This study presents an in-depth investigation of the electronic properties and bandgap energy distribution in 1D molybdenum disulfide (1D-MoS₂) nanostructures. Through a combination of high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) and electron energy-loss spectroscopy (EELS), it reveals significant differences between 1D-MoS₂ nanostructures and their 2D counterparts, shedding light on their localized exciton behavior and their bandgap energy modulation within the nanostructures. Excitonic peaks at around 2 and 3 eV appear localized at the ends or along the sides of the 1D-MoS₂ nanostructures, while the plasmonic resonance at 8.3 eV retains its inner-region localization. It demonstrates the spatial dependence of the bandgap energy, with the central region exhibiting a bandgap of approximately 1.2 eV, consistent with bulk MoS₂, while regions characterized by curvature-induced local strain fields exhibit instead a noticeable reduction. The findings provide valuable insights into the intricate relationship between excitonic behavior and bandgap sensitivity in 1D-MoS₂ nanostructures, streamlining the design and optimization of nanophotonic and optoelectronic devices.

Among the many potential applications of topological insulator materials, their broad potential for the development of novel tunable plasmonics at THz and mid-infrared frequencies for quantum computing, terahertz detectors, and spintronic devices is particularly attractive. The required understanding of the intricate relationship between nanoscale crystal structure and the properties of the resulting plasmonic resonances remains, however, elusive for these materials. Specifically, edge- and surface-induced plasmonic resonances, and other collective excitations, are often buried beneath the continuum of electronic transitions, making it difficult to isolate and interpret these signals using techniques such as electron energy-loss spectroscopy (EELS). Here we focus on the experimentally clean energy-gain EELS region to characterise collective excitations in the topologically insulating material Bi₂Te₃ and correlate them with the underlying crystalline structure with nanoscale resolution. We identify with high significance the presence of a distinct energy-gain peak around −0.8eV, with spatially-resolved maps revealing that its intensity is markedly enhanced at the edge regions of the specimen. Our findings illustrate the reach of energy-gain EELS analyses to accurately map collective excitations in quantum materials, a key asset in the quest towards new tunable plasmonic devices.

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.

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.

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.