Charting the low-loss region in electron energy loss spectroscopy with machine learning

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 E_BG=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.

Materials science applications of HREELS in near edge structure analysis and low-energy loss spectroscopy

New experiments made possible with a commercial transmission electron microscope (TEM) equipped with a high-resolution electron energy loss spectrometer (EELS) are presented. With this commercial system, a 100 meV energy resolution using a sub 2 nm probe or 500 meV at a 0.20 nm probe are possible, in combination with other modern techniques available for TEMs. In this paper a number of explorative examples of the first results are shown. The benefit of the increased resolution for detecting more details in near edge structures are shown for the Ti K edge in TiO(2) (brookite) and for the N K edge in cubic and hexagonal GaN. The bandgap of GaN is studied in both crystal structures, as well as the dependency of the low-loss spectrum on the momentum transfer direction in diffraction mode.