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A Decade of UiO-66 Research: A Historic Review of Dynamic Structure, Synthesis Mechanisms, and Characterization Techniques of an Archetypal Metal–Organic Framework
Abstract
UiO-66 is an archetypal metal–organic framework (MOF) with a very high surface area as well as high thermal stability. It is found that the stability can be attributed to the metal oxide node being cuboctahedral allowing for 12 extension points for 1,4-benzenedicarboxylic acid (BDC) coordination. Because of this and its exceptional tunability and functionality, which are largely due to defect control of both missing-cluster and missing-linker defects, UiO-66 has gained scientific popularity. The combination of these characteristics allows for a highly versatile material that can be adapted to many different applications. The purpose for this work is to provide a historic overview of UiO-66, outlining the major developments that changed the synthesis strategies of Zr-based MOF as well as current and future works, which include defect control, aqueous crystallization, functionality-stability trade-offs, and advanced topographies. A breakdown of the various UiO-66 structures, including isoreticular and reo-type, and different characterization techniques such as powder X-ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric analysis, and nitrogen porosimetry are discussed as well.
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Metadata
- Authors
- Winarta, J., Shan, B., Mcintyre, S. M., Ye, L., Wang, C., Liu, J., & Mu, B.
- Venue
- Crystal Growth & Design
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-12-11
- Volume
- 20
- Issue
- 2
- Pages
- 1347-1362
- ISSN
- 1528-7483 (ISSN-L)
- 1528-7505
- OpenAlex
- W2995923076
- Cited by
- 641
- References
- 98
- FWCI
- 17.38
- Type
- journal article
- Language
- en
- Open access
- closed
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.