Exploiting Heterogeneity in Metal Nanoparticle Populations for Analytical Applications

University Of Alabama Tuscaloosa, Tuscaloosa AL

Investigators

Abstract

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, and co-funding from the Advanced Manufacturing Program in the Division of Civil, Mechanical, and Manufacturing Innovation, plus the Established Program to Stimulate Competitive Research, the research groups of Shane Street and Marco Bonizzoni at the University of Alabama, Tuscaloosa, are developing a new way to address a key analytical challenge. Namely, this collaborative team is focused on a new approach to the detection of “forever chemicals,” such as per- and polyfluoroalkyl substances (PFAS). Researchers will first identify conditions under which the targeted PFAS molecules demonstrably influence the growth of metal nanoparticles in water. Measured physical and chemical properties of these particles will then be fed to sophisticated machine learning methods to derive a unique signature associated with each contaminant. The technique is designed to improve environmental monitoring technologies, and, if successful, to potential contribute to fundamental understanding of how these nanoparticles behave. The work will provide interdisciplinary researach opportunities for students from groups underrepresented in STEM (science, technology, engineering and mathematics). Under this award, the U. Alabama researchers will focus on creating pattern-based chemosensors from metal nanoparticles to qualitatively detect specific anionic contaminants in water. The project will combine chemical synthesis, electron microscopy, and electrochemical measurements with machine-learning data analysis methods. It builds on existing expertise in synthesizing metal nanoparticles encapsulated in cationic hyperbranched polymers (Street) and simple yet powerful machine-learning algorithms to achieve chemical selectivity (Bonizzoni). Poly(ethylene)imine (PEI), a commercially available water-soluble cationic polyelectrolyte, supports the growth of encapsulated metal nanoparticles during chemical reduction of the polymer-coordinated metal ion precursors. The cationic polymer also attracts anionic contaminants to the immediate environment of the growing nanoparticles. Particle morphology is therefore expected to be influenced by the presence of target contaminants. The electrochemical signals associated with nanoparticle oxidation could then provide a unique signature for each PFAS contaminant. This information could be used to train a machine-learning classification algorithm to identify anionic contaminants in aqueous solution. 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.