Deep learning detection of nanoparticles and multiple object tracking of their dynamic evolution during in situ ETEM studies.

ABSTRACT: In situ transmission electron microscopy (TEM) studies of dynamic events produce large quantities of data especially under the form of images. In the important case of heterogeneous catalysis, environmental TEM (ETEM) under gas and temperature allows to follow a large population of supported nanoparticles (NPs) evolving under reactive conditions. Interpreting properly large image sequences gives precious information on the catalytic properties of the active phase by identifying causes for its deactivation. To perform a quantitative, objective and robust treatment, we propose an automatic procedure to track nanoparticles observed in Scanning ETEM (STEM in ETEM). Our approach involves deep learning and computer vision developments in multiple object tracking. At first, a registration step co

SUBMITTER: Faraz K 

PROVIDER: S-EPMC8847623 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature