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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...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2020
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| Materias: | |
| 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 |