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$_2$ nanostructures characterised by a 2H/3R mixed polytypism. From the resulting subtracted spectra we determine the nature and value of the bandgap of polytypic WS$_2$, finding $E_{\rm BG} = 1.6_{-0.2}^{+0.3}\,{\rm eV}$ 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.Comment: 37 pages, 14 figures. The EELSfitter code is available from https://github.com/LHCfitNikhef/EELSfitte

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$_2$ nanostructures characterised by a 2H/3R mixed polytypism. From the resulting subtracted spectra we determine the nature and value of the bandgap of polytypic WS$_2$, finding $E_{\rm BG} = 1.6_{-0.2}^{+0.3}\,{\rm eV}$ 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

Metallic edge states in zig-zag vertically-oriented MoS₂ nanowalls

The remarkable properties of layered materials such as MoS2 strongly depend on their dimensionality. Beyond manipulating their dimensions, it has been predicted that the electronic properties of MoS2 can also be tailored by carefully selecting the type of edge sites exposed. However, achieving full control over the type of exposed edge sites while simultaneously modifying the dimensionality of the nanostructures is highly challenging. Here we adopt a top-down approach based on focus ion beam in order to selectively pattern the exposed edge sites. This strategy allows us to select either the armchair (AC) or the zig-zag (ZZ) edges in the MoS2 nanostructures, as confirmed by high-resolution transmission electron microscopy measurements. The edge-type dependence of the local electronic properties in these MoS2 nanostructures is studied by means of electron energy-loss spectroscopy measurements. This way, we demonstrate that the ZZ-MoS2 nanostructures exhibit clear fingerprints of their predicted metallic character. Our results pave the way towards novel approaches for the design and fabrication of more complex nanostructures based on MoS2 and related layered materials for applications in fields such as electronics, optoelectronics, photovoltaics, and photocatalysts.QN/Conesa-Boj La

Illuminating the Electronic Properties of WS₂ Polytypism with Electron Microscopy

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

A comprehensive review of 30 years of pediatric clinical trial radiotherapy dose constraints

Background: Radiation therapy normal tissue dose constraints are critical when treating pediatric patients. However, there is limited evidence supporting proposed constraints, which has led to variations in constraints over the years. In this study, we identify these variations in dose constraints within pediatric trials both in the United States and in Europe used in the past 30 years. Procedure: All pediatric trials from the Children's Oncology Group website were queried from inception until January 2022 and a sampling of European studies was included. Dose constraints were identified and built into an organ-based interactive web application with filters to display data by organs at risk (OAR), protocol, start date, dose, volume, and fractionation scheme. Dose constraints were evaluated for consistency over time and compared between pediatric US and European trials. Results: One hundred five closed trials were included—93 US trials and 12 European trials. Thirty-eight separate OAR were found with high-dose constraint variability. Across all trials, nine organs had greater than 10 different constraints (median 16, range 11–26), including serial organs. When comparing US versus European dose tolerances, the United States constraints were higher for seven OAR, lower for one, and identical for five. No OAR had constraints change systematically over the last 30 years. Conclusion: Review of pediatric dose-volume constraints in clinical trials showed substantial variability for all OAR. Continued efforts focused on standardization of OAR dose constraints and risk profiles are essential to increase consistency of protocol outcomes and ultimately to reduce radiation toxicities in the pediatric population

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 WS2nanoflowers 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