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説明:

Scientific instruments for material characterization have recently been improved to yield big data. For instance, scanning transmission electron microscopy (STEM) allows us to acquire many diffraction patterns from a scanning area, which is referred to as four-dimensional (4D) STEM. Here we study a combination of 4D-STEM and a statistical technique called non-negative matrix factorization (NMF) to deduce sparse diffraction patterns from a 4D-STEM data consisting of 10,000 diffraction patterns. Titanium oxide nanosheets are analyzed using this combined technique, and we discriminate the two diffraction patterns from pristine TiO2 and reduced Ti2O3 areas, where the latter is due to topotactic reduction induced by electron irradiation. The combination of NMF and 4D-STEM is expected to become a standard characterization technique for a wide range materials.

権利情報:

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International
  

  Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International

キーワード: electron microscopy, four-dimensional scanning transmission electron microscopy, non-negative matrix factorization

刊行年月日: 2020-11-13

出版者: Elsevier BV

掲載誌:

• Ultramicroscopy (ISSN: 03043991)

研究助成金:

• MEXT (MEXT Elements Science and Technology Project and Elements Strategy Initiative to Form Core Research Center)
• MEXT JP16H06440 (MEXT Elements Science and Technology Project and Elements Strategy Initiative to Form Core Research Center)

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.3861

公開URL: https://doi.org/10.1016/j.ultramic.2020.113168

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更新時刻: 2024-01-05 22:11:42 +0900

MDRでの公開時刻: 2022-08-02 19:16:14 +0900