Machine Learning to Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos.

ABSTRACT: Liquid-phase transmission electron microscopy (TEM) has been recently applied to materials chemistry to gain fundamental understanding of various reaction and phase transition dynamics at nanometer resolution. However, quantitative extraction of physical and chemical parameters from the liquid-phase TEM videos remains bottlenecked by the lack of automated analysis methods compatible with the videos' high noisiness and spatial heterogeneity. Here, we integrate, for the first time, liquid-phase TEM imaging with our customized analysis framework based on a machine learning model called U-Net neural network. This combination is made possible by our workflow to generate simulated TEM images as the training data with well-defined ground truth. We apply this framework to three typical systems of

SUBMITTER: Yao L 

PROVIDER: S-EPMC7453571 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature