Abstract: Control of the texture and the biaxial alignment of sputter deposited films has provoked a great deal of interest due to its technological importance. indeed, many physical properties of thin films are influenced by the biaxial alignment. In this context, extensive research has been established to understand the growth mechanism of biaxially aligned Yttria Stabilized Zirconia (YSZ) as a buffer layer for high temperature superconducting copper oxides. In this work, the growth mechanism in general and the mechanism responsible of the biaxial alignment in detail were investigated for thin films of YSZ and TiN deposited by unbalanced magnetron sputtering using non-aligned polycrystalline stainless steel substrates. The mechanism responsible for the preferential out-of-plane alignment has been investigated by performing depositions on a non-tilted substrate. However, to study the in-plane alignment a tilted substrate was used. The microstructure of the deposited layers was characterised by Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). The crystallographic alignment has been investigated by X-ray diffraction (XRD) (angular scans and pole figures) and by Selective Area Diffraction (SAD). It was observed that the deposited layers show a zone T or zone II structure and the layers with a zone T structure consist of faceted grains. There seems to be a correlation between the crystal habit of these faceted grains and the measured biaxial alignment. A model for the preferential out-of-plane orientation, the in-plane alignment and the correlation between the microstructure and the biaxial alignment is proposed.

Abstract: Indoor environments are considered an important contributor to external human exposure to halogenated flame retardants (HFRs) due to the large amounts of chemicals currently incorporated in indoor equipment and the time humans spend every day in indoor environments. In this chapter, the presence and use of novel brominated flame retardants (NBFRs), dechlorane plus (DPs), chlorinated organophosphorus flame retardants (Cl-PFRs) and chlorinated paraffins (CPs) in indoor dust, air and consumer products collected from different indoor microenvironments (homes, public indoor spaces, and vehicles) are discussed. While data on the concentrations of HFRs in indoor dust and air are widely available, figures are still scarce for consumer products, such as textiles and foams, furnishings, flooring, electric and electronic products and building materials. This knowledge gaps still represents the biggest obstacle in linking eventual sources of contamination to the presence and chemical patterns in indoor dust and air.

Abstract: Recent technological advances resulted in high-quality superconducting metallic nanofilms and nanowires. The physical properties of such nanostructures are governed by the size-quantization of the transverse electron spectrum. This has a substantial impact on the basic superconducting characteristics, e.g., the order parameter, the critical temperature and the critical magnetic field. In the present paper we give an overview of our theoretical results on this subject. Based on a numerical self-consistent solution of the Bogoliubov-de Gennes equations, we investigate how the superconducting properties are modified in the quantum-size regime.

Abstract: As the field of perovskite optoelectronics developed, a plethora of strategies has arisen to control their electronic and morphological characteristics for the purpose of producing high efficiency devices. Unfortunately, despite this wealth of deposition approaches, the community experiences a great deal of irreproducibility between different laboratories, batches and preparation methods. Aiming to address this issue, we developed a simple deposition method based on gas quenching that yields smooth films for a wide range of perovskite compositions, in single, double, triple and quadruple cation varieties, and produces planar heterojunction devices with competitive efficiencies, so far up to 20%.

Abstract: We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition.