First-Principles Calculation of Optoelectronic Properties in 2D Materials: The Polytypic WS2 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 WS2 nanomaterials by means of density functional theory (DFT) calculations. We evaluate the band gap, optical response, and energy-loss function associated with 2H/3R WS2 nanomaterials and compare our predictions with experimental measurements of electron energy-loss spectroscopy (EELS) carried out in nanostructures exhibiting the same polytypism. Our results provide further input to the ongoing efforts toward the integration of polytypic 2D materials into functional devices.

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). Individual spectra are classified as a function of the thickness with K-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 WS2 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.

Morphology-induced spectral modification of self-assembled WS2 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. WS2 nanostructures with geometries ranging from monolayers to hollow pyramids have been created. The hollow pyramids exhibit a strongly reduced photoluminescence with respect to horizontally layered tungsten disulphide, facilitating the study of their clear Raman signal in more detail. Excited resonantly, the hollow pyramids exhibit a great number of higher-order phononic resonances. In contrast to monolayers, the spectral features of the optical response of the pyramids are position dependent. Differences in peak intensity, peak ratio and spectral peak positions reveal local variations in the atomic arrangement of the hollow pyramid crater and sides. The position-dependent optical response of hollow WS2 pyramids is characterized and attributed to growth-induced nanogeometry. Thereby the first steps are taken towards producing tunable nanophotonic devices with applications ranging from opto-electronics to non-linear optics.

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 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.

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 MoS2 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.g., with transmission electron microscopy (TEM). However, developing sample preparation methods that result in large inspection areas and ensure full control over the sample orientation while avoiding damage during the transfer to the TEM grid is challenging. Here, we demonstrate the feasibility of deploying ultramicrotomy for the preparation of LM samples in TEM analyses. We show how ultramicrotomy leads to the reproducible large-scale production of both in-plane and out-of-plane cross sections, with bulk vertically oriented MoS2 and WS2 nanosheets as a proof of concept. The robustness of the prepared samples is subsequently verified by their characterization by means of both high-resolution TEM and Raman spectroscopy measurements. Our approach is fully general and should find applications for a wide range of materials as well as of techniques beyond TEM, thus paving the way to the systematic large-area mass-production of cross-sectional specimens for structural and compositional studies.