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.

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.

The fabrication of 2D materials, such as transition metal dichalcogenides (TMDs), in geometries beyond the standard platelet-like configuration exhibits significant challenges which severely limit the range of available morphologies. These challenges arise due to the anisotropic character of their bonding van der Waals out-of-plane while covalent in-plane. Furthermore, industrial applications based on TMD nanostructures with non-standard morphologies require full control on the size-, morphology-, and position on the wafer scale. Such a precise control remains an open problem of which solution would lead to the opening of novel directions in terms of optoelectronic applications. Here, a novel strategy to fabricate position-controlled Mo/MoS₂ core–shell nanopillars (NPs) is reported on. Metal-Mo NPs are first patterned on a silicon wafer. These Mo NPs are then used as scaffolds for the synthesis of Mo/MoS₂ core/shell NPs by exposing them to a rich sulfur environment. Transmission electron microscopy analysis reveals the core/shell nature of the NPs. It is demonstrated that individual Mo/MoS₂ NPs exhibits significant nonlinear optical processes driven by the MoS₂ shell, realizing a precise localization of the nonlinear signal. These results represent an important step towards realizing 1D TMD-based nanostructures as building blocks of a new generation of nanophotonic devices.

We report on the fabrication of molybdenum (Mo) nanopillar (NP) arrays with NP diameters down to 75 nm by means of deep-reactive ion etching at cryogenic temperatures. A variable-thickness Mo metal layer sputtered onto a Si₃N₄/Si substrate makes possible NPs with different lengths in a controllable manner. We demonstrate how our fabrication strategy leads to tunable cross-sections with different geometries, including hexagonal, cylindrical, square and triangular shapes, by using electron beam lithography on hydrogen silsesquioxane negative tone resist. To ensure well-defined facets and surfaces, we employ deep-reactive ion etching in a gas mixture of SF₆ and O₂ at cryogenic temperatures in an inductively coupled plasma reactive ion etching (ICP-RIE) system. These results represent an attractive route towards the realization of high-density Mo NP arrays for applications from nanoelectronics to quantum sensing and hydrogen evolution reaction catalysis.

Tailoring the specific stacking sequence (polytypes) of layered materials represents a powerful strategy to identify and design novel physical properties. While nanostructures built upon transition-metal dichalcogenides (TMDs) with either the 2H or 3R crystalline phases have been routinely studied, knowledge of TMD nanomaterials based on mixed 2H/3R polytypes is far more limited. In this work, mixed 2H/3R free-standing WS₂ nanostructures displaying a flower-like configuration are fingerprinted by means of state-of-the-art transmission electron microscopy. Their rich variety of shape-morphology configurations is correlated with relevant local electronic properties such as edge, surface, and bulk plasmons. Machine learning is deployed to establish that the 2H/3R polytype displays an indirect band gap of (Formula presented.). Further, high resolution electron energy-loss spectroscopy reveals energy-gain peaks exhibiting a gain-to-loss ratio greater than unity, a property that can be exploited for cooling strategies of atomically-thin TMD nanostructures and devices built upon them. The findings of this work represent a stepping stone towards an improved understanding of TMD nanomaterials based on mixed crystalline phases.

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.

Edge structures are low-dimensional defects unavoidable in layered materials of the transition metal dichalcogenides (TMD) family. Among the various types of such structures, the armchair (AC) and zigzag (ZZ) edge types are the most common. It has been predicted that the presence of intrinsic strain localized along these edges structures can have direct implications for the customization of their electronic properties. However, pinning down the relation between local structure and electronic properties at these edges is challenging. Here, we quantify the local strain field that arises at the edges of MoS₂ flakes by combining aberration-corrected transmission electron microscopy (TEM) with the geometrical-phase analysis (GPA) method. We also provide further insight on the possible effects of such edge strain on the resulting electronic behavior by means of electron energy loss spectroscopy (EELS) measurements. Our results reveal that the two-dominant edge structures, ZZ and AC, induce the formation of different amounts of localized strain fields. We also show that by varying the free edge curvature from concave to convex, compressive strain turns into tensile strain. These results pave the way toward the customization of edge structures in MoS₂, which can be used to engineer the properties of layered materials and thus contribute to the optimization of the next generation of atomic-scale electronic devices built upon them.