Cargando…
Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture
Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high‐p...
Autores principales: | Mai, Haoxin, Le, Tu C., Chen, Dehong, Winkler, David A., Caruso, Rachel A. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798988/ https://www.ncbi.nlm.nih.gov/pubmed/36285802 http://dx.doi.org/10.1002/advs.202203899 |
Ejemplares similares
-
Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts
por: Mai, Haoxin, et al.
Publicado: (2021) -
Consequences of rewetting and ditch cleaning on hydrology, water quality and greenhouse gas balance in a drained northern landscape
por: Laudon, Hjalmar, et al.
Publicado: (2023) -
A cooperative adsorbent for the switch-like capture of carbon dioxide from crude natural gas
por: Siegelman, Rebecca L., et al.
Publicado: (2022) -
Integrating greenhouse gas capture and C1 biotechnology: a key challenge for circular economy
por: García, José L., et al.
Publicado: (2021) -
Energy technology availability to mitigate future greenhouse gas emissions : conference proceedings.
Publicado: (1997)