The critical micelle concentration (CMC) is a crucial physical-chemical property (PChP) of per- and polyfluoroalkyl substances (PFAS). It directly influences PFAS distribution across various environmental phases, making it essential for understanding PFAS fate, transport, and bioavailability. However, considering the number of legacy and emerging PFAS species found in aqueous film-forming foam (AFFF) formulations, only a small fraction have reported CMC values. Additionally, existing PFAS CMC data suffer from wide inconsistencies. This lack of reliable and comprehensive CMC data hinders the selection of optimal technologies for characterizing and remediating sites impacted by AFFF.

The overall objective of this proof-of-concept project is to establish a comprehensive database of PFAS CMCs utilizing both experimental and computational approaches. This database will encompass CMC values under a wide range of temperatures and complex environmental conditions for 40 PFAS included in EPA method 1633.

This collaborative project leverages the high sensitivity, versatility, and high-throughput nature of photophysical analysis to determine PFAS CMCs experimentally. The results will then guide computational simulations to enhance the accuracy of PFAS CMCs prediction. This project has four tasks:

Task 1. Develop and verify a photophysical methodology. The project team will determine and verify molecular dye(s) that can provide distinct and measurable optical responses upon PFAS micelle formation.

Task 2. Determine temperature dependent PFAS CMCs. Using the developed method, the project team will measure CMCs for 40 PFAS currently included in Method 1633 at various temperatures (5- 50 °C) in a high-throughput manner.

Task 3. Determine PFAS CMCs under environmentally relevant conditions. The project team will incorporate organic matter, ions, and other PFAS into the measurements to examine how these factors influence PFAS micelle formation.

Task 4. Construct a CMC prediction model. Starting with Quantum Mechanics (QM) and Force Fields trained from QM and comparing with experimental data, the project team will construct and refine a computational model that can predict CMCs prior to experiment for selecting optimum systems for experiments.

This project will deliver four direct key outcomes:

1. A standardized photophysical assay for PFAS CMCs determination (Task 1).
2. A clarification of inconsistencies in currently available experimental reports on PFAS CMCs (Task 1).
3. A comprehensive PFAS CMCs database encompassing a wider range of PFAS species and more informative CMC values for real-world PFAS management (Task 2 & 3).
4. An accurate and reliable PFAS CMCs prediction model that is well-validated by comparison with extensive experimental data (Task 4).

Moreover, both experimental and computational results will offer valuable insights into the thermodynamics and kinetics of PFAS micelle formation, addressing another critical knowledge gap in understanding PFAS aggregation at various interfaces. The computational model, built upon structure activity relationships, will be capable of predicting CMCs in complex systems containing emerging PFAS mixtures found in diverse AFFF formulations. The comprehensive PFAS CMC database will be instrumental in interpreting other relevant PChPs, including aqueous solubility, octanol-water partition coefficients (K_ow), sorbent-water distribution coefficients (K_d or K_sw), and Krafft points. The successful execution of this research will yield data essential for the development of advanced technologies aimed at enhancing ongoing remediation efforts at PFAS-impacted sites, thereby providing crucial safeguards for warfighters and installation communities. (Anticipated Project Completion - 2026)