Evolution and features of dust devil-like vortices in turbulent Rayleigh-Bénard convection - an experimental study. - In: JGR, ISSN 2169-8996, Bd. 128 (2023), 2, e2022JD037466, S. 1-20
We present an experimental study simulating atmospheric dust devils in a controlled laboratory experiment. The experimental facility, called the “Barrel of Ilmenau” (www.ilmenauer-fass.de) represents a classical Rayleigh-Bénard set-up and is believed to model the phenomena in a convective atmospheric boundary layer fairly well. Our work complements and extends the numerical work of Giersch and Raasch (2021) https//doi.org/10.1029/2020jd034334 by experiments. Dust devils are thermal convective vortices with a vertical axis of rotation visualized by entrained soil particles. They evolve in the convective atmospheric boundary layer and are believed to substantially contribute to the aerosol transport into the atmosphere. Thus, their evolution, size, lifetime, and frequency of occurrence are of particular research interest. Extensive experimental studies have been conducted by field measurements and laboratory experiments so far. Beyond that, our study is the first attempt of Rayleigh-Bénard convection (RBC) in air to investigate dust devil-like vortices in a laboratory experiment. Up to now, this set-up mimics the natural process of dust devil evolution as closest to reality. The flow measurement was carried out by particle tracking velocimetry using neutrally buoyant soap bubbles. We initially identified dust devil-like vortices by eye from the Lagrangian velocity field, and in a later, more sophisticated analysis by a specific algorithm from the corresponding Eulerian velocity field. We analyzed their frequency of occurrence, observation time, and size. With our work, we could demonstrate that turbulent RBC is an appropriate model to mimic the natural process of the evolution of dust devils in the convective atmospheric boundary layer without artificial stimulation.
Particle detection and size recognition based on defocused particle images: a comparison of a deterministic algorithm and a deep neural network. - In: Experiments in fluids, ISSN 1432-1114, Bd. 64 (2023), 2, 21, S. 1-16
The systematic manipulation of components of multimodal particle solutions is a key for the design of modern industrial products and pharmaceuticals with highly customized properties. In order to optimize innovative particle separation devices on microfluidic scales, a particle size recognition with simultaneous volumetric position determination is essential. In the present study, the astigmatism particle tracking velocimetry is extended by a deterministic algorithm and a deep neural network (DNN) to include size classification of particles of multimodal size distribution. Without any adaptation of the existing measurement setup, a reliable classification of bimodal particle solutions in the size range of 1.14 μm–5.03 μm is demonstrated with a precision of up to 99.9 %. Concurrently, the high detection rate of the particles, suspended in a laminar fluid flow, is quantified by a recall of 99.0 %. By extracting particle images from the experimentally acquired images and placing them on a synthetic background, semi-synthetic images with consistent ground truth are generated. These contain labeled overlapping particle images that are correctly detected and classified by the DNN. The study is complemented by employing the presented algorithms for simultaneous size recognition of up to four particle species with a particle diameter in between 1.14 μm and 5.03 μm. With the very high precision of up to 99.3 % at a recall of 94.8 %, the applicability to classify multimodal particle mixtures even in dense solutions is confirmed. The present contribution thus paves the way for quantitative evaluation of microfluidic separation and mixing processes.
Effect of poly-crystallinity on the magnetoelectric behavior of TiN/AlN/Ni MEMS cantilevers investigated by finite element methods. - In: Physica status solidi, ISSN 1862-6319, Bd. 0 (2023), 0, 2200839, S. 1-6
Herein, magnetoelectric microelectromechanical system (MEMS) cantilevers are investigated on basis of a TiN/AlN/Ni laminate derived from experimental sensors using finite-element simulations. With the anisotropic ΔE effect as an implication of the magnetocrystalline anisotropy, the lateral sensitivity of the sensor is studied for different nickel layer thicknesses and boundary conditions. It is found that above 60% of the cantilever length, the nickel is effectively not contributing to the sensor sensitivity anymore which is supported by the investigation of sensors with partial nickel coverage. The boundary condition of the magnetostrictive layer is found to affect the sensitivity of thick layers while it is negligible for thinning layers. Further investigations on basis of polycrystalline untextured nickel with slightly preferred orientations reveal a stronger effect on thin layers than on thicker ones. It is found to arise from relatively large crystals in the high-sensitivity region near the clamping of the sensor. For thicker polycrystalline layers, the ΔE effect reproduces a characteristic based mainly on the (110) and (111) orientations while the (100) orientation appears to be underrepresented.
Reactive magnetron sputtering of large-scale 3D aluminum-based plasmonic nanostructure for both light-induced thermal imaging and photo-thermoelectric conversion. - In: Advanced optical materials, ISSN 2195-1071, Bd. 11 (2023), 6, 2202664, S. 1-7
Plasmonic nanostructures have attracted tremendous interest due to their special capability to trap light, which is of great significance for many applications such as solar steam generation and desalination, electric power generation, photodetection, sensing, catalysis, cancer therapy, and photoacoustic imaging. However, the noble metal-based (Au, Ag, Pd) plasmonic nanostructures with expensive costs and limitations to large-scale fabrication restrict their practical applications. Here, a novel and noble-metal-free Al/AlN plasmonic nanostructure fabricated by a reactive magnetron sputtering at the elevated temperature of 200 ˚C is presented. The unique 3D Al/AlN plasmonic nanostructures show a highly efficient (96.8%) and broadband (full solar spectrum) absorption and a strong photothermal conversion effect on its surface, demonstrating the potential in applications in light-induced thermal imaging and photo-thermoelectric power generation. This simple fabrication method and the developed Al/AlN plasmonic nanostructure combine excellent light trapping performance, abundant and low-cost Al and N elements, good heat localization effect, and scalable fabrication method, suggesting a promising alternative to noble-metal plasmonic nanostructures for photonic applications.
