameworks (MOFs) from...

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Detalles Bibliográficos
Autores principales: Long, Rui, Xia, Xiaoxiao, Zhao, Yanan, Li, Song, Liu, Zhichun, Liu, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7772570/
https://www.ncbi.nlm.nih.gov/pubmed/33385115
http://dx.doi.org/10.1016/j.isci.2020.101914

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7772570/
https://www.ncbi.nlm.nih.gov/pubmed/33385115
http://dx.doi.org/10.1016/j.isci.2020.101914

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