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Charting the low-loss region in electron energy loss spectroscopy with machine learning
Ultramicroscopy ( IF 2.2 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.ultramic.2021.113202
Laurien I. Roest , Sabrya E. van Heijst , Louis Maduro , Juan Rojo , Sonia Conesa-Boj

Exploiting the information provided by electron energy-loss spectroscopy (EELS) requires reliable access to the low-loss region where the zero-loss peak (ZLP) often overwhelms the contributions associated to inelastic scatterings off the specimen. Here we deploy machine learning techniques developed in particle physics to realise a model-independent, multidimensional determination of the ZLP with a faithful uncertainty estimate. This novel method is then applied to subtract the ZLP for EEL spectra acquired in flower-like WS2 nanostructures characterised by a 2H/3R mixed polytypism. From the resulting subtracted spectra we determine the nature and value of the bandgap of polytypic WS2, finding EBG=1.6-0.2+0.3eV with a clear preference for an indirect bandgap. Further, we demonstrate how this method enables us to robustly identify excitonic transitions down to very small energy losses. Our approach has been implemented and made available in an open source Python package dubbed EELSfitter.

中文翻译:

使用机器学习绘制电子能量损失谱中的低损耗区域

利用电子能量损失光谱 (EELS) 提供的信息需要可靠地访问低损失区域,在该区域中,零损失峰值 (ZLP) 通常会压倒与样本非弹性散射相关的贡献。在这里,我们部署了在粒子物理学中开发的机器学习技术,以实现与模型无关的、具有忠实不确定性估计的 ZLP 多维确定。然后应用这种新方法减去以 2H/3R 混合多型为特征的花状 WS2 纳米结构中获得的 EEL 光谱的 ZLP。从得到的减影光谱中,我们确定了多型 WS2 带隙的性质和值,发现 EBG=1.6-0.2+0.3eV,明显偏爱间接带隙。更多,我们展示了这种方法如何使我们能够稳健地识别低至非常小的能量损失的激子跃迁。我们的方法已在名为 EELSfitter 的开源 Python 包中实现并提供。
更新日期:2021-03-01
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