Geförderte OA-Publikationen

This paper describes the preparation and obtained magnetic properties of large single domain iron oxide nanoparticles. Such ferrimagnetic particles are particularly interesting for diagnostic and therapeutic applications in medicine or (bio)technology. The particles were prepared by a modified oxidation method of non-magnetic precursors following the green rust synthesis and characterized regarding their structural and magnetic properties. For increasing preparation temperatures (5 to 85 ˚C), an increasing particle size in the range of 30 to 60 nm is observed. Magnetic measurements confirm a single domain ferrimagnetic behavior with a mean saturation magnetization of ca. 90 Am2/kg and a size-dependent coercivity in the range of 6 to 15 kA/m. The samples show a specific absorption rate (SAR) of up to 600 W/g, which is promising for magnetic hyperthermia application. For particle preparation temperatures above 45 ˚C, a non-magnetic impurity phase occurs besides the magnetic iron oxides that results in a reduced net saturation magnetization.

Si sources involved in the growth of Au-SiOx nanostructures are investigated through the rapid thermal annealing of gold thin films on SiO2/Si substrates with various SiO2 layer thicknesses (3, 25, 100, 500 nm) in a reducing atmosphere. This method reveals three Si sources whose involvement depends on the thickness of the SiO2 layers, i.e., Si diffusion from the substrate, and SiO from SiO2 decomposition and from Si active oxidation. Increasing thicknesses of the SiO2 layer hampers the Si diffusion and the decomposition of regions of the SiO2 layer, which decreases the concentrations of discovered regions weakening the Si active oxidation. These discovered regions appear in systems with a SiO2 layer of 25 or 100 nm, while they are absent for a 500 nm layer. Furthermore, Au-SiOx nanostructures of different shapes form in each system. Both behaviors indicate that the influence and transport mechanisms of the different Si sources are largely dependent on the thicknesses of the SiO2 layers and that they control the evolution of the Au-SiOx nanostructures. A clear understanding of the relationship between these thicknesses and the possible Si sources and their roles in the evolution of the nanostructures makes the tailored fabrication of nanostructures possible.

For a continuous multi-objective optimization problem, it is usually not a practical approach to compute all its nondominated points because there are infinitely many of them. For this reason, a typical approach is to compute an approximation of the nondominated set. A common technique for this approach is to generate a polyhedron which contains the nondominated set. However, often these approximations are used for further evaluations. For those applications a polyhedron is a structure that is not easy to handle. In this paper, we introduce an approximation with a simpler structure respecting the natural ordering. In particular, we compute a box-coverage of the nondominated set. To do so, we use an approach that, in general, allows us to update not only one but several boxes whenever a new nondominated point is found. The algorithm is guaranteed to stop with a finite number of boxes, each being sufficiently thin.

When operating a conditionally automated vehicle, humans occasionally have to take over control. If the driver is out of the loop, a certain amount of time is necessary to gain situation awareness. This work evaluates the potential of stereoscopic 3D (S3D) dashboards for presenting smart S3D take-over-requests (TORs) to support situation assessment. In a driving simulator study with a 4 × 2 between-within design, we presented 3 smart TORs showing the current traffic situation and a baseline TOR in 2D and S3D to 52 participants doing the n-back task. We further investigate if non-standard locations affect the results. Take-over performance indicates that participants looked at and processed the TORs' visual information and by that, could perform more safe take-overs. S3D warnings in general, as well as warnings appearing at the participants’ focus of attention and warnings at the instrument cluster, performed best. We conclude that visual warnings, presented on an S3D dashboard, can be a valid option to support take-over while not increasing workload. We further discuss participants’ gaze behavior in the context of visual warnings for automotive user interfaces.