Issue 44, 2023

Fluorescent covalent organic frameworks for environmental pollutant detection sensors and enrichment sorbents: a mini-review

Abstract

Covalent organic frameworks (COFs) are a class of porous crystalline materials based on organic building blocks containing light elements, such as C, H, O, N, and B, interconnected by covalent bonds. Because of their regular crystal structure, high porosity, stable mechanical structure, satisfactory specific surface area, easy functionalization, and high tunability, they have important applications in several fields. Currently, most of the established methods based on COFs can only be used for individual detection or adsorption of the target. Impressively, fluorescent COFs as a special member of the COF family are able to achieve highly selective and sensitive detection of target pollutants by fluorescence enhancement or quenching. The construction of a dual-functional platform for detection and adsorption based on fluorescent COFs can enable the simultaneous realization of visual monitoring and adsorption of target pollutants. Therefore, this paper reviews the research progress of fluorescent COFs as fluorescence sensors and adsorbents. First, the fluorescent COFs were classified according to the different bonding modes between the building blocks, and then the applications of fluorescent COF-based detection and adsorption bifunctional materials for various environmental contaminants were highlighted. Finally, the challenges and future application prospects of fluorescent COFs are discussed.

Graphical abstract: Fluorescent covalent organic frameworks for environmental pollutant detection sensors and enrichment sorbents: a mini-review

Article information

Article type
Minireview
Submitted
09 יול 2023
Accepted
18 אוק 2023
First published
18 אוק 2023

Anal. Methods, 2023,15, 5919-5946

Fluorescent covalent organic frameworks for environmental pollutant detection sensors and enrichment sorbents: a mini-review

Q. Liu, Y. Yang, Y. Zou, L. Wang, Z. Li, M. Wang, L. Li, M. Tian, D. Wang and D. Gao, Anal. Methods, 2023, 15, 5919 DOI: 10.1039/D3AY01166F

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