Uesugi Fumihiko, Koshiya Shogo, Kikkawa Jun, Nagai Takuro, Mitsuishi Kazutaka, Kimoto Koji
National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan.
National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan.
Ultramicroscopy. 2021 Feb;221:113168. doi: 10.1016/j.ultramic.2020.113168. Epub 2020 Nov 13.
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 TiO and reduced TiO 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.
用于材料表征的科学仪器最近得到了改进，能够产生大数据。例如，扫描透射电子显微镜（STEM）使我们能够从扫描区域获取许多衍射图样，这被称为四维（4D）STEM。在这里，我们研究了4D-STEM与一种称为非负矩阵分解（NMF）的统计技术的结合，以从包含10000个衍射图样的4D-STEM数据中推导出稀疏衍射图样。使用这种组合技术对氧化钛纳米片进行了分析，我们区分了原始TiO和还原TiO区域的两种衍射图样，后者是由电子辐照引起的拓扑还原所致。NMF和4D-STEM的结合有望成为广泛材料的标准表征技术。
