DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time.

In-situ irradiation transmission electron microscopy (TEM) offers unique insights into the millisecond-timescale post-cascade process, such as the lifetime and thermal stability of defect clusters, vital to the mechanistic understanding of irradiation damage in nuclear materials. Converting in-situ irradiation TEM video data into meaningful information on defect cluster dynamic properties (e.g.

Studies of Single-Nanoparticle-Level Nickel Thermal Oxidation: From Early Oxide Nucleation to Diffusion-Balanced Oxide Thickening.

High-temperature oxidation mechanisms of metallic nanoparticles have been extensively investigated; however, it is challenging to determine whether the kinetic modeling is applicable at the nanoscale and how the differences in nanoparticle size influence the oxidation mechanisms. In this work, we study thermal oxidation of pristine Ni nanoparticles ranging from 4 to 50 nm in 1 bar 1%O/N at 600 °C using gas-cell environmental transmission electron microscopy. Real-space oxidation videos revealed an unexpected nanoparticle surface refacetting before oxidation and a strong Ni nanoparticle size dependence, leading to distinct structural development during the oxidation and different final NiO morphology.

Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels.

Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has demonstrated success in recognizing feature patterns in images with well-defined contrast, automated identification of nanometer scale crystallographic defects in electron micrographs governed by complex contrast mechanisms is still a challenging task.