Gold-core silver-shell nanoparticle as a SERS substrate for the detection of pesticides in tea

Abstract

In recent years, there has been significant interest in the application of innovative analytical methods combined with nanomaterials for the rapid detection of food contaminants. Pesticides are commonly used in tea cultivation to protect tea plants from various pests and diseases. However, pesticide residues can contaminate tea and have negative impacts on the environment and human health. Conventional chromatography-based analytical methods are destructive and require lengthy sample preparation. In this study, gold-silver core-shell nanoparticles (Au/Ag) coupled with surface-enhanced Raman spectroscopy (SERS) were developed as a novel, facile, and sensitive testing technique for detecting pesticide residues in tea samples. The Ag coating of Au NPs can continuously enhance Raman scattering signals. To assess the effectiveness of SERS method, a Raman reporter molecule, known as 4-aminothiophenol, was employed. This approach resulted in distinctive Raman spectra like fingerprints, and the SERS method demonstrated an impressive sensitivity capable of detecting concentrations as low as 0.1 mg/L. Two pesticides, phosmet and paraquat, were successfully detected individually or as a mixture in tea samples by SERS coupled with nanosubstrate. Strong Raman scattering signals for both phosmet and paraquat were obtained within a Raman shift range of 400-2000 cm^-1. The contamination of grapes with pesticide residues has generated significant concern. As consumers demand safer food products, it has become essential to conduct regular pesticide residue inspections in food. The objective of the second study was to synthesize polyhedral gold nanostars (AuNS) with multi-angled corners and utilize them in conjunction with SERS to detect pesticide residues in grape products. This study aimed to develop a fast and simple technique for detecting two pesticides (phosmet and paraquat) present on the surface of grapes using SERS. During the experiment, ethanol was applied to the contaminated grape surface extract pesticides. Subsequently, AuNS were introduced to generate SERS signals of the pesticides. The spiky tips of the AuNS act as SERS hot-spots, amplifying the Raman signals of the analyte molecules. Furthermore, the rough surface of AuNS increases the surface area, resulting in improved interactions between the substrate and analyte. Prominent SERS peaks of blended contaminants were observed under a 785 nm laser excitation, and these peaks were chosen to characterize and quantify the concentration of the contaminants. It was discovered that the SERS intensity of these two peaks changed in proportion to the concentration ratio of phosmet and paraquat. Additionally, AuNS demonstrated better SERS enhancement performance than gold nanoparticles. Our experimental results indicate that the lowest detectable concentration for both pesticides on grape surfaces is 0.5 ppm. These findings suggest that SERS coupled with AuNS is a practical and highly promising approach for the detection and quantification of trace contaminants in food.