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.

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 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: 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: 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: 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: 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: Temperature-programmed oxygen isotopic exchange study was performed on nanocrystalline tin dioxide-based materials synthesized via sol-gel route and modified by palladium. Such materials are widely used as resistive gas sensors. The experiments were carried out in a flow-reactor up to complete isotopic substitution of oxygen. Substantial rates of isotopic exchange for SnO2 were observed from about 700 K. The distribution of isotopic molecules O-16(2). (OO)-O-16-O-18 and O-18(2) corresponds to simple dioxygen heteroexchange mechanism with single lattice oxygen atom. The modification of SnO2 by Pd introduced multiple heteroexchange mechanism with preliminary O-2 dissociation on the clusters surface. Spill-over of atomic oxygen from Pd to the surface of SnO2 and fast exchange with lattice oxygen result in more than 100% increase of apparent heteroexchange rate. The exchange on SnO2/Pd was shown to be a complex process involving partial deactivation of the catalytic centers at temperature higher than 750 K. (C) 2011 Elsevier Inc. All rights reserved.

Abstract: Carbon black (CB) supported Ni, Fe, or Fe-Ni alloy catalysts were synthesized by sol-gel to elucidate the reaction pathways over each catalyst, as well as synergistic effects in glucose to sorbitol hydrogenation. The bimetallic materials presented small and alloyed nanoparticles that were richer in reduced metallic sites at the surface than their monometallic counterparts. Glucose isomerization to fructose was favoured over Fe/CB, while glucose hydrogenation to sorbitol is the dominating pathway over Ni/CB catalyst. By contrast, sorbitol production was promoted and undesired isomerization was suppressed when Fe and Ni formed a nanoalloy. In addition, the alloy catalyst presented better stability than the corresponding monometallic catalyst. A comparison with a mechanical mixture of Fe/CB and Ni/CB monometallic catalysts demonstrated the synergy at the nanoscale in the alloy. By comparing different Fe:Ni ratios, the 1:1 formulation was identified as the best compromise to achieve a high activity while maintaining high sorbitol selectivity.

Abstract: The limitation in time and length scale is a major issue of molecular dynamics (MD) simulation. Although several methods have been developed to extend the MD time scale, their performance usually deteriorates with increasing system size. Therefore, an acceleration method which is applicable to large systems is required to bridge the gap between the MD simulations and target phenomena. In this study, an accelerated MD method for large system is developed based on the collective variable-driven hyperdynamics (CVHD) method [K.M. Bal and E.C. Neyts, 2015]. The key idea is to run CVHD in parallel with rate control and accelerate multiple possible events simultaneously. Using this novel method, carbon diffusion in bcc-iron bicrystal with grain boundary is examined as an application for practical materials. Carbon atoms reaching at the grain boundary are trapped whereas carbon atoms in the bulk region diffuse randomly, and both dynamic regimes can be simultaneously accelerated with the parallel CVHD technique.

Abstract: We have developed an innovative methodology to overcome the lack of techniques for real-time assessment of degradable biomedical polymers at physiological conditions. The methodology was established by combining polymer characterization techniques with electrochemical sensors. The in vitro hydrolytic degradation of a series of aliphatic polyesters was evaluated by following the molar mass decrease and the mass loss at different incubation times while tracing pH and l-lactate released into the incubation media with customized miniaturized electrochemical sensors. The combination of different analytical approaches provided new insights into the mechanistic and kinetics aspects of the degradation of these biomedical materials. Although molar mass had to reach threshold values for soluble oligomers to be formed and specimens’ resorption to occur, the pH variation and l-lactate concentration were direct evidence of the resorption of the polymers and indicative of the extent of chain scission. Linear models were found for pH and released l-lactate as a function of mass loss for the l-lactide-based copolymers. The methodology should enable the sequential screening of degradable polymers at physiological conditions and has potential to be used for preclinical material’s evaluation aiming at reducing animal tests.

Abstract: This research is aimed to the study and application of an electrochemical immunosensor for the detection of ovalbumin (OVA) from egg white (or albumen) used as a binder in some works of art, such as some historical photographic prints and tempera paintings. The immunosensor takes advantage of the interesting biodetection capabilities offered by nanoelectrode ensembles (NEEs). The NEEs used to this aim are prepared by template deposition of gold nanoelectrodes within the pores of track-etched polycarbonate (PC) membranes. The affinity of polycarbonate for proteins is exploited to capture OVA from the aqueous extract obtained by incubation in phosphate buffer of a small sample fragment (<1 mg). The captured protein is reacted selectively with anti-OVA antibody, labelled with glucose oxidase (GOx). In the case of positive response, the addition of the GOx substrate (i.e. glucose) and a suitable redox mediator (a ferrocenyl derivative) reflects in the up rise of an electrocatalytic oxidation current, which depends on the OVA amount captured on the NEE, this amount correlating with OVA concentration in the extract. After optimization, the sensor is successfully applied to identify OVA in photographic prints dating back to the late 19th century, as well as in ancient tempera paintings from the 15th and 18th centuries.