Issue 34, 2022

A plasmonic AgNP decorated heterostructure substrate for synergetic surface-enhanced Raman scattering identification and quantification of pesticide residues in real samples

Abstract

Rapid and on-site Raman spectroscopic identification and quantification of pesticide residues have been restricted to the low instrumental sensitivity of a portable Raman instrument, and no ideal platforms have been reported for analyzing pesticides on real sample surfaces. An efficient method to improve the detection sensitivity is to fabricate a highly sensitive surface-enhanced Raman scattering (SERS) substrate. Here, we present a MOF-derived ZnO@TiO2 heterostructure combined with plasmonic AgNPs as a SERS sensor to achieve synergetic EM and CM enhancement, exhibiting high sensitivity, excellent signal reproducibility (RSD < 5.9%) and superior stability for analysis of model molecules. The SERS sensor achieved a low detection concentration of 10−8 M for both CV and R6G molecular solutions on a portable Raman device. As a proof of concept, we modelled a pesticide residue on real samples of dendrobium leaves. Thiram, triazophos and fonofos solutions were selected as analytes for mimicking the function of on-site analysis. The SERS analytical platform showed not only high sensitivity for single- and multi-component identification, but also quantitative detection of pesticide residues on dendrobium leaves. These preliminary investigations indicate that this SERS analytical platform will allow the development and potential applications in rapid and on-site pesticide analysis.

Graphical abstract: A plasmonic AgNP decorated heterostructure substrate for synergetic surface-enhanced Raman scattering identification and quantification of pesticide residues in real samples

Supplementary files

Article information

Article type
Paper
Submitted
04 Jul 2022
Accepted
01 Aug 2022
First published
02 Aug 2022

Anal. Methods, 2022,14, 3250-3259

A plasmonic AgNP decorated heterostructure substrate for synergetic surface-enhanced Raman scattering identification and quantification of pesticide residues in real samples

X. Li, C. Xu, L. Yan, Y. Feng, H. Li, C. Ye, M. Zhang, C. Jiang, J. Li and Y. Wu, Anal. Methods, 2022, 14, 3250 DOI: 10.1039/D2AY01068B

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