The DOI matches its Crossref record; 150 of the 150 references it cites were found in a registry or an index.
Recent advances on simulation and theory of hydrogen storage in metal–organic frameworks and covalent organic frameworks
Abstract
This critical review covers the application of computer simulations, including quantum calculations (ab initio and DFT), grand canonical Monte-Carlo simulations, and molecular dynamics simulations, to the burgeoning area of the hydrogen storage by metal-organic frameworks and covalent-organic frameworks. This review begins with an overview of the theoretical methods obtained from previous studies. Then strategies for the improvement of hydrogen storage in the porous materials are discussed in detail. The strategies include appropriate pore size, impregnation, catenation, open metal sites in metal oxide parts and within organic linker parts, doping of alkali elements onto organic linkers, substitution of metal oxide with lighter metals, functionalized organic linkers, and hydrogen spillover (186 references).
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Cites
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Carbonaceous Electrode Materials for Supercapacitors
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Li, C., Li, Q., Kaneti, Y. V., Hou, D., Yamauchi, Y., & Mai, Y. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00021c · Cited by 513
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Metadata
- Authors
- Han, S. S., Mendoza-Cortés, J. L., & Goddard III, W. A.
- Venue
- Chemical Society Reviews
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2009
- Volume
- 38
- Issue
- 5
- Pages
- 1460
- ISSN
- 0306-0012 (ISSN-L)
- 1460-4744
- OpenAlex
- W2142681205
- Cited by
- 594
- References
- 150
- FWCI
- 0.67
- Type
- journal article
- Language
- en
- Open access
- closed
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- Han, S. S., Mendoza-Cortés, J. L., & Goddard III, W. A. (2009). Recent advances on simulation and theory of hydrogen storage in metal–organic frameworks and covalent organic frameworks. Chemical Society Reviews. https://doi.org/10.1039/b802430h
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.