New NSF Project 2311104

Electron microscopes take beautiful and informative pictures of metal particles of nanometer size, but the images can sometimes be difficult to interpret. The chemically-complex metallic-alloy nanoparticles (CCA-NPs) that motivate this work are of great interest in a wide range of applications including catalysis, energy conversion and storage, and bio/plasmonic imaging. This research project develops a multi-disciplinary modeling methodology supported by experimental measurements to keep pace with the growing widespread application of atomically resolved microscopic measurements. The main objective of this project is to utilize a novel modeling framework based on machine learning to extract information about atomic column heights and chemical elements from experimental high-resolution electron microscopy images of CCA-NPs of different compositions and sizes. Although the present work is motivated primarily by nanoparticles, the framework is general and easily extendable to other nanoscience research amenable to scanning transmission electron microscopy, such as catalysis, crystallography, and phase evolution.

The team will introduce in their undergraduate and graduate courses a number of topics related to the present project, stressing familiarity with current research problems and direct experience with different computational methods as well as open source and commercial software. The investigators and their group members actively participate in outreach activities for local high-school women and members of underrepresented groups through the University of Illinois Chicago (UIC) Open House and the UIC Youth Program. The computer simulation results with narratives will be used for classroom teaching and will also be made available to the public and scientific community via microblogging and social-network services.