Authors
Ruixue Zhang, Chen Fan, Jie Li, Ran Duan
Published in
Analytica chimica acta. Volume 1425. Pages 346331. Dec 01, 2026. Epub Sep 27, 2026.
Abstract
Per- and polyfluoroalkyl substance (PFAS) pesticides remain an under-quantified source of human dietary exposure, largely due to their trace-level occurrence and the complexity of food matrices. These analytical challenges underscore the need for selective extraction approaches capable of minimizing matrix interferences.
In this work, a new thin-film microextraction (TFME) device incorporating multifunctional eutectogels was developed for the selective extraction and preconcentration of the PFAS herbicide diflufenican from food samples prior to UPLC-PDA quantification, and its performance was systematically evaluated. High-throughput screening of selectivity-enhanced deep eutectic solvents (DESs) toward diflufenican was performed using machine-learning-enhanced quantum chemical methods. Simultaneously, the eutectogel-based thin film was fabricated via UV-initiated photopolymerization of the DES, monomer, and crosslinker, yielding supramolecular gels stabilized by non-covalent interactions. The established method demonstrated excellent analytical performance, with a wide linear range (0.5-500 μg/L), a low LOD (0.04 μg/L and 0.53 μg/kg), and satisfactory recoveries (97.2-112%). Compared with the established methods, the proposed method possessed higher precision while affording a lower limit of detection and reduced hazardous reagent consumption. Mechanistic exploration corroborated by molecular simulations and multi-spectroscopic characterizations revealed that the eutectogel effectively captures diflufenican through hydrogen-bond interaction, π-π transpose, and proton-transfer inhibition, while the original DES interaction network undergoes dynamic reorganization.
The demonstrated tunability of the developed eutectogel-based thin-film microextraction device positions it as a versatile platform for the selective preconcentration of fluorinated species in complex matrices, supporting advances in the monitoring of hazardous substances in food.
PMID:
42951566
Bibliographic data and abstract were imported from PubMed on 29 Oct 2026.
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