九州大学-研究者情報 [井原史朗 (助教) 先導物質化学研究所融合材料部門]

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, https://doi.org/10.1039/D3NR00992K, 15, 10133-10140, 2023.06.
2.	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., https://doi.org/10.1038/s41598-022-17360-3, 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..
3.	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..

主要総説, 論評, 解説, 書評, 報告書等

全てを見る→

主要学会発表等

全てを見る→

1.	井原史朗, 深層学習の援用による走査透過電子顕微鏡法その場観察および3次元観察の高速化, マルチスケール材料力学部門委員会, 2023.10.
2.	井原史朗, STEM を用いた加熱その場観察における機械学習の応用, 超高分解能顕微鏡法分科会研究討論会, 2023.03.

学会活動

所属学会名

日本物理学会

日本顕微鏡学会

日本塑性加工学会

日本材料学会

日本機械学会

研究資金

科学研究費補助金の採択状況(文部科学省、日本学術振興会)

2022年度～2023年度, 若手研究, 代表, 転位組織を反映させたデータ同化型結晶塑性解析手法の開発.

2021年度～2022年度, 研究活動スタート支援, 代表, 機械学習による高速STEM像の高精度化および3次元転位その場観察への応用.

競争的資金(受託研究を含む)の採択状況

2022年度～2022年度, 池谷科学技術振興財団助成金, 代表, 機械学習を援用したデータ同化型結晶塑性解析手法の開発.

学内資金・基金等への採択状況

2021年度～2021年度, 汎オミクス計測・計算科学共同研究支援, 代表, その場観察に向けた高速STEM撮像における深層学習ノイズフィルタの開発.

2021年度～2021年度, ＱＲプログラム（わかばチャレンジ）, 代表, 機械学習を用いたSTEMによる3次元転位その場観察手法の開発.

九大関連コンテンツ

pure2017年10月2日から、「九州大学研究者情報」を補完するデータベースとして、Elsevier社の「Pure」による研究業績の公開を開始しました。

QIR　九州大学学術情報リポジトリシステム情報科学研究院

先導物質化学研究所


	※ 研究者情報全体を検索する場合は右端の「すべて」を選択して下さい。