The DOI matches its Crossref record; 123 of the 123 references it cites were found in a registry or an index.
Present and future of MOF research in the field of adsorption and molecular separation
Open accessAbstract
It is a critical and exciting time for research on adsorption using metal–organic frameworks (MOFs) as we witness the commercial release of the MOF-based products TruPick and ION-X, two MOF-based products with application in the food packaging and electronic gas delivery platform sectors, respectively. Such milestones call for reflection on the outputs of MOF research in the field of adsorption so far and the challenges and opportunities lying ahead. To this end, this review provides a snapshot of current trends in this research space (i.e. new adsorption application, manufacturing, role of modeling), highlights the most promising structures for given applications (i.e. in gas adsorption, gas storage, molecular separation, water purification and harvesting), points to technical barriers, and exposes a vision for future research in this area.
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Citations
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Cited by
Winarta, J., Shan, B., Mcintyre, S. M., Ye, L., Wang, C., Liu, J., & Mu, B. (2020) · Crystal Growth & Design · doi:10.1021/acs.cgd.9b00955 · Cited by 641
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Regeneration, degradation, and toxicity effect of MOFs: Opportunities and challenges
Kumar, P., Anand, B., Tsang, Y. F., Kim, K.-H., Khullar, S., & Wang, B. (2019) · Environmental Research · doi:10.1016/j.envres.2019.05.019 · Cited by 319
Metadata
- Authors
- Petit, C.
- Venue
- Current Opinion in Chemical Engineering
- Publisher
- Elsevier BV
- Published
- 2018-06
- Volume
- 20
- Pages
- 132-142
- ISSN
- 2211-3398 (ISSN-L)
- OpenAlex
- W2804283532
- Cited by
- 292
- References
- 123
- FWCI
- 0.38
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.