Machine learning peeks into nano-aquariums

3 years ago
Anonymous $UzyKJJH9oy

https://www.sciencedaily.com/releases/2020/08/200824105607.htm

The new study, led by Qian Chen, a professor of materials science and engineering at the University of Illinois, Urbana-Champaign, builds upon her past work with liquid-phase electron microscopy and is published in the journal ACS Central Science.

Being able to see -- and record -- the motions of nanoparticles is essential for understanding a variety of engineering challenges. Liquid-phase electron microscopy, which allows researchers to watch nanoparticles interact inside tiny aquariumlike sample containers, is useful for research in medicine, energy and environmental sustainability and in fabrication of metamaterials, to name a few. However, it is difficult to interpret the dataset, the researchers said. The video files produced are large, filled with temporal and spatial information, and are noisy due to background signals -- in other words, they require a lot of tedious image processing and analysis.