Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

In Situ Scanning Transmission Electron Microscopy Study of MoS₂ Formation on Graphene with a Deep-Learning Framework.

ABSTRACT: Atomic-scale information is essential for understanding and designing unique structures and properties of two-dimensional (2D) materials. Recent developments in in situ transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) enable research to provide abundant insights into the growth of nanomaterials. In this study, 2D MoS₂ is synthesized on a suspended graphene substrate inside a TEM column through thermolysis of the ammonium tetrathiomolybdate (NH₄)₂MoS₄ precursor at 500 °C. To avoid misinterpretation of the in situ STEM images, a deep-learning framework, DeepSTEM, is developed. The DeepSTEM framework successfully reconstructs an object function in atomic-resolution STEM imaging for accurate determination of the atomic structure and dynamic analysis. In situ STEM imaging with DeepSTEM enables observation of the edge configuration, formation, and reknitting progress of MoS₂ clusters with the formation of a mirror twin boundary. The synthesized MoS₂/graphene heterostructure shows various twist angles, as revealed by atomic-resolution TEM. This deep-learning framework-assisted in situ STEM imaging provides atomic information for in-depth studies on the growth and structure of 2D materials and shows the potential use of deep-learning techniques in 2D material research.

SUBMITTER: Lee Y

PROVIDER: S-EPMC8388093 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Partial Scanning Transmission Electron Microscopy with Deep Learning.

Project description:Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a two-stage multiscale generative adversarial neural network to complete realistic 512?×?512 scanning transmission electron micrographs from spiral, jittered gridlike, and other partial scans. For spiral scans and mean squared error based pre-training, this enables electron beam coverage to be decreased by 17.9× with a 3.8% test set root mean squared intensity error, and by 87.0× with a 6.2% error. Our generator networks are trained on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of loss spikes and an auxiliary trainer network. Our source code, new dataset, and pre-trained models are publicly available.

| S-EPMC7239858 | biostudies-literature

In Situ Transmission Electron Microscopy Modulation of Transport in Graphene Nanoribbons.

Project description:In situ transmission electron microscopy (TEM) electronic transport measurements in nanoscale systems have been previously confined to two-electrode configurations. Here, we use the focused electron beam of a TEM to fabricate a three-electrode geometry from a continuous 2D material where the third electrode operates as side gate in a field-effect transistor configuration. Specifically, we demonstrate TEM nanosculpting of freestanding graphene sheets into graphene nanoribbons (GNRs) with proximal graphene side gates, together with in situ TEM transport measurements of the resulting GNRs, whose conductance is modulated by the side-gate potential. The TEM electron beam displaces carbon atoms from the graphene sheet, and its position is controlled with nanometer precision, allowing the fabrication of GNRs of desired width immediately prior to each transport measurement. We also model the corresponding electric field profile in this three-terminal geometry. The implementation of an in situ TEM three-terminal platform shown here further extends the use of a TEM for device characterization. This approach can be easily generalized for the investigation of other nanoscale systems (2D materials, nanowires, and single molecules) requiring the correlation of transport and atomic structure.

| S-EPMC4850503 | biostudies-literature

Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy study.

Project description:The presence and configurations of defects are primary components determining materials functionality. Their population and distribution are often nonergodic and dependent on synthesis history, and therefore rarely amenable to direct theoretical prediction. Here, dynamic electron beam-induced transformations in Si deposited on a graphene monolayer are used to create libraries of possible Si and carbon vacancy defects. Deep learning networks are developed for automated image analysis and recognition of the defects, creating a library of (meta) stable defect configurations. Density functional theory is used to estimate atomically resolved scanning tunneling microscopy (STM) signatures of the classified defects from the created library, allowing identification of several defect types across imaging platforms. This approach allows automatic creation of defect libraries in solids, exploring the metastable configurations always present in real materials, and correlative studies with other atomically resolved techniques, providing comprehensive insight into defect functionalities.

| S-EPMC6764837 | biostudies-literature

Elasticity of MoS<sub>2</sub> Sheets by Mechanical Deformation Observed by in Situ Electron Microscopy.

Project description:MoS2 has been the focus of extensive research due to its potential applications. More recently, the mechanical properties of MoS2 layers have raised interest due to applications in flexible electronics. In this article, we show in situ transmission electron microcsopy (TEM) observation of the mechanical response of a few layers of MoS2 to an external load. We used a scanning tunneling microscope (STM) tip mounted on a TEM stage to induce deformation on nanosheets of MoS2 containing few layers. The results confirm the outstanding mechanical properties on the MoS2. The layers can be bent close to 180°. However, when the tip is retrieved the initial structure is recovered. Evidence indicates that there is a significant bond reconstruction during the bending with an outstanding capability to recover the initial bond structure. The results show that flexibility of three layers of MoS2 remains the same as a single layer while increasing the bending modulus by 3 orders of magnitude. Our findings are consistent with theoretical calculations and confirm the great potential of MoS2 for applications.

| S-EPMC4291041 | biostudies-literature

In situ oxidation and reduction of cerium dioxide nanoparticles studied by scanning transmission electron microscopy.

