34 citations · 62 across the 8 of their papers we have counts for
9 papers
Substrate-Mediated Evaporation and Stochastic Evolution of Supported Au Nanoparticles
Dmitri N. Zakharov, Xiaohui Qu, Hong Wang +6
We use in situ transmission electron microscopy with automated tracking to study supported gold nanoparticles (NPs) during high-temperature vacuum annealing. \rev{The average mass…
Visualizing metal-mediated nucleation and growth of GaN
Abby Liu, Zhucong Xi, Xiaobo Chen +6
Understanding the atomic-scale mechanisms governing metal-mediated nucleation and growth of gallium nitride (GaN) and related alloys is critical for tailoring their structural and…
Machine Learning Pipeline for Denoising Low Signal-To-Noise Ratio and Out-of-Distribution Transmission Electron Microscopy Datasets
Brian Lee, Meng Li, Judith C Yang +2
High-resolution transmission electron microscopy (HRTEM) is crucial for observing material's structural and morphological evolution at Angstrom scales, but the electron beam can al…
The role of the solid-melt interface in accelerating the self-catalyzed growth kinetics of III-V semiconductors
Zhucong Xi, Abby Liu, Xiaobo Chen +5
Solid-melt interfaces play a pivotal role in governing crystal growth and metal-mediated epitaxy of gallium nitride (GaN) and other semiconductor materials. Using atomistic simulat…
One-Dimensional Twisted and Tubular Structures of Zinc Oxide by Semiconductor-Catalyzed Vapor-Liquid-Solid Synthesis
Thang Pham, Sampath Kommandur, Haeyeon Lee +3
The exploration of new catalysts for the vapor-liquid-solid (VLS) synthesis of one-dimensional (1-D) materials promises to yield new morphologies and functionality. Here, we show,…
Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation
James P. Horwath, Dmitri N. Zakharov, Remi Megret +1
Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since the…