The behaviour of electrons and holes in a crystal lattice is a fundamental quantum phenomenon, accounting for a rich variety of material properties. Boosted by the remarkable electronic and physical properties of two-dimensional materials such as graphene and topological insulators, transition metal dichalcogenides have recently received renewed attention. In this context, the anomalous bulk properties of semimetallic WTe2 have attracted considerable interest. Here we report angle- and spin-resolved photoemission spectroscopy of WTe2 single crystals, through which we disentangle the role of W and Te atoms in the formation of the band structure and identify the interplay of charge, spin and orbital degrees of freedom. Supported by first-principles calculations and high-resolution surface topography, we reveal the existence of a layer-dependent behaviour. The balance of electron and hole states is found only when considering at least three Te–W–Te layers, showing that the behaviour of WTe2 is not strictly two dimensional.

In this paper we applied transfer learning techniques for image recognition, automatic categorization, and labeling of nanoscience images obtained by scanning electron microscope (SEM). Roughly 20,000 SEM images were manually classified into 10 categories to form a labeled training set, which can be used as a reference set for future applications of deep learning enhanced algorithms in the nanoscience domain. The categories chosen spanned the range of 0-Dimensional (0D) objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces, and 3D patterned surfaces such as pillars. The training set was used to retrain on the SEM dataset and to compare many convolutional neural network models (Inception-v3, Inception-v4, ResNet). We obtained compatible results by performing a feature extraction of the different models on the same dataset. We performed additional analysis of the classifier on a second test set to further investigate the results both on particular cases and from a statistical point of view. Our algorithm was able to successfully classify around 90% of a test dataset consisting of SEM images, while reduced accuracy was found in the case of images at the boundary between two categories or containing elements of multiple categories. In these cases, the image classification did not identify a predominant category with a high score. We used the statistical outcomes from testing to deploy a semi-automatic workflow able to classify and label images generated by the SEM. Finally, a separate training was performed to determine the volume fraction of coherently aligned nanowires in SEM images. The results were compared with what was obtained using the Local Gradient Orientation method. This example demonstrates the versatility and the potential of transfer learning to address specific tasks of interest in nanoscience applications.

We report on the growth and characterization of ultrathin YBa2Cu3O 7−δ (YBCO) films on MgO (110) substrates, which exhibit superconducting properties at thicknesses down to 3 nm. YBCO nanowires, with thicknesses down to 10 nm and widths down to 65 nm, have been also successfully fabricated. The nanowires protected by a Au capping layer show superconducting properties close to the as-grown films, and critical current densities, which are only limited by vortex dynamics. The 10 nm thick YBCO nanowires without the Au capping present hysteretic current voltage characteristics, characterized by a voltage switch which drives the nanowires directly from the superconducting to the normal state. Such bistability is associated in NbN nanowires to the presence of localized normal domains within the superconductor. The presence of the voltage switch, in ultrathin nanostructures characterized by high sheet resistance values, though preserving high quality superconducting properties, make our nanowires very attractive devices to engineer single photon detectors.arXiv:1708.04721v1 [cond-mat.supr-con]

Using high-resolution transmission electron microscopy and image simulation techniques in combination with ab initio calculations, we show the existence of two different superlattices of crystallographic shear planes, analogous to the Magnéli phases of rutile, in oxygen-deficient films of anatase TiO 2 epitaxially grown on LaAlO 3 substrates. (103)-and (101)-oriented shear plane structures are detected in the outer film region and in proximity of the film/substrate interface, respectively. We show that these shear planes are characterized by TiO-like cubic local structures, which can deviate from the Ti n O 2n−1 stoichiometry of the classical rutile-derived Magnéli phases, particularly in the outer part of the film. Computed formation energies provide insights into the thermodynamic stability of the observed structures and their relations to the growth dynamics.

We report the study of anatase TiO(001)-oriented thin films grown by pulsed laser deposition on LaAlO(001). A combination of in situ and ex situ methods has been used to address both the origin of the Ti-localized states and their relationship with the structural and electronic properties on the surface and the subsurface. Localized in-gap states are analyzed using resonant X-ray photoelectron spectroscopy and are related to the Ti electronic configuration, homogeneously distributed over the entire film thickness. We find that an increase in the oxygen pressure corresponds to an increase in Ti only in a well-defined range of deposition pressure; outside this range, Ti and the strength of the in-gap states are reduced.