First-Principles Calculation of Optoelectronic Properties in 2D Materials: The Polytypic WS Case.

The phenomenon of polytypism, namely unconventional crystal phases displaying a mixture of stacking sequences, represents a powerful handle to design and engineer novel physical properties in two-dimensional (2D) materials. In this work, we characterize from first-principles the optoelectronic properties associated with the 2H/3R polytypism occurring in WS nanomaterials by means of density functional theory (DFT) calculations. We evaluate the band gap, optical response, and energy-loss function associated with 2H/3R WS nanomaterials and compare our predictions with experimental measurements of electron energy-loss spectroscopy (EELS) carried out in nanostructures exhibiting the same polytypism.

Spatially Resolved Band Gap and Dielectric Function in Two-Dimensional Materials from Electron Energy Loss Spectroscopy.

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).

Morphology-induced spectral modification of self-assembled WS pyramids.

Due to their intriguing optical properties, including stable and chiral excitons, two-dimensional transition metal dichalcogenides (2D-TMDs) hold the promise of applications in nanophotonics. Chemical vapor deposition (CVD) techniques offer a platform to fabricate and design nanostructures with diverse geometries. However, the more exotic the grown nanogeometry, the less is known about its optical response.

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 WS nanostructures characterised by a 2H/3R mixed polytypism.

Robust Sample Preparation of Large-Area In- and Out-of-Plane Cross Sections of Layered Materials with Ultramicrotomy.

Layered materials (LMs) such as graphene or MoS have attracted a great deal of interest recently. These materials offer unique functionalities due to their structural anisotropy characterized by weak van der Waals bonds along the out-of-plane axis and covalent bonds in the in-plane direction. A central requirement to access the structural information on complex nanostructures built upon LMs is to control the relative orientation of each sample prior to their inspection, e.