Notice: Server Maintenance (November 1st-5th, 2019)
NanotechJapan website, all inquiry forms and e-mail services will not be available. Sorry for the inconvenience.
 

News from Nanotechjapan

測定・評価

X-ray Spectro-ptychography Combined with Unsupervised Learning Visualized Oxidation Behavior in Oxygen Storage Materials

 On April 26, 2019, Riken, Osaka University, Japan Advanced Institute of Science and Technology (JAIST), Nagoya University, Tohoku University, and Japan Synchrotron Radiation Research Institute (JASRI) announced that the research group made up by the above organizations has succeeded in visualizing oxygen-diffusion-driven oxidation behavior and tracking areas in oxygen storage and release materials by X-ray spectro-ptychography with unsupervised learning. Details were published in Communications Chemistry with Yukio Takahashi at Riken as the senior author*.

 Oxygen storage and release material supports automobile exhaust catalyst by emission and absorption of diffused oxygen. A typical catalyst is Pt/ CZ-x (Ce-Zr solid solution oxide: Ce2Zr2Ox, where x = 7-8). Toxic component hydrocarbon and carbon mono-oxide are oxidized to carbon dioxide, and nitric oxide reduced to nitrogen through oxygen diffusion. However, details of the oxygen storage pathways in the CZ-x particles have remained unclear because of the difficulty in visualizing the oxygen diffusion tracks.

 The research group developed 3D hard X-ray spectro-ptychography (HXSP) imaging coupled with unsupervised learning to succeed in visualizing 3D nanoscale oxygen diffusion tracks. Using hard X-ray from SPring-8, X-ray CT technique was implemented in X-ray spectro-ptychography, in which coherent X-ray phases construct nanoscale 2D images, and combined with X-ray Absorption Fine Structure (XAFS) to find electronic states of absorbing atoms. Diffraction patterns as large as 110,000 are obtained, and their energy dependence resulted in 27,662,400 nanoscale (50 nm) spatial resolution 3D XAFS spectrum data. Data mining procedure using unsupervised learning visualized oxygen storage-release behavior from this big data.

*M. Hirose, N. Ishiguro, K. Shimomura, D.-N. Nguyen, H. Matsui, H. C. Dam, M. Tada and Y. Takahashi, "Oxygen-diffusion-driven Oxidation Behavior and Tracking Areas Visualized by X-ray Spectro-ptychography with Unsupervised Learning", Communications Chemistry Vol. 2, Article number: 50 (2019), DOI: 10.1038/s42004-019-0147-y; Published: 26 April 2019