Granerød Cecilie S, Zhan Wei, Prytz Øystein
Department of Physics, Centre for Materials Science and Nanotechnology, University of Oslo, P. O. Box 1048 Blindern, N-0316 Oslo, Norway.
Department of Physics, Centre for Materials Science and Nanotechnology, University of Oslo, P. O. Box 1048 Blindern, N-0316 Oslo, Norway.
Ultramicroscopy. 2018 Jan;184(Pt A):39-45. doi: 10.1016/j.ultramic.2017.08.006. Epub 2017 Aug 17.
Band gap variations in thin film structures, across grain boundaries, and in embedded nanoparticles are of increasing interest in the materials science community. As many common experimental techniques for measuring band gaps do not have the spatial resolution needed to observe these variations directly, probe-corrected Scanning Transmission Electron Microscope (STEM) with monochromated Electron Energy-Loss Spectroscopy (EELS) is a promising method for studying band gaps of such features. However, extraction of band gaps from EELS data sets usually requires heavy user involvement, and makes the analysis of large data sets challenging. Here we develop and present methods for automated extraction of band gap maps from large STEM-EELS data sets with high spatial resolution while preserving high accuracy and precision.
薄膜结构、跨晶界以及嵌入纳米颗粒中的带隙变化在材料科学界越来越受到关注。由于许多用于测量带隙的常见实验技术没有直接观察这些变化所需的空间分辨率，配备单色电子能量损失谱（EELS）的探针校正扫描透射电子显微镜（STEM）是研究此类特征带隙的一种很有前景的方法。然而，从EELS数据集中提取带隙通常需要用户大量参与，并且使得大数据集的分析具有挑战性。在这里，我们开发并展示了从具有高空间分辨率的大型STEM-EELS数据集中自动提取带隙图的方法，同时保持高精度和高精确度。
