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On-site sample pretreatment: Natural deep eutectic solvent-based multiple air-assisted liquid-liquid microextraction.

Yang Yang¹, Weihang Liu¹, Na Hang¹

¹Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 100083, China.

Journal of Chromatography. A

|May 25, 2022

Summary

This study introduces a green, on-site method for detecting forest pesticides in water using deep eutectic solvents and microextraction. The technique simplifies sample preparation, offering rapid and accurate analysis for environmental monitoring.

Keywords:

Air-assisted liquid–liquid microextraction Modular array device Natural deep eutectic solvent On-site procedure Water samples in the forest

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Area of Science:

Environmental Chemistry
Analytical Chemistry
Green Chemistry

Background:

Chemical pesticides are crucial for forest pest control but pose risks to aquatic ecosystems.
Monitoring trace pesticides in environmental water is essential for ecological safety.
Existing methods for pesticide analysis can be time-consuming and complex for on-site applications.

Purpose of the Study:

To develop a novel, on-site method for the simultaneous determination of four key forest pesticides (triadimefon, bifenthrin, bromopropylate, permethrin) in water samples.
To utilize natural deep eutectic solvents (DES) as environmentally friendly extractants for microextraction.
To design a portable device for efficient and simplified on-site sample pretreatment.

Main Methods:

Synthesis and selection of menthol-based DES for optimal extraction performance.
Development of an air-assisted liquid-liquid microextraction (LA-µE) technique coupled with gas chromatography-electron capture detection (GC-ECD).
Design and implementation of a portable modular array device for simultaneous, on-site sample pretreatment.

Main Results:

A menthol-based DES (1:2 DL-menthol:citric acid) demonstrated high stability and extraction efficiency.
The developed LA-µE method achieved high extraction recoveries (92-95%) with low relative standard deviations (<4.9%).
The method provided sensitive detection limits (0.34–3.3 µg L⁻¹) and excellent linearity (R² > 0.999) for the target pesticides, validated by eco-friendly assessments.

Conclusions:

The proposed on-site method is green, efficient, and flexible for rapid water sample pretreatment in forest environments.
The use of natural DES and portable LA-µE technology simplifies complex analytical procedures for field applications.
This approach offers a new perspective for on-site sample processing, enhancing environmental monitoring capabilities.