Real-time detection and classification of PFAS using dynamic behaviors at liquid-liquid interfaces

Rapid detection and classification of per- and polyfluoroalkyl substances (PFAS) are important for monitoring their concentrations at potential contamination sites due to their severe impact on environmental and health safety. Herein, we present a combination of Janus droplets and microfluidics-based sensors to measure dynamic interfacial behaviors of PFAS at liquid-liquid interfaces. The time-series data are used as chemical fingerprints to classify the identity of PFAS based on their differences in chain length and head group and quantify their concentration. We demonstrate that classification of four different PFAS is possible using the time-series data of under ten minutes. We also extend this proof-of-concept work toward complex matrices of synthetic groundwater and binary mixtures of PFAS. Our results illustrate the potential of a real-time and continuous sensing platform for on-site environmental monitoring. Rapid detection and classification of per- and polyfluoroalkyl substances (PFAS) are important for monitoring their concentrations at potential contamination sites due to their severe impact on environmental and health safety.