Geng Hongshuai, Xu Guiju, Liu Lu, Wang Xiaoli, Zhao Rusong
Qilu University of Technology (Shandong Academy of Sciences), Shandong Analysis and Test Center, Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Jinan 250014, China.
Qilu University of Technology (Shandong Academy of Sciences), Shandong Analysis and Test Center, Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Jinan 250014, China.
J Chromatogr A. 2022 Oct 25;1682:463516. doi: 10.1016/j.chroma.2022.463516. Epub 2022 Sep 18.
The determination of traces levels of pesticide residue in water is crucial for monitoring water quality. In this study, covalent organic frameworks (COFs), namely TAPA-TFPB-COFs were prepared at room temperature (25 °C) and applied as adsorbents for the solid phase extraction (SPE) of phenoxy carboxylic acid herbicides (PCAs). The extraction was followed by analyzation using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Under the optimal conditions, ultrasensitive and specific analysis of PCAs in water samples was achieved. The method exhibited high sensitivity with low limits of detection (0.08-0.28 ng L), good linearity in the range of 1.00 to 200 ng L and satisfactory repeatability (intra-day: 3.72-5.30%; inter-day: 2.02-4.04%). The method was successfully applied to the analyzation of trace PCAs in tap, well, and river water and the spiked recoveries were in the range of 81.1-112%. These results indicate that the SPE-LC-MS/MS technique with TAPA-TFPB-COFs as the SPE adsorbent is a promising technique for the detection of trace levels of PCAs in environmental water samples.
水中痕量农药残留的测定对于监测水质至关重要。在本研究中,在室温(25°C)下制备了共价有机框架(COF),即TAPA-TFPB-COF,并将其用作苯氧基羧酸类除草剂(PCA)固相萃取(SPE)的吸附剂。萃取后采用液相色谱-串联质谱(LC-MS/MS)进行分析。在最佳条件下,实现了对水样中PCA的超灵敏和特异性分析。该方法具有高灵敏度,检测限低(0.08-0.28 ng/L),在1.00至200 ng/L范围内具有良好的线性,重复性令人满意(日内:3.72-5.30%;日间:2.02-4.04%)。该方法成功应用于自来水、井水和河水中痕量PCA的分析,加标回收率在81.1-112%范围内。这些结果表明,以TAPA-TFPB-COF作为SPE吸附剂的SPE-LC-MS/MS技术是检测环境水样中痕量PCA的一种有前景的技术。