Fabrication of ultrasensitive hydrogel SERS substrate and its applications in trace analysis of methamphetamine

Methamphetamine (MAMP) poses a significant public health and safety risk in communities. Its use causes behavioral changes in a person that can hinder their ability to undertake tasks such as driving and work. Therefore, the development of a rapid and reliable detection method for MAMP is critical for both employers and law enforcement agencies. Surface-enhanced Raman scattering (SERS) can reliably distinguish MAMP by identifying its unique spectral fingerprints. Hydrogels can act as scaffolds for plasmonic nanomaterials to enable effective SERS detection of a wide range of analytes.

In this study, we investigated the fabrication of polyvinyl alcohol (PVA) hydrogels integrated with two-dimensional (2D) plate-like silver nanoplates (AgNPls) for ultrasensitive SERS analysis of MAMP. The resulting PVA hydrogel containing silver nanoprisms (AgNP-Hydrogel) was further modified through halide ion-induced etching and salting-out using the Hofmeister effect, significantly enhancing its SERS performance. The PVA matrix not only provides a scaffolding effect, preventing the aggregation of silver nanoparticles, but also enables the effective tuning of their morphologies. Its porous network structure facilitated efficient analyte diffusion and exchange. The salting-out process induces the AgNP-Hydrogel to shrink, generating "hot spots" that significantly enhance SERS sensitivity. By treating the AgNP-Hydrogel with KCl and subsequently salting it out using Na₂SO₄, an optimal SERS hydrogel (SERS-Hydrogel-I) was developed, capable of detecting Rhodamine 6G (R6G) at concentrations as low as 10^-12 M.

This promising SERS-Hydrogel-I was successfully applied to detection of MAMP, with a minimum detectable concentration of 0.01 ppm. The integration of PVA hydrogel and silver nanoplates, with the assistance of etching with halide ions and salting-out with sulfate ions, provides a convenient and effective platform for SERS-based detection, demonstrating strong potential for practical applications.