Broad pH-resistant microdroplet SERS platform based on Ag@SiO₂@PVP NPs for real-time analysis of microbial...

Surface-enhanced Raman scattering (SERS) holds great promise for the real-time detection of microbial metabolites. However, pH fluctuations in fermentation environments can induce probe corrosion, aggregation, or precipitation, severely compromising the stability and reproducibility of SERS signals and thus diminishing system reliability for real-time analysis. To address this challenge, this study proposes a "material-system co-design" strategy to develop a wide pH-tolerant microdroplet SERS detection platform based on Ag@SiO₂@PVP nanoprobes. First, SERS nanoprobes with inherent pH tolerance were constructed through the synergistic effects of physical isolation provided by the SiO₂ shell and steric hindrance from the outer polyvinylpyrrolidone (PVP) coating. Subsequently, to achieve integrated operations including rapid mixing of nanoprobes and metabolites, homogeneous droplet generation, in-situ trapping, and SERS detection, a microfluidic chip incorporating a herringbone-shaped mixer, a droplet generation zone, an in-situ trapping zone, and a reflective substrate was designed. Using L-DOPA as a model molecule for performance validation, the results demonstrated a limit of detection as low as 9.5 × 10^-9 g/mL (based on the 3σ method). The SERS signals remained highly stable and reproducible across the pH 3-11 range, with a linear correlation coefficient R² > 0.99 and a relative standard deviation (RSD) of characteristic peak intensities below 5% (n = 20). In tests using real samples from E. coli fermentation broth, the method maintained clear spectral recognition capability across the concentration range of 10^-8 ~ 10^-4 g/mL, exhibiting significantly superior detection performance and stability compared to bare silver nanoparticles. Preliminary tests with L-tyrosine and arbutin further validated the platform's universality. Through its synergistic "dual-protection probe-multifunctional chip" strategy, this study provides a powerful tool for reliable, high-throughput analysis of metabolites in complex biological environments.