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.