Brokkelkamp Abel, Ter Hoeve Jaco, Postmes Isabel, van Heijst Sabrya E, Maduro Louis, Davydov Albert V, Krylyuk Sergiy, Rojo Juan, Conesa-Boj Sonia
Kavli Institute of Nanoscience, Delft University of Technology, 2628CJ Delft, The Netherlands.
Nikhef Theory Group, Science Park 105, 1098 XG Amsterdam, The Netherlands.
J Phys Chem A. 2022 Feb 24;126(7):1255-1262. doi: 10.1021/acs.jpca.1c09566. Epub 2022 Feb 15.
The electronic properties of two-dimensional (2D) materials depend sensitively on the underlying atomic arrangement down to the monolayer level. Here we present a novel strategy for the determination of the band gap and complex dielectric function in 2D materials achieving a spatial resolution down to a few nanometers. This approach is based on machine learning techniques developed in particle physics and makes possible the automated processing and interpretation of spectral images from electron energy loss spectroscopy (EELS). Individual spectra are classified as a function of the thickness with -means clustering, and then used to train a deep-learning model of the zero-loss peak background. As a proof of concept we assess the band gap and dielectric function of InSe flakes and polytypic WS nanoflowers and correlate these electrical properties with the local thickness. Our flexible approach is generalizable to other nanostructured materials and to higher-dimensional spectroscopies and is made available as a new release of the open-source EELSfitter framework.
二维（2D）材料的电子特性对直至单层水平的底层原子排列敏感地依赖。在此，我们提出一种用于确定二维材料中带隙和复介电函数的新策略，实现低至几纳米的空间分辨率。该方法基于粒子物理学中开发的机器学习技术，并使得对来自电子能量损失谱（EELS）的光谱图像进行自动化处理和解释成为可能。通过均值聚类将各个光谱作为厚度的函数进行分类，然后用于训练零损失峰背景的深度学习模型。作为概念验证，我们评估了InSe薄片和多型WS纳米花的带隙和介电函数，并将这些电学性质与局部厚度相关联。我们灵活的方法可推广到其他纳米结构材料和更高维光谱学，并且作为开源EELSfitter框架的新版本可用。
