Abstract
Pesticides pose a great threat to human health and their rapid detection has become an urgent public safety issue engaging the scientific community to search for fast and reliable detection techniques. In this context, Surface Enhanced Raman Spectroscopy (SERS) has emerged as a valuable detection and analysis tool due to its high sensitivity and selectivity, proving its suitability for the food industry and environmental monitoring applications. Here, we report on the fabrication of colloidal silver nanoparticle (AgNP) films by convective self-assembly (CSA) on solid planar substrate and their use for the SERS analyses of two types of pesticides, the fungicide thiabendazole (TBZ) and the insecticide α-endosulfan (α-ES). Electron microscopy shows that these nanoparticle films are dense, highly compact, and uniform across several mm2 areas. The SERS efficiency of the fabricated AgNP films is evaluated using a well-known Raman probe, p-aminothiophenol, for multiple excitation laser lines (532 nm, 633 nm, and 785 nm). The films exhibit the largest SERS enhancement factors for 785 nm excitation, reaching values larger than 105. Thiabendazole could be readily adsorbed on the AgNPs without any sample surface functionalization and detected down to 10-6 M, reaching the sub-ppm range. Endosulfan, a challenging analyte with poor affinity to metal surfaces, was captured near the metal surface by using self-assembled alkane thiol monolayers (hexanethiol and octanethiol), as demonstrated by the thorough vibrational band analysis, and supported by density functional theory (DFT) calculations. In addition, principal component analysis (PCA) based on SERS spectra offers significant leverage in discrimination of the molecules anchored onto the metallic nanostructured surface. This present study demonstrates the utility of self-assembled colloidal nanoparticle films as SERS substrates for a broad range of analytes (para-aminothiophenol, thiabendazole, α-endosulfan, and alkanethiols) and contributes to the development of SERS-based sensors for pesticides detection, identification and monitoring.