Abstract: As a semiconductor ferroelectric, GeTe has become a focus of renewed attention due to the recent discovery of giant Rashba splitting. It already has a wide range of applications, from thermoelectricity to data storage. Its stability in ambient air, as well as the structure and properties of an oxide layer, define the processing media for device production and operation. Here, we studied a reaction between the GeTe (111) surface and molecular oxygen for crystals having solely inversion domains. We evaluated the reaction kinetics both ex situ and in situ using NAP XPS. The structure of the oxide layer is extensively discussed, where, according to HAADF-STEM and STEM-EDX, nanoscale phase separation of GeO2 and Te is observed, which is unusual for semiconductors. We believe that such behaviour is closely related to the ferroelectric properties and the domain structure of GeTe.

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.

Abstract: Nanoscience and nanotechnologies are of immense importance across many fields of science and for numerous practical applications. In this context, scanning transmission electron microscopy (STEM) and 4D-STEM are among the most powerful characterization methods at the atomic scale. Annular dark-field (ADF)-STEM can be used to quantify atomic structures in 3D by counting atoms based on a single projection image. In 4D-STEM a full diffraction pattern is recorded at each scan step, which enables more dose efficient imaging and the utilization of various advanced imaging modalities, which can however be complex and slow. Both, STEM and 4D-STEM suffer from noise and distortions. In the first section of this work the most important of these distortions are discussed and it is shown how image restoration with a dedicated convolutional neural network (CNN) can be beneficial for atomic structure quantifications in ADF-STEM. In the second part, a new 4D-STEM imaging method real-time-integrated-centre-of-mass (riCOM) is introduced, which is a very dose-efficient and fast algorithm that enables unprecedented live-imaging capabilities for 4D-STEM. It is based on the integrated centre-of-mass approach, but is reformulated with variable integration ranges and optional filters, which allows for a tunable contrast transfer function. This enables the imaging of light and heavy elements simultaneously at very low doses. In the third part another new 4D-STEM method, coined AIRPI (AI-assisted rapid phase imaging) is introduced, which uses a CNN to retrieve a patch of the specimen's phase image for each scan position, based on the diffraction patterns in the probe's immediate surroundings. This allows also live imaging in principle and surpasses comparable state-of-the-art algorithms in terms of resolution also at low doses. Different atomic columns can be reliably distinguished over a wide range of atomic numbers, enabling a very good image interpretability. Further, AIRPI can recover low frequency image components, which preserves thickness information. This is a unique and important feature which could make quantitative 4D-STEM feasible.

Abstract: Plasma bioscience and medicine are both rapidly growing fields. Their aim is to utilize cold physical plasmas for desired biological outcomes in medicine, biotechnology, agriculture, and general hygienic purposes. Great success has been achieved in many applications with individually designed plasma sources and plasma parameters. Although lab and application-specific tuning of plasmas is a great advantage of this technology, standardized units to define plasma treatments are required to facilitate comparison of the effects found by different researchers who do not use the same plasma sources. By drawing conclusions from over a century of plasma biomedical research, we propose that all researchers adopt the use of a standardized value, the plasma treatment unit (PTU), to describe the biological effects of different cold plasma sources and treatment regimens. It quantifies a key plasma effector in biological systems as an indicator and may provide the foundation for an analogous and clinically relevant unit in the future.

Abstract: The requirements for new technologies to serve as anticancer agents go far beyond their toxicity potential. Novel applications also need to be safe on a molecular and patient level. In a broader sense, this also relates to cancer metastasis and inflammation. In a previous study, the toxicity of an atmospheric pressure argon plasma jet in four human pancreatic cancer cell lines was confirmed and plasma treatment did not promote metastasis in vitro and in ovo. Here, these results are extended by additional types of analysis and new models to validate and define on a molecular level the changes related to metastatic processes in pancreatic cancer cells following plasma treatment in vitro and in ovo. In solid tumors that were grown on the chorion-allantois membrane of fertilized chicken eggs (TUM-CAM), plasma treatment induced modest to profound apoptosis in the tissues. This, however, was not associated with a change in the expression levels of adhesion molecules, as shown using immunofluorescence of ultrathin tissue sections. Culturing of the cells detached from these solid tumors for 6d revealed a similar or smaller total growth area and expression of ZEB1, a transcription factor associated with cancer metastasis, in the plasma-treated pancreatic cancer tissues. Analysis of in vitro and in ovo supernatants of 13 different cytokines and chemokines revealed cell line-specific effects of the plasma treatment but a noticeable increase of, e.g., growth-promoting interleukin 10 was not observed. Moreover, markers of epithelial-to-mesenchymal transition (EMT), a metastasis-promoting cellular program, were investigated. Plasma-treated pancreatic cancer cells did not present an EMT-profile. Finally, a realistic 3D tumor spheroid co-culture model with pancreatic stellate cells was employed, and the invasive properties in a gel-like cellular matrix were investigated. Tumor outgrowth and spread was similar or decreased in the plasma conditions. Altogether, these results provide valuable insights into the effect of plasma treatment on metastasis-related properties of cancer cells and did not suggest EMT-promoting effects of this novel cancer therapy.

Abstract: We investigate the possibility to laterally confine surface electrons on a liquid helium surface by inserting magnetic discs and stripes which generate nonhomogeneous magnetic field profiles. (C) 2002 Elsevier Science B.V. All rights reserved.

Abstract: The behavior of excitons in spatially modulated magnetic fields is described taking into account the exciton spin contribution. The results show that the exciton trapping in periodic magnetic fields is possible and dependent on the modulation profile.