Green solvent-based high-throughput dispersive liquid-liquid microextraction for detecting strobilurin fungicides in environmental water samples

文献类型： 外文期刊

第一作者： Liu, Jin

作者： Liu, Jin;Pan, Wei;Li, Suzhen;Li, Li;Liu, Jin;Jing, Xu;Nie, Yuanjun;Bi, Xinyuan

作者机构：

关键词： High-throughput; Self-separation; Semi-automatization; Renewable solvents; Environmental monitoring

期刊名称：JOURNAL OF CHROMATOGRAPHY A （ 影响因子：4.0； 五年影响因子：3.6 ）

ISSN： 0021-9673

年卷期： 2025 年 1749 卷

页码：

收录情况： SCI

摘要： A green solvent-based high-throughput dispersive liquid-liquid microextraction (HTDLLME) method has been developed as an environmentally friendly, straightforward, and efficient sample pretreatment technique. Six batches of extractant (eucalyptol) and dispersant (gamma-valerolactone) mixtures were simultaneously injected into six samples and uniformly dispersed using electric 12-channel pipettes, achieving rapid extraction. The extractant and dispersant are sustainable, renewable, and non-toxic, eliminating the need for harmful solvents. The rapid dispersion and spontaneous separation of the extractant do not require auxiliary devices or demulsifiers, aside from the electric 12-channel pipettes. The combined use of electric 12-channel pipettes and deep-well plates increased pretreatment throughput, enabled semi-automatic operation, and improved overall efficiency. UHPLC-MS/MS was selected as the detector to ensure the sensitivity of the proposed method. Under optimal extraction conditions, the linear range was 0.002-0.2 mu g L-1. The limit of detection and limit of quantification were 0.001 mu g L-1 and 0.002 mu g L-1, respectively. The intra-day and inter-day relative standard deviations ranged from 2.4 % to 5.5 % and 1.5 % to 5.8 %, respectively. The green solvent-based HTDLLME-UHPLC-MS/MS method achieved rapid detection of SFs in different environmental samples, with extraction recoveries of 81.1 %-105.8 %. Greenness and blueness evaluation tools confirmed the environmental friendliness and practicality of the method. The green solvent-based HTDLLME approach provides a sustainable, straightforward, and practical solution for processing multiple samples simultaneously, supporting effective environmental monitoring.

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