Evidence map›Paper›PMID 41286150›Full record

ArticleMikrochimica acta2025

Bifunctional luminescent iCOF enabling onsite ultrasensitive monitoring and effective removal of perfluorooctane sulfonate in water.

Fang Yang, Zhen-Ni Yi, Yu Liu, Yao-Wen Wang, Yi-Peng Xiao, Min-Hua Su, Di-Yun Chen, Hui Sun

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Article in Mikrochimica acta, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Temperature-responsive polymers for bioanalytical systems.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026
    Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Fang YangCollege of Environmental Science & Engineering, Guangzhou University, Guangzhou, 510006, China.
Zhen-Ni YiCollege of Environmental Science & Engineering, Guangzhou University, Guangzhou, 510006, China.
Yu LiuCollege of Environmental Science & Engineering, Guangzhou University, Guangzhou, 510006, China.
Yao-Wen WangCollege of Environmental Science & Engineering, Guangzhou University, Guangzhou, 510006, China.
Yi-Peng XiaoCollege of Environmental Science & Engineering, Guangzhou University, Guangzhou, 510006, China.
Min-Hua SuCollege of Environmental Science & Engineering, Guangzhou University, Guangzhou, 510006, China. mhsu@gzhu.edu.cn.
Di-Yun ChenCollege of Environmental Science & Engineering, Guangzhou University, Guangzhou, 510006, China.
Hui SunCollege of Environmental Science & Engineering, Guangzhou University, Guangzhou, 510006, China. esesunhui@gzhu.edu.cn.

Funding

National Natural Science Foundation of China 22076034National Natural Science Foundation of China 22276043, 42230719National Natural Science Foundation of China 22376041
6 · The paper itself

Abstract

In consideration of the severe hazards of perfluorooctane sulfonate (PFOS) pollution, this study presents a bifunctional luminescent ionic covalent organic framework (iCOF), N⁺-DT-COF, engineered for the simultaneous ultrasensitive detection and highly efficient removal of PFOS in water. The material was synthesized via post-functional modification by grafting quaternary ammonium cationic chains onto a parent spherical DT-COF framework, which was comprehensively confirmed through SEM, XPS, FT-IR, and PXRD analyses. This strategic modification induced a critical photophysical transition that converted the non-emissive DT-COF into a highly fluorescent material by disrupting its inherent π–π stacking. The resulting N⁺-DT-COF functioned as a superior sensor, achieving an ultra-low detection limit of 0.079 ng/L for PFOS via a fluorescence quenching mechanism, with a rapid response within 5 min and exceptional selectivity. Concurrently, it served as a powerful adsorbent, exhibiting a remarkable maximum adsorption capacity of 561.8 mg/g, following pseudo-second-order kinetics. Validation using real Pearl River water samples demonstrated not only high analytical accuracy (102.9–107.4% recovery) and precision (RSDs < 2.5% intra-day, < 3.1% inter-day), but N⁺-DT-COF also achieved complete PFOS removal, underscoring its dual-function capability. This work establishes a promising strategy for the integrated monitoring and remediation of PFOS contamination.

Indexed as

Fluorescence detectionIonic covalent organic frameworksPFOSPFOS adsorptive removal

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.