New Study Targets Key PFAS Property to Boost Environmental Cleanup Strategies

A new Department of Defense funded research project led by Lei Guo, assistant professor of civil engineering at the University of Arkansas, is tackling one of the most elusive aspects of per- and polyfluoroalkyl substances, or PFAS: their critical micelle concentration (CMC). By developing a high-throughput experimental assay and a machine-learning-powered prediction model, Guo's team aims to close critical data gaps and improve remediation efforts at contaminated military installations.

PFAS, often referred to as "forever chemicals" due to their persistence in the environment, have been widely used in firefighting foams, non-stick cookware and water-repellent fabrics. Their resistance to natural degradation and tendency to accumulate in soil and the food chain have raised global environmental and health concerns.

"Understanding PFAS CMC is essential to predicting their environmental behavior, how they spread and the best ways to clean them up," Guo said. "Our goal is to provide faster, more accurate ways to measure and predict CMC, giving policymakers and scientists tools they need to address PFAS contamination more effectively."

Despite the growing urgency around PFAS cleanup, reliable CMC data, key to understanding how PFAS interact with water, soil and living organisms, remains limited and inconsistent. CMC is a key physiochemical property governing PFAS transport, adsorption and bioaccumulation, all of which influence the success of remediation technologies such as foam fractionation and sorptive removal.

To address this, the team is deploying a photophysical assay designed for high-throughput analysis, allowing researchers to screen PFAS compounds rapidly and with high sensitivity. The approach offers a significant technical advantage over traditional methods that are often time-consuming and sample-extensive.

Complementing the lab-based research is a computational model that combines molecular dynamics and machine learning to predict CMC values based on PFAS molecular structure. While the model is still in early stages, the team believes it can eventually predict CMCs for PFAS compounds that are beyond legacy PFAS species, unknown, or even in mixtures—offering a powerful tool for preemptive environmental risk assessment.

 "Our work provides a faster, more accurate way to measure and predict CMC, filling a critical data gap that has hindered effective PFAS regulation and remediation," Guo said. "By combining high-throughput lab methods with computational modeling, we aim to arm decision makers with the tools needed to prioritize high-risk PFAS, optimize cleanup strategies and evaluate safer alternatives before they enter the environment."

The project, recently funded and conducted in collaboration with researchers at the California Institute of Technology, is expected to deliver a proof-of-concept by 2027. Long-term goals of the project include contributing to a more complete and accessible understanding of PFAS behavior in the environment.

"For me, this is personal," Guo added. "As a scientist and engineer, I'm committed to addressing the pressing PFAS environmental challenge, which impacts daily life for communities worldwide. My goal is to ensure safe drinking water for the public."

For environmental scientists, regulators and the public alike, the message is clear: by improving how we measure and predict PFAS behavior, researchers like Guo are helping bring the scientific community one step closer to effective, evidence-based solutions.