Recent advances and future outlooks towards engineered materials and interfaces for PFAS sensing

Per- and polyfluoroalkyl substances (PFAS) are widespread environmental contaminants associated with significant health risks, necessitating sensitive and reliable detection methods across varied matrices. Conventional techniques like liquid chromatography-tandem mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-MS) provide high sensitivity and specificity but are hampered by their complexity, cost, and limited field applicability. This review critically surveys recent advancements in PFAS sensing, focusing on engineered nanomaterial-based sensors and their integration into practical devices. We categorize sensing materials by structural characteristics, emphasizing nanomaterials functionalized with specific recognition elements to improve selectivity. The review also explores hybrid sensor platforms combining multiple detection modalities. Further, we discuss the role of artificial intelligence and machine learning techniques in enhancing sensor selectivity, data processing, and adaptability to complex environmental samples. Real-world validation studies are analyzed, highlighting sensor performance in matrices including drinking water, serum, and soil, emphasizing strategies to mitigate matrix interferences such as antifouling coatings and sample pretreatment. Challenges in sensor reproducibility, miniaturization, and regulatory compliance for field deployment are also examined. Overall, we provide a comprehensive outlook on the materials innovation, mechanistic understanding, and data-driven approaches that underpin the development of smart, portable PFAS sensors. These advances promise to bridge gaps between laboratory research and real-world applications, supporting regulatory monitoring and environmental protection efforts with scalable, cost-effective sensing solutions. • Dimension-based classification of engineered materials towards PFAS sensing • Comparison of performance metrics such as selectivity, recovery, interference effects and reusability • AI/ML-enhances PFAS detection approaches • Critical evaluation of sensor performance in real-world matrices