Per- and polyfluoroalkyl substances (PFAS) pose significant environmental and health concerns, necessitating their efficient and accurate identification to facilitate their eventual mitigation from the environment. Surface-enhanced Raman spectroscopy (SERS) enables highly sensitive and precise molecular identification, but trace-level detection of chemicals and fluorescence interference remain significant challenges. Here, we present a uniform 3D AgNP@Si substrate for SERS, leveraging photon counting to achieve susceptible and low-fluorescence detection. This approach enables the detection of PFAS, including perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), at concentrations as low as 10⁻¹⁵ M, with Rhodamine 6G (R6G) used as a model analyte. Additionally, the quantitative analysis demonstrated a strong logarithmic relationship between Raman intensity and analyte concentration, with high correlation coefficients (R² = 0.98 for R6G and 0.97 for PFOA and PFOS). This pioneering approach offers a promising alternative to current analytical techniques for monitoring PFAS and other contaminants in the environment.

Silver/gold (Ag/Au) core–shell nanostructures exhibit tunable plasmonic properties and enhanced catalytic performance, enabling applications across sensing, biomedicine, and environmental remediation. This review presents representative synthetic strategies for fabricating Ag/Au bimetallic core–shell nanostructures with three distinct morphologies: nanospheres, nanocubes, and nanowires. For each architecture, we cover the representative synthetic approaches, such as seed-mediated growth, one-pot synthesis, and evaporation deposition methods, along with their corresponding applications. This review provides discussions on the synthesis methods and applications through specific examples, offering researchers guidance for fabricating Ag/Au core–shell nanostructures with tailored morphologies while addressing major challenges in controlling bimetallic formation.

Per- and polyfluoroalkyl substances (PFAS) present a significant environmental and health threat due to their persistence, widespread contamination, and toxicological effects. Despite stringent regulatory limits and growing monitoring efforts, conventional analytical techniques such as LC-MS/MS are limited by complexity, cost, and throughput. Recent developments in nanosensor technologies have demonstrated substantial potential to overcome these challenges, offering ultrasensitive, selective, rapid, and portable detection of PFAS. This review systematically examines state-of-the-art PFAS nanosensors categorized by their transduction principles—electrochemical, optical, and bio-based—highlighting representative strategies involving metal nanoparticles, metal–organic frameworks (MOFs), molecularly imprinted polymers (MIPs), fluorescent nanomaterials, plasmonic nanostructures, microresonators, and bio-recognition elements. We further explore the integration of machine learning as an enabling technology, showcasing its role in enhancing sensitivity, robustness, and selectivity through advanced data analytics and sensor optimization, exemplified by recent ultrasensitive surface-enhanced Raman scattering (SERS) studies. Finally, we discuss the current limitations, future directions, and opportunities for nanosensors to provide scalable and reliable solutions for real-time environmental monitoring and comprehensive PFAS risk management are discussed.

Per- and polyfluoroalkyl substances (PFASs) are persistent environmental contaminants that commonly occur as mixtures, yet their weak Raman scattering and overlapping vibrational features make direct optical quantification challenging. Here, we develop a plasmonic nanosensing platform that integrates three-dimensional Ag nanoparticle surface-enhanced Raman spectroscopy (SERS) substrates with single-photon Raman readout for quantitative analysis of binary perfluorooctanoic acid (PFOA)/perfluorooctane sulfonate (PFOS) mixtures. The multilayer silver nanoparticle (AgNP) architecture provides dense electromagnetic hot spots to amplify weak PFAS signatures, while photon-counting detection improves the measurement of low-intensity Raman features. Mixed spectra obtained from a 4 × 4 concentration matrix spanning ${10}^{-6}$ to ${10}^{-15}$ M exhibited substantial peak overlap, band broadening, and concentration-dependent spectral contour changes, highlighting the limitations of single-band calibration. Full-spectrum multivariate regression was therefore used to extract composition-dependent information from the coupled SERS response. Under nested cross-validation with replicates grouped by mixture composition, both partial least squares regression and linear support vector regression captured the main concentration-dependent spectral variation, achieving $R^2$ values of approximately 0.81–0.87 across the two analytes and identifying experimental concentration levels within one adjacent level after nearest-level assignment. These results demonstrate that coupling engineered plasmonic AgNP substrates with single-photon Raman detection enables mixture-resolved PFAS quantification from weak and overlapping SERS signatures, providing a nanomaterial-enabled framework for environmental molecular sensing.

Per- and polyfluoroalkyl substances (PFAS) pose significant environmental and health concerns, necessitating their efficient and accurate identification to facilitate their eventual mitigation from the environment. Surface-enhanced Raman spectroscopy (SERS) enables highly sensitive and precise molecular identification, but trace-level detection of chemicals and fluorescence interference remain significant challenges. Here, we present a uniform 3D AgNP@Si substrate for SERS, leveraging photon counting to achieve susceptible and low-fluorescence detection. This approach enables the detection of PFAS, including perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), at concentrations as low as 10^− 15 M, with Rhodamine 6G (R6G) used as a model analyte. Additionally, the quantitative analysis demonstrated a strong logarithmic relationship between Raman intensity and analyte concentration, with high correlation coefficients (R² = 0.98 for R6G and 0.97 for PFOA and PFOS). This pioneering approach offers a promising alternative to current analytical techniques for monitoring PFAS and other contaminants in the environment.