Researchers at the University of Chicago Pritzker School of Molecular Engineering and Argonne National Laboratory have developed a new method to quickly detect very low levels of per- and polyfluoroalkyl substances (PFAS), commonly known as "forever chemicals," in water. These chemicals are known for their persistence in the environment and have been linked to health risks such as cancers, thyroid issues, and weakened immune systems.
The team aims to make this technology available through a portable handheld device that uses specialized probes to measure PFAS levels, including toxic compounds like perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS). The U.S. Environmental Protection Agency has recently proposed stricter limits for these substances.
“Existing methods to measure levels of these contaminants can take weeks, and require state-of-the-art equipment and expertise,” said Junhong Chen, Crown Family Professor at the UChicago Pritzker School of Molecular Engineering and lead water strategist at Argonne. “Our new sensor device can measure these contaminants in just minutes.”
The technology is detailed in the journal Nature Water. It is sensitive enough to detect PFAS concentrations as low as 250 parts per quadrillion, which is comparable to finding one grain of sand in an Olympic-sized swimming pool.
Andrew Ferguson, professor of molecular engineering at UChicago PME, noted the broader significance: “PFAS detection and elimination is a pressing environmental and public health challenge. Computer simulations and machine learning have proven to be an incredibly powerful tool to understand how these molecules bind to molecular sensors and can guide experimental efforts to engineer more sensitive and selective molecular probes.”
Seth Darling, a senior scientist at both Argonne and UChicago, explained how their approach works: “Even though they are typically present at miniscule concentrations, PFAS do have certain molecular characteristics that differentiate them from other things dissolved in water—and our probes are designed to recognize those features.”
PFAS are widely used for their oil- and water-resistant properties in consumer products such as non-stick cookware, fast food packaging, firefighting foam, raincoats, and stain-resistant carpets. Their persistence means they accumulate over time in people’s bodies and the environment.
Current standard tests for PFAS rely on liquid chromatography/tandem mass spectrometry—a laboratory-based process that separates chemical compounds but is costly and time-consuming. Detecting PFAS is further complicated by their low concentrations relative to other contaminants and by the diversity among thousands of different PFAS compounds.
Chen’s group has worked on portable sensors for over 15 years. Their proposal to adapt their chip-based sensor technology for PFAS was included in the National Science Foundation Water Innovation Engine in the Great Lakes region.
To make each sensor specific for individual types of “forever chemicals,” researchers used machine learning algorithms to select chemical probes likely to bind only with target PFAS molecules. In 2021, they received a Discovery Challenge Award from the UChicago Center for Data and Computing supporting this artificial intelligence-driven approach.
“In this context, machine learning is a tool that can quickly sort through countless chemical probes and predict which ones are the top candidates for binding to each PFAS,” said Chen.
Tests showed that one computationally-selected probe could specifically bind with PFOS even when other common tap water chemicals were present at higher concentrations. The device measures changes in electrical conductivity when a target molecule binds with its probe; this change indicates how much PFOS is present.
To verify accuracy, researchers compared results from their sensor with data obtained using EPA-approved methods. Results matched closely, confirming reliability across multiple detection cycles—an important step toward real-time monitoring.
“Our next step is to predict and synthesize new probes for other, different PFAS chemicals and show how this can be scaled up,” said Chen. “From there, there are many possibilities about what else we can sense with this same approach—everything from chemicals in drinking water to antibiotics and viruses in wastewater.”
The study appears under the title “Reversible ppt-Level Detection of Perfluorooctane Sulfonic Acid in Tap Water using Field-Effect Transistor Sensors” by Wang et al., published September 25, 2025 in Nature Water (DOI: 10.1038/s44221-025-00505-9).
This article first appeared on the UChicago PME website.