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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 WS$_2$ nanostructures characterised by a 2H/3R mixed polytypism. From the resulting subtracted spectra we determine the nature and value of the bandgap of polytypic WS$_2$, finding $E_{rm BG} = 1.6_{-0.2}^{+0.3},{rm eV}$ 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.
Experimental valence electron energy loss spectra (VEELS), up to the Li K edge, obtained on different phases of LixFePO4 are compared to first principles calculations using the density functional code WIEN2k. In the 4-7 eV range, a large peak is iden
Electron energy-loss spectroscopy (EELS) performed in transmission electron microscopes is shown to directly render the photonic local density of states (LDOS) with unprecedented spatial resolution, currently below the nanometer. Two special cases ar
Transmission electron microscopy, scanning transmission electron tomography, and electron energy loss spectroscopy were used to characterize three-dimensional artificial Si nanostructures called metalattices, focusing on Si metalattices synthesized b
Recently it has been demonstrated that a careful treatment of both longitudinal and transverse matrix elements in electron energy loss spectra can explain the mystery of relativistic effects on the {it magic angle}. Here we show that there is an addi
The spatial distributions of anti-bonding $pi^ast$ and $sigma^ast$ states in epitaxial graphene multilayers are mapped using electron energy-loss spectroscopy in a scanning transmission electron microscope. Inelastic channeling simulations validate t