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Water-contaminating chemicals such as polyfluoroalkyl substances (PFAS) may lead to severe environmental and health effects, such as low infant birth weight, cancer, and thyroid hormone disruption. Current approaches for detecting these chemicals are expensive, time-consuming, and require bulky equipment and skilled personnel. The vast number of contaminants — over 4,000 in the PFAS family alone — also prohibit conventional development of biological or chemical probes.

This initiative will develop a platform using molecular simulation, organic synthesis, and artificial intelligence to rapidly explore the large molecular space of potential PFAS probes and efficiently identify, design, and fabricate new chemical probes for sensing and removing contaminants from water. The work will also advance data science, characterization at the Argonne Advanced Photon Source, and high-performance simulation, and could potentially transfer to the screening and removal of other water contaminants, such as pharmaceuticals, to advance global public health.


Junhong Chen

Crown Family Professor, Molecular Engineering, UChicago & Lead Water Strategist, Argonne National Laboratory

Stuart Rowan

Barry L. MacLean Professor for Molecular Engineering Innovation and Enterprise, UChicago

Andrew Ferguson

Associate Professor of Molecular Engineering

Sang Soo Lee

Geochemist, Argonne National Laboratory

Chris Benmore

Physicist, Argonne National Laboratory

Seth Darling

Director, Senior Scientist, Argonne Center for Molecular Engineering

Rebecca Willett

Faculty Director of AI, Data Science Institute; Professor, Statistics, Computer Science, and the College

Eric Jonas

Assistant Professor, Computer Science