Fundamental understanding of nonaqueous and hybrid Na-CO2 batteries: challenges and perspectives. - In: Small, ISSN 1613-6829, Bd. 19 (2023), 5, 2206445, S. 1-30
Alkali metal-CO2 batteries, which combine CO2 recycling with energy conversion and storage, are a promising way to address the energy crisis and global warming. Unfortunately, the limited cycle life, poor reversibility, and low energy efficiency of these batteries have hindered their commercialization. Li-CO2 battery systems have been intensively researched in these aspects over the past few years, however, the exploration of Na-CO2 batteries is still in its infancy. To improve the development of Na-CO2 batteries, one must have a full picture of the chemistry and electrochemistry controlling the operation of Na-CO2 batteries and a full understanding of the correlation between cell configurations and functionality therein. Here, recent advances in CO2 chemical and electrochemical mechanisms on nonaqueous Na-CO2 batteries and hybrid Na-CO2 batteries (including O2-involved Na-O2/CO2 batteries) are reviewed in-depth and comprehensively. Following this, the primary issues and challenges in various battery components are identified, and the design strategies for the interfacial structure of Na anodes, electrolyte properties, and cathode materials are explored, along with the correlations between cell configurations, functional materials, and comprehensive performances are established. Finally, the prospects and directions for rationally constructing Na-CO2 battery materials are foreseen.
Review of individualized current flow modeling studies for transcranial electrical stimulation. - In: Journal of neuroscience research, ISSN 1097-4547, Bd. 101 (2023), 4, S. 405-423, insges. 19 S.
There is substantial intersubject variability of behavioral and neurophysiological responses to transcranial electrical stimulation (tES), which represents one of the most important limitations of tES. Many tES protocols utilize a fixed experimental parameter set disregarding individual anatomical and physiological properties. This one-size-fits-all approach might be one reason for the observed interindividual response variability. Simulation of current flow applying head models based on available anatomical data can help to individualize stimulation parameters and contribute to the understanding of the causes of this response variability. Current flow modeling can be used to retrospectively investigate the characteristics of tES effectivity. Previous studies examined, for example, the impact of skull defects and lesions on the modulation of current flow and demonstrated effective stimulation intensities in different age groups. Furthermore, uncertainty analysis of electrical conductivities in current flow modeling indicated the most influential tissue compartments. Current flow modeling, when used in prospective study planning, can potentially guide stimulation configurations resulting in individually effective tES. Specifically, current flow modeling using individual or matched head models can be employed by clinicians and scientists to, for example, plan dosage in tES protocols for individuals or groups of participants. We review studies that show a relationship between the presence of behavioral/neurophysiological responses and features derived from individualized current flow models. We highlight the potential benefits of individualized current flow modeling.
Application of capacitive sensors and controlled injection pressure to minimize void formation in resin transfer molding. - In: Polymer composites, ISSN 1548-0569, Bd. 44 (2023), 3, S. 1658-1671
Void formation as a result of irregular resin flow at the flow front is discussed and a practical method for reducing void formation during resin transfer molding (RTM) is introduced. In this study, a sensor system is developed for in situ measurement of resin velocity inside a closed cavity. Assisted by the acquired data, a resin injection system is augmented to automatically adjust the injection pressure and achieve a uniform flow front velocity. It is proven, that the developed system is suited to monitor the resin flow front and is able to sufficiently control flow velocity of a linear flow front. Test specimen produced by this method show significantly reduced void contents in comparison to a common resin transfer molding process.
Steep resonance of parametrically excited active MEMS cantilevers for dynamic mode in Atomic Force Microscopy. - In: Proceedings in applied mathematics and mechanics, ISSN 1617-7061, Bd. 22 (2023), 1, e202200230, S. 1-6
Ongoing developments in nanotechnology demand higher spatial resolution and thus, higher amplitude sensitivity in Atomic Force Microscopy (AFM). In this work, active cantilevers with integrated sensor and actuator systems are parametrically excited using a novel, analog feedback circuit. With that it is possible to adapt the strength and sign of a cubic nonlinearity which provides a bound to the amplitudes in resonance operation. The system response shows steeper resonance curves and therefore higher amplitude sensitivities compared to forced excited cantilevers. Theoretical findings are validated experimentally.
Hamiltonicity of graphs perturbed by a random geometric graph. - In: Journal of graph theory, ISSN 1097-0118, Bd. 103 (2023), 1, S. 12-22
We study Hamiltonicity in graphs obtained as the union of a deterministic n-vertex graph H with linear degrees and a d-dimensional random geometric graph G d (n, r) for any d ≥ 1. We obtain an asymptotically optimal bound on the minimum r for which a.a.s. H ∪ G d (n, r) is Hamiltonian. Our proof provides a linear time algorithm to find a Hamilton cycle in such graphs.
Relatively bounded perturbations of J-non-negative operators. - In: Complex analysis and operator theory, ISSN 1661-8262, Bd. 17 (2023), 1, 14, insges. 30 S.
We improve known perturbation results for self-adjoint operators in Hilbert spaces and prove spectral enclosures for diagonally dominant J-self-adjoint operator matrices. These are used in the proof of the central result, a perturbation theorem for J-non-negative operators. The results are applied to singular indefinite Sturm-Liouville operators with Lp-potentials. Known bounds on the non-real eigenvalues of such operators are improved.