ABSTRACT

With support from the Chemical Measurement and Imaging Program in the Division of Chemistry, Alexander Gundlach-Graham and his research group at Iowa State University are working to improve the detection and quantification of anthropogenic nanoparticles and microparticles in environmental samples. Nanoparticle pollution is a contemporary public health concern. For the foreseeable future, production and application of engineered nanoparticles and exposure to incidental nanoparticles produced through human activities are expected to increase. Thus, the development of analytical tools to accurately measure, characterize, and monitor these species is critical to understanding the extent?and predicting the impact?of nanoparticle pollution. The Gundlach-Graham group will advance the use of trace-element mass spectrometry as a tool to detect, quantify, and classify anthropogenic nanomaterials at extremely low concentrations. They will develop novel instrumental approaches and open-source software tools to improve the throughput and accuracy of particle measurements. As part of this project, the team will also generate and implement new educational materials to teach advanced data analysis strategies as part of analytical chemistry curricula.

Nanoparticles in environmental samples are difficult to detect because they are small (hundreds to millions of atoms), dilute in terms of total mass concentration, and are often present in complex, particle-rich matrices that contain naturally occurring particulates. The aim of this research is to develop a robust high-throughput measurement system for the quantification and classification of metal-containing nanoparticles and microparticles from diverse sample types. To meet this goal, the Gundlach-Graham group will develop novel calibration approaches and data-processing strategies for the analysis of nanoparticles by single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS). Specifically, the team will advance the use of multi-element fingerprints to separate natural NPs from anthropogenic NPs, investigate unsupervised machine learning approaches to identify novel particle classes, and create open-source software tools for the accurate and robust quantification of particle types by spICP-TOFMS. These developments are expected to improve the ability of scientists to track nanoparticulates and to understand the fate and transport of contaminating particles in the environment. In the educational component of this proposal, the team will develop computer-based learning modules to teach basic concepts of computer programming, Monte Carlo simulation, and data analysis strategies to analytical chemistry students; these resources will be freely available and published open access.

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.

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