Collaborative Research: Environmental Sensing of Per and Polyfluoroalkyl Substances in Water Utilizing a Microelectrode Sensor Array Platform and Machine Learning Enabled Detection

Per- and polyfluoroalkyl substances (PFAS) are a group of “forever chemicals” that are used in numerous consumer and industrial products including non-stick cookware, paints, clothes, cleaning products, food packaging, and firefighting foams. These products are either released to the environment or disposed of in landfills and, therefore, have the potential to contaminate natural waters and drinking water sources.

Medical studies suggest that exposure to very low levels of PFAS could result in long-term developmental disabilities in infants, increased infertility, and risk of cancer. As such, PFAS contamination is an important environmental problem, and low-cost methods for rapid and reliable monitoring are necessary. Current PFAS detection methods require expensive equipment and specialized training to maintain the complicated instrumentation.

The overall objective of this project is to create a low-cost PFAS sensing method to monitor PFAS contamination in water. This objective will be accomplished by developing a microelectrode sensor array coupled with machine learning algorithms to detect a mixture of PFAS in diverse water sources. The successful completion of this project will benefit society through the development of a low-cost method to monitor PFAS.

Additional benefits to society will be achieved through student education and outreach including the mentoring of two graduate students at the University of Illinois at Chicago and an undergraduate student at Purdue University.

Low-cost methods for rapid and reliable monitoring of per- and polyfluoroalkyl substances (PFAS) are greatly needed. Current PFAS analysis relies on chromatographic methods coupled to expensive and bulky mass spectrometric detectors. While these methods are useful for accurate low-level quantification of PFAS, they are not mobile, and they require specialized training to maintain the complicated instrumentation.

The overarching objective of this project is to create a bottom-up framework for the development of mobile, low-cost PFAS sensing platforms that can be used in-situ and at the point-of-use to monitor PFAS contamination in water. The proposed framework will be demonstrated through the development of a functionalized microelectrode sensor array (MESA) platform, coupled with machine learning algorithms, for the detection and quantification of a mixture of PFAS with a range of physical properties in diverse water matrices.

The specific research objectives of this project are to: (1) characterize the fundamental adsorption/desorption mechanisms of PFAS on sorbent materials using an electrochemical quartz crystal microbalance experimental setup; (2) utilize computational density functional theory calculations to reveal the specific surface interactions that control PFAS adsorption/desorption on different sorbent materials; (3) integrate the experimental-computational results to guide the selection of selective, reversible adsorbents for various PFAS; and (4) fabricate and test a machine-learning enabled MESA platform for PFAS detection. The successful completion of this project has potential for transformative impact through the development of a sensor for the selective detection of individual compounds within a PFAS mixture with detection limits in the low ng/L concentration range and reliable performance in varying source water matrices.

Further benefits to society will be accomplished through an annual summer research experience for undergraduates and by creating a four-week workshop to introduce machine-learning concepts to high school students.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.