研究目的
Investigating the strain fields induced by single-atom defects in 2D transition metal dichalcogenides with sub-picometer precision using deep learning and aberration-corrected scanning transmission electron microscopy.
研究成果
The study demonstrates the ability to measure strain fields induced by single-atom defects in 2D materials with sub-picometer precision using deep learning and aberration-corrected STEM. The methods reveal complex, oscillating strain fields around Se vacancies, indicating the potential of computer vision for high-precision electron microscopy in beam-sensitive materials.
研究不足
The precision of measurements is limited by signal-to-noise ratio and potential electron beam damage to the sample. The method requires large datasets of nominally identical defects for class averaging.
1：Experimental Design and Method Selection:
Utilized deep learning to mine large datasets of aberration-corrected scanning transmission electron microscopy images to locate and classify point defects. Combined hundreds of images of nominally identical defects to generate high signal-to-noise class averages.
2：Sample Selection and Data Sources:
Analyzed monolayer 2D transition metal dichalcogenide, WSe2-2xTe2x, synthesized using cooling-mediated, one-step chemical vapor deposition on SiO2/Si substrates.
3：List of Experimental Equipment and Materials:
Aberration-corrected scanning transmission electron microscopy (STEM) for imaging.
4：Experimental Procedures and Operational Workflow:
Acquired atomic-resolution ADF-STEM images as 10 sequential frames with short dwell times and then frame-averaged to minimize image distortions. Used deep learning models based on fully convolutional networks (FCNs) with ResUNet architecture to locate and classify point defects.
5：Data Analysis Methods:
Applied 2D Gaussian fitting to determine the positions of atomic columns and measure atomic spacings. Used bootstrap approach for statistical analysis of class-averaged images.
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