A new sustainable filtration membrane extraction method based on nanoconfined liquid phase extraction: Determination of organochlorinated pesticides and pyrethroids in tea samples


Anal Chim Acta. 2024 Jun 8;1307:342624. doi: 10.1016/j.aca.2024.342624. Epub 2024 Apr 21.


Pesticides are used in agricultural production for prevent and control crop diseases and pests, but it is easy to cause excessive pesticides residues in agricultural products, polluting the environment and endangering human health. Due to their unmatched and sustainable capabilities, nanoextraction procedures are becoming every day more important in Analytical Chemistry. In particular, nanoconfined liquid phase extraction has shown extraction capabilities toward polar, medium polar, and/or nonpolar substances, which can be easily modulated depending on the nanoconfined solvent used. Furthermore, this “green” technique showed excellent characteristics in terms of recoveries, extraction time (≤1 min), reliability, and versatility. (97) RESULTS: In this work, the advantages of this technique have been coupled with those of filtration membrane extraction, making use of carbon nanofibers (CnFs) growth on carbon microspheres (CμS). This substrate has been deposited on a filter, which combined with gas chromatographic mass spectrometry (GC-MS) analysis successfully employed for the nanoextraction of 30 pesticides (18 organochlorine and 12 pyrethroids) in tea samples. Under the optimized extraction conditions, the linear range with standard solutions was from 1 to 1000 ng mL-1 (R2 ≥ 0.99), the limit of detections in tea samples were in the range 0.56-17.98 μg kg-1. The accuracy of the developed method was evaluated by measuring the extraction recovery of the spiked tea samples, and recoveries between 74.41 % and 115.46 %. (119) SIGNIFICANCE: Considering the versatility of nanoconfined liquid phase extraction and the functionality of the filtration membrane extraction procedure, this new extraction method can be considered a powerful candidate for automatized high-throughput analyses of real samples. (34).

PMID:38719414 | DOI:10.1016/j.aca.2024.342624