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1.
Nanoscale ; 15(23): 10133-10140, 2023 Jun 15.
Article in English | MEDLINE | ID: mdl-37264793

ABSTRACT

The nanoscale characterization of thermally activated solid reactions plays a pivotal role in products manufactured by nanotechnology. Recently, in situ observation in transmission electron microscopy combined with electron tomography, namely four-dimensional observation for heat treatment of nanomaterials, has attracted great interest. However, because most nanomaterials are highly reactive, i.e., oxidation during transfer and electron beam irradiation would likely cause fatal artefacts; it is challenging to perform the artifact-free four-dimensional observation. Herein, we demonstrate our development of a novel in situ three-dimensional electron microscopy technique for thermally activated solid-state reaction processes in nanoparticles (NPs). The sintering behaviour of Cu NPs was successfully visualized and analyzed in four-dimensional space-time. An advanced image processing protocol and a newly designed state-of-the-art MEMS-based heating holder enable the implementation of considerably low electron dose imaging and prevent air exposure, which is of central importance in this type of observation. The total amount of electron dose for a single set of tilt-series images was reduced to 250 e- nm-2, which is the lowest level for inorganic materials electron tomography experiments. This study evaluated the sintering behaviour of Cu NPs in terms of variations in neck growth and particle distance. A negative correlation between the two parameters is shown, except for the particle pair bound by neighbouring NPs. The nanoscale characteristic sintering behavior of neck growth was also captured in this study.


Subject(s)
Electron Microscope Tomography , Nanoparticles , Anaerobiosis , Microscopy, Electron, Transmission , Microscopy, Electron
2.
Sci Rep ; 12(1): 13462, 2022 08 05.
Article in English | MEDLINE | ID: mdl-35931705

ABSTRACT

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.


Subject(s)
Deep Learning , Diagnostic Imaging , Image Processing, Computer-Assisted/methods , Microscopy, Electron, Scanning Transmission , Signal-To-Noise Ratio
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