To improve the long-term management of sites impacted with aqueous film-forming foams (AFFF), it is critical to understand the role of self-assembly of per- and polyfluoroalkyl substances (PFAS) in source zones and the factors that impact self-assembly on the long-term release and transport of PFAS to groundwater. In this context, self-assembly refers to the intra-molecular attraction between amphiphilic PFAS resulting in the potential formation of various supramolecular structures. The overall objective of this work is to evaluate relevant environmental factors such as PFAS concentration and type, hydrocarbon co-surfactants, ionic matrix, and soil chemical/physical properties that affect the formation and stability of supramolecular systems within AFFF source zones. This project aims to obtain a fundamental understanding of the self-assembly of PFAS mixtures in solution and at the air/water and water/soil interfaces in the presence of hydrocarbon co-surfactants, co-solvents, and inorganic ions during wetting and drying cycles. This project will advance the fundamental understanding of the self-assembly behavior of PFAS mixtures (C4 - C10, different head groups) found in soil, at the air/water interface, and the water/surface interface in the presence of inorganic ions, solvents, and co-surfactants. PFAS assembly during soil wetting/drying cycles will be studied, which has not been done to date. Molecular dynamics and machine learning will simulate the assembly and release and build a predictive model for conditions that mimic those found in soil and water.

This project aims to improve the understanding of PFAS self-assembly behavior in solution and on surfaces during soil wetting/drying cycles in the presence of multivalent ions, solvents, and co-surfactants to improve PFAS transport models. A congruent goal is to develop machine learning models of PFAS assembly that can predict how different environmental factors will impact PFAS self-assembly and stability, thus guiding experimental variables and subsequent site management strategies. The project is divided into four hypothesis-based objectives.

• Objective 1 will study the self-assembly in solution of PFAS mixtures (C4 -C10) in the presence of inorganic ions, solvents, and hydrocarbon surfactants, which aims to bridge the knowledge gap within PFAS assembly characterization in solution and at the air/water interface.
• Objective 2 will investigate self-assembly of PFAS mixtures on a variety of surfaces in the presence of salt, co-solvent and co-surfactants.
• Objective 3 will investigate PFAS self-assembly during soil wetting/drying cycles in lab-scale columns to evaluate factors impacting self-assembly and PFAS leaching under various PFAS, matrix, and soil conditions.
• Objective 4 will develop computational and machine learning models using molecular and Brownian Dynamics to study and predict self-assembly of PFAS.

The successful completion of this project will provide a robust dataset that will advance the understanding of PFAS transport through the soil profile. The molecular models resulting from this work will be a useful tool in assessing and prioritizing sites for remedial action. Overall, this information will aid in long-term site management, minimizing long-term impacts of source zones to groundwater, and improving risk assessment at impacted sites. The ability to optimize site management and better understand future risks may also lead to cost savings in both environmental monitoring and remediation. (Anticipated Project Completion - 2027)