In-situ heating-and-electron tomography for materials research: from 3D (in-situ 2D) to 4D (in-situ 3D).

In-situ observation has expanded the application of transmission electron microscopy (TEM) and has made a significant contribution to materials research and development for energy, biomedical, quantum, etc. Recent technological developments related to in-situ TEM have empowered the incorporation of three-dimensional observation, which was previously considered incompatible. In this review article, we take up heating as the most commonly used external stimulus for in-situ TEM observation and overview recent in-situ TEM studies. Then, we focus on the electron tomography (ET) and in-situ heating combined observation by introducing the authors' recent research as an example. Assuming that in-situ heating observation is expanded from two dimensions to three dimensions using a conventional TEM apparatus and a commercially available in-situ heating specimen holder, the following in-situ heating-and-ET observation procedure is proposed: (i) use a rapid heating-and-cooling function of a micro-electro-mechanical system holder; (ii) heat and cool the specimen intermittently and (iii) acquire a tilt-series dataset when the specimen heating is stopped. This procedure is not too technically challenging and can have a wide range of applications. Essential technical points for a successful 4D (space and time) observation will be discussed through reviewing the authors' example application.

In situ electron tomography for the thermally activated solid reaction of anaerobic nanoparticles.

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.

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

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.

Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering.

Scanning transmission electron microscopy (STEM) is suitable for visualizing the inside of a relatively thick specimen than the conventional transmission electron microscopy, whose resolution is limited by the chromatic aberration of image forming lenses, and thus, the STEM mode has been employed frequently for computed electron tomography based three-dimensional (3D) structural characterization and combined with analytical methods such as annular dark field imaging or spectroscopies. However, the image quality of STEM is severely suffered by noise or artifacts especially when rapid imaging, in the order of millisecond per frame or faster, is pursued. Here we demonstrate a deep-learning-assisted rapid STEM tomography, which visualizes 3D dislocation arrangement only within five-second acquisition of all the tilt-series images even in a 300 nm thick steel specimen. The developed method offers a new platform for various in situ or operando 3D microanalyses in which dealing with relatively thick specimens or covering media like liquid cells are required.