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表面增强激光拉曼技术对白酒中克百威残留定性检测方法研究

谭文渊1,陈雨琴2,付大友1,王寿峰1,明红梅1   

  1. 1. 四川理工学院
    2. 四川理工学院分析测试中心
  • 收稿日期:2018-03-29 修回日期:2019-02-14 出版日期:2019-05-25 发布日期:2019-05-31
  • 通讯作者: 谭文渊 E-mail:twyhyx@126.com
  • 基金资助:
    基于白酒产业转型升级的关键技术研究与集成;基于分子印迹技术GC/MS测定白酒中MC和EC含量研究

Research on Qualitative Detection of Carbofuran Residues in Liquor by Surface Enhanced Laser Raman Technique

CHEN Yuqin 2, 2,   

  • Received:2018-03-29 Revised:2019-02-14 Online:2019-05-25 Published:2019-05-31

摘要: 摘 要: 建立了表面增强激光拉曼技术对白酒中的克百威残留进行定性检测方法。制备金溶胶(Au-NPS)和不同Ag壳厚度的银包金纳米颗粒(Au@Ag NPS),通过探测分子罗丹明B(R6G)比较了Au-NPS和不同Ag壳厚度Au@Ag NPS的SERS增强效果;碱性条件下,向克百威分子引入标记物2,6-二氯醌-4-氯亚胺,再与增强效果较好的NPS按比例混合进行拉曼测试,讨论了SERS信号采集条件,并对拉曼谱峰进行了比较和归属,计算了克百威在Au@Ag NPS增强基底上的增强因子。结果表明:克百威、标记物和Au@Ag NPS的比例为1:0.225:7,约7nmAg壳厚度 Au@Ag NPS效果最好,克百威的检测下限为1×10-17M,Au@Ag NPS增强因子为2×1010,用此方法检测三种市售白酒中均含有微量的克百威,方法快速、简便、结果可靠。

关键词: 表面增强拉曼散射, 银包金纳米粒子, 表征, 克百威, 定性检测

Abstract: Abstract: In this paper,a method of surface-enhanced Raman spectroscopy(SERS) for the detection of the residue of carbofuran in liquor was established.Firstly. Au NPS and Au@Ag NPS with different thicknesses of Ag shell were prepared ,and then the SERS performance of them were evaluated by a dye molecule rhodamine 6G (R6G); In alkaline solution, the 2, 6-Dichloroquinone-4-chloroimide as a marker was introduced to carbofuran, and then mixed with the NPS that has the better SERS performance in the proportion.we discussed the conditions for SERS signal acquisition,compared and belonged the Raman peaks,and estimated the enhancement factor of carbofuran on NPS enhanced substrate. The results shows that : Au@Ag NPS with 7nm Ag shell thickness possessed the best SERS effect,and it can be used as the SERS substrate ,The lowest concentration of Au@Ag NPS which was obtained to carbofuran reached 1×10-17M and enhancement factor reached 2×1010.All three kinds of liquor were detected by this method and the amount of the carbofuran was found.The method is fast, simple and reliable.

Key words: SERS, Au@Ag NPS, Characterization, Carbofuran, qualitative detection

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