Advancing PFAS Remediation through Physics-Based Modeling of 2D Materials: Recent Progress, Challenges, and Opportunities

In recent years, the discovery and optimization of two-dimensional (2D) materials for environmental applications have garnered significant attention, particularly in the treatment of per- and polyfluoroalkyl substances (PFAS). PFAS, known for their strong carbon–fluorine bonds and persistence in the environment, present a critical challenge due to their resistance to degradation and harmful health effects. Traditional methods for PFAS remediation are often resource-intensive and inefficient. In this study, we propose leveraging physics-based machine learning (PBM) models to accelerate the discovery and optimization of 2D materials for PFAS treatment, particularly through adsorption and electrochemical degradation. The integration of fundamental physical laws with machine learning in an inverse PBM (IPBM) framework enables faster, more cost-effective predictions of material properties tailored to PFAS remediation. We highlight recent advancements in 2D materials, such as graphene, MXenes, and boron nitride, and their potential applications in environmental remediation. This approach promises to provide scalable, high-performance solutions to address the global PFAS contamination crisis, offering a path forward in developing advanced materials for sustainable water treatment technologies.