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Abstract
Per- and polyfluoroalkyl substances (PFAS) are persistent, toxic environmental contaminants that pose significant threats to water systems and human health. Addressing these challenges requires technologies that can not only detect PFAS at ultratrace levels but also selectively remove them from complex aqueous environments. Both detection and adsorption rely on the same underlying molecular interactions between PFAS and surface-bound probes, making a mechanistic understanding of these interactions essential for advancing both goals. The objectives of this work are to develop a molecular-level framework for designing highly selective probes and sensing platforms for PFAS, and to translate these probes into practical sensing and adsorption strategies for effective monitoring and remediation of PFAS in water systems. Through a combination of computational modeling, experimental validation, and surface engineering, we aim to elucidate PFAS–probe interactions and advance the field toward real-world applications. Chapter 1 introduces PFAS, their occurrence in water, and the current state of PFAS detection and adsorption technologies, highlighting the critical importance of selectivity for sensors and sorbents. Chapter 2 presents a remote gate field-effect transistor (RGFET) sensor modified with β-cyclodextrin (β-CD)-functionalized reduced graphene oxide for parts per trillion (ppt)-level detection of perfluorooctane sulfonic acid (PFOS) in tap water. The sensor achieves a lower detection limit of ~250 parts per quadrillion (ppq), well below the U.S. EPA's maximum contaminant level (4 ppt). The system demonstrates excellent selectivity against major inorganic and organic interferents, and the binding mechanisms uncovered through molecular dynamics simulations and quartz crystal microbalance (QCM) experiments lay a strong foundation for future probe design. Chapter 3 builds upon this concept by incorporating α-, β-, and γ-CDs as differential capture probes in a sensor array to distinguish PFOS from challenging interferents such as sodium dodecyl sulfate (SDS) and trichloroacetic acid (TCAA). A combination of MD simulations, nuclear magnetic resonance (NMR) spectroscopy, and isothermal titration calorimetry (ITC) validates the distinct binding patterns between probes and analytes. QCM analysis reveals key factors—such as nonspecific interactions and probe density—that influence binding behavior at the sensing interface, leading to an optimized surface functionalization strategy that preserves solution-phase selectivity on sensor surfaces. Chapter 4 presents a rational probe design strategy guided by machine learning and artificial intelligence to systematically study PFAS–surface interactions using a library of small-molecule probes representing different binding mechanisms. This work explores probe selectivity through various surface functionalization strategies and experimental interaction characterization, identifying selective probes for both sensing and adsorptive removal of PFAS. Together, these studies offer a unified molecular framework for understanding and engineering PFAS–probe interactions. By integrating theoretical modeling with experimental validation, this work provides guiding principles for the development of advanced materials for both the selective detection and removal of PFAS from contaminated water sources. This research lays the foundation for practical sensing technologies and selective sorbents, with significant potential to mitigate PFAS contamination, supporting cleaner water and improved public health.