AIMC Topic: Microscopy, Electron, Scanning Transmission
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Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy.
Scientific reports
Aug 5, 2022
Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM's high affinity with various analytical options into account. As is well...
Low-Dose Sparse-View HAADF-STEM-EDX Tomography of Nanocrystals Using Unsupervised Deep Learning.
ACS nano
Jun 21, 2022
High-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) can be acquired together with energy dispersive X-ray (EDX) spectroscopy to give complementary information on the nanoparticles being imaged. Recent deep learning ...
STEM Image Analysis Based on Deep Learning: Identification of Vacancy Defects and Polymorphs of MoS.
Nano letters
Jun 8, 2022
Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution structural analysis for a wide range of materials. The conventional analysis of STEM images is an extensive hands-on process, which limits efficient handl...
Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations.
ACS nano
Dec 14, 2017
Recent advances in scanning transmission electron and scanning probe microscopies have opened exciting opportunities in probing the materials structural parameters and various functional properties in real space with angstrom-level precision. This pr...
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