A team of scientists have developed a method to illuminate the dynamic behavior of nanoparticles. This is a fundamental component in the creation of pharmaceuticals, electronics, industrial and energy fusion materials.
This advance, reported in Journal Science's “Visualization and Instability of Nanoparticle Surface Dynamics and Instability Enabled by Deep Removal,” combines artificial intelligence and electron microscopy to render a vision of how these small substances respond to stimuli.
“The nature of particle change is very diverse and manifests as rapid changes in the flaxional period, atomic structure, particle shape and orientation. To understand these dynamics, we need to understand new statistical tools,” the author of the paper. “This study will utilize topological data analysis to quantify flaxionism and introduce new statistics that track particle stability during transitions between ordered and disordered states.”
The study also includes researchers from New York University, Arizona State University and the University of Iowa, where electron microscopes can be blended with AI to allow scientists to see the structure and movement of molecules a hundredth of a meter of their size in an unprecedented time-resolution.
“Nanoparticle-based catalytic systems have a major impact on society,” says Carlos Fernandez-Granda, director of the NYU's Data Science Center and professor of mathematics and data science, one of the authors of the paper. “It is estimated that 90% of all manufactured products are involved in catalytic processes somewhere in the production chain. We have developed an artificial intelligence method that opens a new window to explore the atomic-level structural dynamics of materials.”
Observing the movement of atoms on nanoparticles is important for understanding their function in industrial applications. The problem is that the atoms are barely visible to the data, so scientists are unable to be sure how they behave. This corresponds to a tracking object for video shot with an older camera. To address this challenge, the paper authors trained deep neural networks, the computational engine of AI. This “brightens” the electron microscope image and reveals the underlying atoms and their dynamic behavior.
“Electronic microscopy allows images to be captured with high spatial resolution, but due to the rate at which the atomic structure of nanoparticles changes during chemical reactions, data must be collected at extremely high speeds to understand their function.”
“This creates a very loud measurement. We have developed an artificial intelligence method that will help us learn how to remove this noise.
This research was supported by a grant from the National Science Foundation.
