Issue 17, 2020

Tunable plasmonics of hollow raspberry-like nanogold for the robust Raman scattering detection of antibiotics on a portable Raman spectrometer

Abstract

It is important to develop novel sensors for the simple, rapid, and quantitative detection of residual antibiotics in food, considering their potential threats to human health. Herein, we report the successful fabrication of raspberry-like nanogold (R-like Au) structures with rough surfaces and partially hollow structures for the rapid and sensitive detection of antibiotics in duck meats on a portable Raman spectrometer. The R-like Au with tunable plasmonic wavelengths was fabricated by a two-step method. Ag particles as seeds were substituted by Au particles through a replacement reaction; moreover, the surface plasmon resonances (SPRs) of R-like Au red-shifted with the increase in the Au ion concentration. The R-like Au with a diameter of about 120 nm that matched well with the 785 nm laser on the portable Raman spectrometer was used as an excellent surface-enhanced Raman spectroscopy (SERS) active platform for detecting two kinds of antibiotics, namely, nitrofurantoin (NFT) and nitrofurazone (NFZ), in spiked duck meats. Both of them have sufficient sensitivity (0.05ā€“10 mg Lāˆ’1), good linear relationship (R2 > 0.99) and high recovery in quantitative SERS analysis. The platform is simple without the need for complex pretreatment of the food samples or use of large scale Raman spectrometers, promising easy implementation for the on-site analysis of residuals of antibiotics in food.

Graphical abstract: Tunable plasmonics of hollow raspberry-like nanogold for the robust Raman scattering detection of antibiotics on a portable Raman spectrometer

Supplementary files

Article information

Article type
Paper
Submitted
25 May 2020
Accepted
23 Jun 2020
First published
23 Jun 2020

Analyst, 2020,145, 5854-5860

Tunable plasmonics of hollow raspberry-like nanogold for the robust Raman scattering detection of antibiotics on a portable Raman spectrometer

Y. Wu, Y. Liu, X. Tang, Z. Cheng and H. Liu, Analyst, 2020, 145, 5854 DOI: 10.1039/D0AN01049A

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