Magnetic molecularly imprinted polymer-assisted target enrichment for the fluorescence detection of chloramphenicol in water

Abstract

The abuse of antibiotics has induced various environmental problems and poses a threat to human health and life. Fluorescence analysis methods have received extensive attention in the field of antibiotic detection due to their high sensitivity and specificity. However, traditional fluorescence methods are confronted with the challenges of background interference and sample complexity, and thus, there is an urgent need to develop efficient antibiotic detection methods. Herein, an efficient sample separation and enrichment method based on magnetic molecularly imprinted polymers (MMIPs) was introduced into a fluorescence biosensor for the anti-interference detection of antibiotics in water. As a proof-of-concept of our approach, chloramphenicol (CAP) was chosen as a model antibiotic to be investigated. MMIPs were synthesized using the surface molecular imprinting technology on Fe3O4 nanoparticles, achieving high selectivity and an adsorption capacity of 49.6 mg g−1. More importantly, the MMIP-assisted fluorescent probe exhibited good anti-interference ability, while the ordinary fluorescent probe was easily interfered by the coexisting substances (such as humic acid) in water, resulting in its poor detection performance. These results demonstrated the potential of MMIPs as an efficient, cost-effective tool for CAP extraction in environmental samples, offering a promising approach for the environmental monitoring of antibiotic contamination.

Graphical abstract: Magnetic molecularly imprinted polymer-assisted target enrichment for the fluorescence detection of chloramphenicol in water

Supplementary files

Article information

Article type
Paper
Submitted
23 Jan 2026
Accepted
04 Feb 2026
First published
05 Feb 2026

Analyst, 2026, Advance Article

Magnetic molecularly imprinted polymer-assisted target enrichment for the fluorescence detection of chloramphenicol in water

Z. Long, J. Guo, X. Liu, C. Long, P. Zhang, B. Feng and T. Qing, Analyst, 2026, Advance Article , DOI: 10.1039/D6AN00075D

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