Project description:Cerium dioxide nanocubes and truncated octahedra were reduced and oxidized in the scanning transmission electron microscope. The reduction process was stimulated by the electron beam and oxidation was supported by background gases in the microscope environment. High-angle annular dark field imaging is sensitive to local lattice distortions that arise as oxygen vacancies are created and cerium cations reduce enabling high spatial resolution characterization of this process with temporal resolution on the order of seconds. Such measurements enable us to differentiate and infer that the observed behavior between the nanocubes and truncated octahedra may be due to the difference in crystallographic termination of surfaces. In situ measurements taken with different partial pressures of oxygen reveal the cerium oxidation state and the dose rate threshold for the onset of beam reduction are influenced by the environment. Increasing oxygen partial pressure reduces the Ce3+ content and decreases susceptibility to electron beam driven reduction.

| S-EPMC6392188 | biostudies-literature

Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy.

Project description: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 known, STEM's image acquisition time needs to be further shortened to capture a targeted phenomenon in real-time as STEM's current temporal resolution is far below the conventional TEM's. However, rapid image acquisition in the millisecond per frame or faster generally causes image distortion, poor electron signals, and unidirectional blurring, which are obstacles for realizing video-rate STEM observation. Here we show an image correction framework integrating deep learning (DL)-based denoising and image distortion correction schemes optimized for STEM rapid image acquisition. By comparing a series of distortion corrected rapid scan images with corresponding regular scan speed images, the trained DL network is shown to remove not only the statistical noise but also the unidirectional blurring. This result demonstrates that rapid as well as high-quality image acquisition by STEM without hardware modification can be established by the DL. The DL-based noise filter could be applied to in-situ observation, such as dislocation activities under external stimuli, with high spatio-temporal resolution.

| S-EPMC9356044 | biostudies-literature

Three-dimensional scanning transmission electron microscopy of biological specimens.

Project description:A three-dimensional (3D) reconstruction of the cytoskeleton and a clathrin-coated pit in mammalian cells has been achieved from a focal-series of images recorded in an aberration-corrected scanning transmission electron microscope (STEM). The specimen was a metallic replica of the biological structure comprising Pt nanoparticles 2-3 nm in diameter, with a high stability under electron beam radiation. The 3D dataset was processed by an automated deconvolution procedure. The lateral resolution was 1.1 nm, set by pixel size. Particles differing by only 10 nm in vertical position were identified as separate objects with greater than 20% dip in contrast between them. We refer to this value as the axial resolution of the deconvolution or reconstruction, the ability to recognize two objects, which were unresolved in the original dataset. The resolution of the reconstruction is comparable to that achieved by tilt-series transmission electron microscopy. However, the focal-series method does not require mechanical tilting and is therefore much faster. 3D STEM images were also recorded of the Golgi ribbon in conventional thin sections containing 3T3 cells with a comparable axial resolution in the deconvolved dataset.

| S-EPMC2917646 | biostudies-literature

Big Data Analytics for Scanning Transmission Electron Microscopy Ptychography.

Project description:Electron microscopy is undergoing a transition; from the model of producing only a few micrographs, through the current state where many images and spectra can be digitally recorded, to a new mode where very large volumes of data (movies, ptychographic and multi-dimensional series) can be rapidly obtained. Here, we discuss the application of so-called "big-data" methods to high dimensional microscopy data, using unsupervised multivariate statistical techniques, in order to explore salient image features in a specific example of BiFeO3 domains. Remarkably, k-means clustering reveals domain differentiation despite the fact that the algorithm is purely statistical in nature and does not require any prior information regarding the material, any coexisting phases, or any differentiating structures. While this is a somewhat trivial case, this example signifies the extraction of useful physical and structural information without any prior bias regarding the sample or the instrumental modality. Further interpretation of these types of results may still require human intervention. However, the open nature of this algorithm and its wide availability, enable broad collaborations and exploratory work necessary to enable efficient data analysis in electron microscopy.

| S-EPMC4876439 | biostudies-literature

Materials characterisation by angle-resolved scanning transmission electron microscopy.

Project description:Solid-state properties such as strain or chemical composition often leave characteristic fingerprints in the angular dependence of electron scattering. Scanning transmission electron microscopy (STEM) is dedicated to probe scattered intensity with atomic resolution, but it drastically lacks angular resolution. Here we report both a setup to exploit the explicit angular dependence of scattered intensity and applications of angle-resolved STEM to semiconductor nanostructures. Our method is applied to measure nitrogen content and specimen thickness in a GaNxAs1-x layer independently at atomic resolution by evaluating two dedicated angular intervals. We demonstrate contrast formation due to strain and composition in a Si- based metal-oxide semiconductor field effect transistor (MOSFET) with GexSi1-x stressors as a function of the angles used for imaging. To shed light on the validity of current theoretical approaches this data is compared with theory, namely the Rutherford approach and contemporary multislice simulations. Inconsistency is found for the Rutherford model in the whole angular range of 16-255?mrad. Contrary, the multislice simulations are applicable for angles larger than 35?mrad whereas a significant mismatch is observed at lower angles. This limitation of established simulations is discussed particularly on the basis of inelastic scattering.

| S-EPMC5111052 | biostudies-literature

Electron counting detectors in scanning transmission electron microscopy via hardware signal processing.

Project description:Transmission electron microscopy is a pivotal instrument in materials and biological sciences due to its ability to provide local structural and spectroscopic information on a wide range of materials. However, the electron detectors used in scanning transmission electron microscopy are often unable to provide quantified information, that is the number of electrons impacting the detector, without exhaustive calibration and processing. This results in arbitrary signal values with slow response times that cannot be used for quantification or comparison to simulations. Here we demonstrate and optimise a hardware signal processing approach to augment electron detectors to perform single electron counting.

| S-EPMC10457289 | biostudies-literature