Abstract: In this article, we report on the synthesis of thin, epitaxial films of the transparent conductive oxide Al:ZnO on (0001)-oriented synthetic sapphire substrates by DC sputtering from targets with a nominal 1 at.% Al substitution. The deposition was carried out at an unusually low substrate temperature of only 250 °C in argonoxygen mixtures as well as in pure argon. The impact of the processgas composition on the morphology was analysed by transmission electron microscopy, revealing epitaxial growth in all the cases with a minor impact of the process parameters on the resulting grain sizes. The transport properties resistivity, Hall effect and magnetoresistance were studied in the range from 10 to 300 K in DC and pulsed magnetic fields up to 45 T. While the carrier density and mobility are widely temperature independent, we identified a low fieldlow temperature regime in which the magnetoresistance shows an anomalous, negative behaviour. At higher fields and temperatures, the magnetoresistance exhibits a more conventional, positive curvature with increasing field strength. As a possible explanation, we propose carrier scattering at localised magnetic trace impurities and magnetic correlations.

Abstract: In this paper, we report on the electronic transport properties of mesoscopic, ultrathin graphite flakes with a thickness corresponding to a stack of 150 graphene layers. The graphite flakes show an unexpectedly strong positive magnetoresistance (PMR) already at room temperature, which scales in good approximation with the square of the magnetic field. Furthermore, we show that the resistivity is unaffected by magnetic fields oriented in plane with the graphene layers. Hall effect measurements indicate that the charge carriers are p-type and their concentration increases with increasing temperature while the mobility is decreasing. The Hall voltage is non-linear in higher magnetic fields. Possible origins of the observed effects are discussed. Ball and stick model of the two topmost carbon layers of the hexagonal graphite structure.

Abstract: Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image

Abstract: In this review paper, we present several examples of reactive molecular dynamics simulations, which contribute to a better understanding of the underlying mechanisms in plasma medicine on the atomic scale. This includes the interaction of important reactive oxygen plasma species with the outer cell wall of both gram-positive and gram-negative bacteria, and with lipids present in human skin. Moreover, as most biomolecules are surrounded by a liquid biofilm, the behavior of these plasma species in a liquid (water) layer is presented as well. Finally, a perspective for future atomic scale modeling studies is given, in the field of plasma medicine in general, and for cancer treatment in particular.

Abstract: In the present work, reactive magnetron sputtering in DC mode was used to grow complex oxide thin films, starting from two separate pure metal targets. A series of coatings was produced with a stoichiometry of the film ranging from MgO, over MgxAlyOz to Al2O3. The surface energy, crystallinity, hardness, refractive index, and surface roughness were investigated. A relationship between all properties studied and the Mg content of the samples was found. A critical compositional region for the Mg-Al-O system where all properties exhibit a change was noticed.

Abstract: It is demonstrated that glycogen as a biodegradable and inexpensive material coming from renewable resources can be used as a carrier for the construction of in vivo imaging nanoagents. The model system considered is composed of glycogen modified with gadolinium and fluorescent labels. Systematic studies of properties of these nanocarriers by a variety of physical methods and results of in vivo tests of biodegradability are reported. This represents, to the authors' best knowledge, the first such use of glycogen.

Abstract: The peanut allergen Ara h 1 belonging to the vicilin family of seed storage proteins is known to be a suitable marker for peanut detection in foods. In the present work, we suggest an electrochemical detection of Ara h 1 using a recently selected 80-base DNA aptamer. The detection strategy relies on insulation effect of a bulky protein captured by an immobilized aptamer. The electrodes were modified by the aptamer and characterized using electrochemical impedance spectroscopy (EIS). The aptamer surface density and analytical parameters of the calibration curves for Ara h 1 were compared with the system prepared with thrombin binding aptamer (TBA) and operated in the same conditions.

Abstract: Zeolitic growth is often absent or occurs in separate phases when synthetic strategies based on the combination of zeolite templates and mesopore templating agents are applied. In this work, zeolitic growth and mesopore formation have been investigated at different temperatures by applying a one-pot templating approach, based on a TS-1 zeolite synthesis whereby part of the microtemplate (tetrapropylammonium hydroxide, TPAOH) is replaced by a mesotemplate (hexadecyltrimethylammonium bromide, CTMABr). Moreover, the synthesis duration and the molar ratio of the microtemplate/mesotemplate have also been studied. The different syntheses clearly show the inherent competitive mechanism between zeolitic growth and mesopore formation. These insights have led to the conclusion that by following a one-pot templating strategy with standard, nonexotic commercial templates, i.e. CTMABr and TPAOH, it is not possible to develop a true hierarchical mesoporous zeolite, meaning a mesoporous siliceous material with highly crystalline zeolitic walls. The resultant materials are instead combined zeolitic/mesoporous composite structures with, however, highly tuneable and controllable porosity characteristics.