Collaborative Research: Elucidating nanofiltration removal performance and mechanisms for short-chain PFASs: An integrated experimental and computational approach

Per- and polyfluoroalkyl substances (PFAS) are a group of manmade chemicals that are used in many consumer products and industrial processes due to their unique chemical properties. However, their persistence in the environment poses a significant threat to the drinking water supply of roughly one in three people in the U.S. One promising method for PFAS removal from contaminated water is nanofiltration (NF), a technique that removes nanoscale particles from a liquid using membranes as a filter. Yet, several critical challenges must be addressed to make NF a viable part of PFAS cleanup efforts. First, the effectiveness of NF in removing the wide variety of PFAS types, especially (ultra)short-chain PFASs and those found in complex mixtures, remains unknown. Second, a better understanding of how various forms of PFAS interact with NF membranes at a molecular level is needed. Third, the lack of predictive models to identify key factors that affect PFAS passage through NF membranes hinders rational membrane design and selection. This research aims to address these knowledge gaps by combining experiments and computer simulations, integrated with specialized modeling techniques such as machine learning, to investigate how NF removes PFAS from contaminated water resources. The fundamental knowledge gained through this work will advance membrane-based technologies for remediating PFAS-contaminated water. In addition, this project will include public engagement and educational activities such as developing a new educational module, training students from underserved groups, and hosting outreach activities for PreK-12 students to increase PFAS scientific literacy and awareness. The overarching goal of this research is to use an innovative integration of experimental and computational studies to elucidate the performance and mechanisms of (ultra)short-chain PFAS removal by NF. To achieve this goal, the NF removal performance for (ultra)short-chain PFAS of varied structural features will be evaluated, and the structure-property-performance relationship of PFAS removal in NF treatment will be established using machine learning techniques. The investigators will use non-targeted chemical analyses to further assess the NF performance in removing diverse PFAS from complex aqueous film-forming foam-impacted water. The interactions and transport of PFAS at the water-membrane interface and within polyamide NF membranes will be probed theoretically using molecular dynamics simulations to gain mechanistic insights into the experimental results. The findings of this research will generate fundamental knowledge to inform rational design strategies for developing more effective NF membranes tailored to remediating PFAS-contaminated water. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.