Reconstructing the exit wave of 2D materials in high-resolution transmission electron microscopy using machine learning.

Reconstruction of the exit wave function is an important route to interpreting high-resolution transmission electron microscopy (HRTEM) images. Here we demonstrate that convolutional neural networks can be used to reconstruct the exit wave from a short focal series of HRTEM images, with a fidelity comparable to conventional exit wave reconstruction. We use a fully convolutional neural network based on the U-Net architecture, and demonstrate that we can train it on simulated exit waves and simulated HRTEM images of graphene-supported molybdenum disulphide (an industrial desulfurization catalyst). We then apply the trained network to analyse experimentally obtained images from similar samples, and obtain exit waves that clearly show the atomically resolved structure of both the MoS2 nanoparticles and the graphene support. We also show that it is possible to successfully train the neural networks to reconstruct exit waves for 3400 different two-dimensional materials taken from the Computational 2D Materials Database of known and proposed two-dimensional materials.

Probing atom dynamics of excited Co-Mo-S nanocrystals in 3D.

Advances in electron microscopy have enabled visualizations of the three-dimensional (3D) atom arrangements in nano-scale objects. The observations are, however, prone to electron-beam-induced object alterations, so tracking of single atoms in space and time becomes key to unravel inherent structures and properties. Here, we introduce an analytical approach to quantitatively account for atom dynamics in 3D atomic-resolution imaging. The approach is showcased for a Co-Mo-S nanocrystal by analysis of time-resolved in-line holograms achieving ~1.5 Å resolution in 3D. The analysis reveals a decay of phase image contrast towards the nanocrystal edges and meta-stable edge motifs with crystallographic dependence. These findings are explained by beam-stimulated vibrations that exceed Debye-Waller factors and cause chemical transformations at catalytically relevant edges. This ability to simultaneously probe atom vibrations and displacements enables a recovery of the pristine Co-Mo-S structure and establishes, in turn, a foundation to understand heterogeneous chemical functionality of nanostructures, surfaces and molecules.

The catalytic effects of sulfur in ethane dehydroaromatization.

In this work, we investigated the catalytic effect of adding sulfur on Zn/ZSM-5 catalyst for direct conversion of ethane to aromatics. We show that the continuous addition of hydrogen sulfide (H2S) effectively stabilizes zinc, prevents coking and results in a highly selective and stable catalyst. Considering the high content of sulfur in shale gas resources, these results highlight the importance of investigating catalysts under realistic operating conditions.

Visualizing the stoichiometry of industrial-style Co-Mo-S catalysts with single-atom sensitivity.

The functional properties of transition metal dichalcogenides (TMDs) may be promoted by the inclusion of other elements. Here, we studied the local stoichiometry of single cobalt promoter atoms in an industrial-style MoS2-based hydrotreating catalyst. Aberration-corrected scanning transmission electron microscopy and electron energy loss spectroscopy show that the Co atoms occupy sites at the (-100) S edge terminations of the graphite-supported MoS2 nanocrystals in the catalyst. Specifically, each Co atom has four neighboring S atoms that are arranged in a reconstructed geometry, which reflects an equilibrium state. The structure agrees with complementary studies of catalysts that were prepared under vastly different conditions and on other supports. In contrast, a small amount of residual Fe in the graphite is found to compete for the S edge sites, so that promotion by Co is strongly sensitive to the purity of the raw materials. The present single-atom-sensitive analytical method therefore offers a guide for advancing preparative methods for promoted TMD nanomaterials.

Clinical benefit of a gluten-free diet in type 1 diabetic children with screening-detected celiac disease: a population-based screening study with 2 years' follow-up.

OBJECTIVE: This study was performed to 1) determine the prevalence of celiac disease in Danish children with type 1 diabetes and 2) estimate the clinical effects of a gluten-free diet (GFD) in patients with diabetes and celiac disease. RESEARCH DESIGN AND METHODS: In a region comprising 24% of the Danish population, all patients <16 years old with type 1 diabetes were identified and 269 (89%) were included in the study. The diagnosis of celiac disease was suspected in patients with endomysium and tissue transglutaminase antibodies in serum and confirmed by intestinal biopsy. Patients with celiac disease were followed for 2 years while consuming a GFD. RESULTS: In 28 of 33 patients with celiac antibodies, an intestinal biopsy showed villous atrophy. In 5 patients, celiac disease had been diagnosed previously, giving an overall prevalence of 12.3% (95% CI 8.6-16.9). Patients with celiac disease had a lower SD score (SDS) for height (P < 0.001) and weight (P = 0.002) than patients without celiac disease and were significantly younger at diabetes onset (P = 0.041). A GFD was obtained in 31 of 33 patients. After 2 years of follow-up, there was an increase in weight SDS (P = 0.006) and in children <14 years old an increase in height SDS (P = 0.036). An increase in hemoglobin (P = 0.002) and serum ferritin (P = 0.020) was found, whereas HbA(1c) remained unchanged (P = 0.311) during follow-up. CONCLUSIONS: This population-based study showed the highest reported prevalence of celiac disease in type 1 diabetes in Europe. Patients with celiac disease showed clinical improvements with a GFD. We recommend screening for celiac disease in all children with type 1 diabetes.