Metal–Organic Frameworks for PFAS Remediation and Sensing: From Molecular Design to Real-World Implementation

Metal–organic frameworks (MOFs) have emerged as promising materials for the remediation and sensing of per- and polyfluoroalkyl substances (PFAS), which are persistent environmental contaminants with serious health implications. This review comprehensively examines MOF-based strategies for PFAS removal and detection, focusing on fundamental aspects of MOF design, adsorption mechanisms, and sensor technologies. The unique properties of MOFs, including high porosity, tunable surface chemistry, and selective binding interactions, facilitate effective PFAS capture through electrostatic, hydrogen bonding, and fluorophilic interactions. Advances in MOF-polymer and MOF-carbon composites have improved structural stability, recyclability, and catalytic degradation, while MOF-based photocatalysts and electrocatalysts offer promising pathways for PFAS degradation. Emerging artificial intelligence (AI)-driven design approaches further accelerate the discovery of optimized MOFs for ultrasensitive PFAS detection via optical, electrochemical, and field-effect transistor (FET) sensors. Despite their potential, challenges related to aqueous stability, regeneration efficiency, and scalability remain. This review highlights current breakthroughs, identifies critical knowledge gaps, and outlines future research directions aimed at the practical implementation of MOF-based technologies for PFAS remediation and sensing, ultimately contributing to improved water quality and public health. Integrating insights from materials science, environmental engineering, and AI, this review offers a multidimensional perspective to guide future scalable and sustainable PFAS remediation solutions for widespread implementation.