文献类型：期刊文献

英文题名：A novel metastable state nanoparticle-enhanced Raman spectroscopy coupled with thin layer chromatography for determination of multiple pesticides

作者：Kang, Yan[1,2];Wu, Ting[1,2];Chen, Wanchao[1,2];Li, Long[1,2];Du, Yiping[1,2]

机构：[1]East China Univ Sci & Technol, Shanghai Key Lab Funct Mat Chem, Sch Chem & Mol Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Res Ctr Anal & Test, Sch Chem & Mol Engn, Shanghai 200237, Peoples R China

年份：2019

卷号：270

起止页码：494

外文期刊名：FOOD CHEMISTRY

收录：;EI(收录号：20183005599789);WOS:【SCI-EXPANDED(收录号:WOS:000443253200065)】；

基金：This work was supported by Science and Technology Commission of Shanghai Municipality (No. 17142202600) and Fundamental Research Funds for the Central Universities (No. 222201714047). The authors are grateful to Dr. L. Li and Dr. L.J. Zhao for helpful discussions.

语种：英文

外文关键词：Metastable state nanoparticle-enhanced Raman spectroscopy; Pesticide; Polyurethane; Micelle; Thin layer chromatography

摘要：A novel and highly sensitive metastable state nanoparticle-enhanced Raman spectroscopy (MSNERS) was reported in this work, which employed an amphiphilic polymer polyurethane-Ag nanoparticle (AgNPs) as the MSNERS substrate. Polyurethane could form micelle to incorporate nanoparticles and analytes, where targets could be in close contact with the metal surface, which was effective for further enhancing the detection sensitivity. Time-dependent visible absorption spectra and time-dependent Raman spectra indicated that polyurethane could greatly enhance the stability of AgNPs aggregates during the volatilization process, which was a great improvement of MSNERS substrate. Combining this MSNERS substrate with the rapid separation method of thin layer chromatography (TLC), this TLC-MSNERS was successfully applied to analyze mixed pesticides on fruit skin and the detection limits of thiabendazole, triazophos and phosmet were 0.02 mu g/mL, 0.8 mu g/mL, and 0.6 mu g/mL, respectively. It enhanced 1 order of magnitude the signals of analytes in comparison to that of traditional TLC-SERS method.