Manufacturers use chemicals known as per- and polyfluoroalkyl substances (PFAS) for a wide range of consumer and industrial products, such as stain-resistant carpeting, non-stick cookware, and takeout packaging. These man-made chemicals have strong resistance to grease, oil, water, and heat. Since PFAS do not breakdown easily, they build up in the environment – soil, water, and the air – and in our bodies. Exposure to these chemicals can lead to adverse health effects including low infant birth weight, cancer, and thyroid hormone disruption.
Currently, detection of PFAS requires the use of bulky and expensive equipment, skilled personnel, and tedious sample preparation and data collection. A team of researchers including Profs. Junhong Chen, Andrew Ferguson, Eric Jonas, Stuart Rowan, and Rebecca Willett at the University of Chicago and Drs. Chris Benmore, Seth Darling, and Sang Soo Lee from Argonne National Laboratory aim to make detection and removal of these chemicals from one of the biggest sources of contamination – water – fast, accurate, and more affordable.
The team will use artificial intelligence (AI) to sift through thousands of molecules to identify target PFAS molecules and use these computational screens to guide the design and synthesis of new molecules that can remove them. The newly developed molecules will be tested using infrared and nuclear magnetic resonance spectroscopy and x-ray scattering. This combination of AI-guided molecular modeling, organic synthesis, and advanced characterization techniques will ultimately lead to new devices for real-time sensing of PFAS and advanced sorbents to remove them.