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Review on Metal–Organic Framework Classification, Synthetic Approaches, and Influencing Factors: Applications in Energy, Drug Delivery, and Wastewater Treatment
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Metal ions or clusters that have been bonded with organic linkers to create one- or more-dimensional structures are referred to as metal-organic frameworks (MOFs). Reticular synthesis also forms MOFs with properly designated components that can result in crystals with high porosities and great chemical and thermal stability. Due to the wider surface area, huge pore size, crystalline nature, and tunability, numerous MOFs have been shown to be potential candidates in various fields like gas storage and delivery, energy storage, catalysis, and chemical/biosensing. This study provides a quick overview of the current MOF synthesis techniques in order to familiarize newcomers in the chemical sciences field with the fast-growing MOF research. Beginning with the classification and nomenclature of MOFs, synthesis approaches of MOFs have been demonstrated. We also emphasize the potential applications of MOFs in numerous fields such as gas storage, drug delivery, rechargeable batteries, supercapacitors, and separation membranes. Lastly, the future scope is discussed along with prospective opportunities for the synthesis and application of nano-MOFs, which will help promote their uses in multidisciplinary research.
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METAL ORGANIC FRAMEWORKS–SYNTHESIS AND APPLICATIONS
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Cited by
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Metadata
- Authors
- Yusuf, V. F., Malek, N. I., & Kailasa, S. K.
- Venue
- ACS Omega
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-12-02
- Volume
- 7
- Issue
- 49
- Pages
- 44507-44531
- ISSN
- 2470-1343 (ISSN-L)
- OpenAlex
- W4311376748
- Cited by
- 894
- References
- 150
- FWCI
- 44.97
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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Data sources
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.