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Atomically Traceable Nanostructure Fabrication
Published on: July 17, 2015
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Tailoring atomic 1T phase CrTe2forin situfabrication
Chaolun Wang1, Qiran Zou2, Zhiheng Cheng1
1In Situ Devices Center, Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, 500 Dongchuan Road, Shanghai 200241, People's Republic of China.
Nanotechnology
|November 17, 2021
Summary
Researchers controllably tailored 1T-phase chromium ditelluride (CrTe2) nanopores using in situ transmission electron microscopy (TEM). Machine learning efficiently analyzed TEM images, enabling precise nanostructure control for advanced nanodevices.
Area of Science:
- Materials Science
- Nanotechnology
- Condensed Matter Physics
Background:
- Controlling the phase-structure relationship of 1T-phase two-dimensional (2D) materials is crucial for nanodevice applications.
- In situ transmission electron microscopy (TEM) allows atomic-resolution monitoring and regulation of 2D material nanostructure evolution.
Purpose of the Study:
- To controllably tailor 1T-phase chromium ditelluride (CrTe2) nanopores using in situ TEM.
- To investigate the atomic-scale mechanisms of nanopore formation and healing in 1T-CrTe2.
- To apply machine learning for efficient analysis of TEM data.
Main Methods:
- Utilized in situ transmission electron microscopy (TEM) for atomic-scale observation and manipulation.
- Employed controllable tailoring of 1T-CrTe2 nanopores by regulating crystal face transformations ({1010} and {1120}).
- Applied deep-learning-based image segmentation for automated nanopore identification in TEM images.
Main Results:
- Achieved controllable tailoring of 1T-CrTe2 nanopores with preferred border structure formation.
- Observed and studied the nanopore healing process at the atomic scale.
- Demonstrated high-efficiency automated TEM image analysis with a DICE metric of 93.17% using deep learning.
Conclusions:
- Presented unique structure evolution insights for 1T-phase 2D materials.
- Established computer-aided, high-efficiency TEM data analysis using deep learning.
- Showcased techniques applicable to other materials for nanostructure regulation and analysis.

