Kyushu University Academic Staff Educational and Research Activities Database
List of Papers
Shiro Ihara Last modified date:2024.06.03

Assistant Professor / Department of Integrated Materials / Institute for Materials Chemistry and Engineering

1. Shiro Ihara, Mizumo Yoshinaga, Hiroya Miyazaki, Kota Wada, Satoshi Hata, Hikaru Saito and Mitsuhiro Murayama, In situ electron tomography for the thermally activated solid reaction of anaerobic nanoparticles, Nanoscale,, 15, 10133-10140, 2023.06.
2. Jesada Punyafu, Sukyoung Hwang, Shiro Ihara, Hikaru Saito, Nobuhiro Tsuji, Mitsuhiro Murayama, Microstructural factors dictating the initial plastic deformation behavior of an ultrafine-grained Fe–22Mn-0.6C TWIP steel, Materials Science and Engineering: A,, 862, 144506, 2023.01.
3. Avala Lavakumar, Shuhei Yoshida, Jesada Punyafu, Shiro Ihara, Yan Chong, Hikaru Saito, Nobuhiro Tsuji, Mitsuhiro Murayama, Yield and flow properties of ultra-fine, fine, and coarse grain microstructures of FeCoNi equiatomic alloy at ambient and cryogenic temperatures, Scripta Materialia,, 230, 115392, 2023.06.
4. S. Ihara,H. Saito,M. Yoshinaga,A. Lavakumar,M. Murayama, Deep learning-based noise filtering toward millisecond order imaging by using scanning transmission electron microscopy, Sci. Rep.,, 12, 13462, 2022.08, 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..
5. Y. Zhao, S. Koike, R. Nakama, S. Ihara, M. Mitsuhara, M. Murayama, S. Hata and H. Saito, Five-second STEM dislocation tomography for 300 nm thick specimen assisted by deep-learning-based noise filtering, Sci. Rep., 11, 20720, 2021.10, 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..