Paderborn University

The natural band alignments between indium phosphide and the main dioxides of titanium, i.e. rutile, anatase, and brookite as well as amorphous titania are calculated from the branch-point energies of the respective materials. Irrespective of the titania polymorph considered, type-I band alignment is predicted. This may change, however, in dependence on the microscopic interface structure: supercell calculations for amorphous titania grown on P-rich InP(001) surfaces result in a titania conduction band that nearly aligns with that of InP. Depending on the interface specifics, both type-I band and type-II band alignments are observed in the simulations. This agrees with recent experimental findings.

The rising demand for sustainable IoT has promoted the adoption of battery-free devices intermittently powered by ambient energy for sensing. However, the intermittency poses significant challenges in sensing data collection. Despite recent efforts to enable one-to-one communication, routing data across multiple intermittently-powered battery-free devices, a crucial requirement for a sensing system, remains a formidable challenge. This paper fills this gap by introducing Swift, which enables seamless data routing in intermittently-powered battery-free sensing systems. Swift overcomes the challenges posed by device intermittency and heterogeneous energy conditions through three major innovative designs. First, Swift incorporates a reliable node synchronization protocol backed by number theory, ensuring successful synchronization regardless of energy conditions. Second, Swift adopts a low-latency message forwarding protocol, allowing continuous message forwarding without repeated synchronization. Finally, Swift features a simple yet effective mechanism for routing path construction, enabling nodes to obtain the optimal path to the sink node with minimum hops. We implement Swift and perform large-scale experiments representing diverse realworld scenarios. The results demonstrate that Swift achieves an order of magnitude reduction in end-to-end message delivery time compared with the state-of-the-art approaches for intermittentlypowered battery-free sensing systems.

Rolling processes of conventional cast Al-Li alloys quickly reach their limits due to relatively poor material formability. This can be overcome by using twin-roll casting to produce thin sheets. Further thermomechanical treatment, including hot or cold rolling, and heat treatment can adjust the mechanical properties of twin-roll cast Al-Li sheets. The whole manufacturing chain requires detailed knowledge of the precipitation and dissolution behavior during heating, soaking and cooling, to purposefully select any process parameters. This study shows the process chain of a twin-roll cast Al–Cu–Li alloy achieving a hardness of around 180 HV1 by adapting the heat treatment parameters for homogenisation, hot rolling and age hardening. Both hardness and microstructure evolution are visualised along the process chain.

Time series forecasts are always associated with uncertainty. However, experimental studies on the impact of uncertainty communication provide inconclusive results on the effect of providing this uncertainty to end users. In this study, we examine the impact of uncertainty visualizations on advice utilization in the context of time series forecasts with line charts. Based on a literature review, we identified probabilistic framing versus frequency framing as a theoretical foundation for studying the topic. We then used the Judge Advisor System (JAS) as a framework to create an experimental design with two treatments (95% prediction interval [PI] and ensemble plots), one control group (point plot), and various mediating variables (e.g., confidence, graph literacy, and domain knowledge). The results of an online experiment () indicate a U‐shaped relation between uncertainty visualization and forecasting performance. Additionally, we examine how confidence, advice utilization, and other factors mediate the effect of uncertainty visualizations. This paper highlights the benefits of PI plots for researchers and practitioners engaged in the development of effective uncertainty visualizations for decision‐making processes.

A strategy for multifunctional biosurfaces exploiting multiblock copolymers and the antipolyelectrolyte effect is reported. Combining a polyzwitterionic/antifouling and a polycationic/antibacterial block with a central anchoring block for attachment to titanium oxide surfaces affords surface coatings that exhibit antifouling properties against proteins and allow for surface regeneration by clearing adhering proteins by employing a salt washing step. The surfaces also kill bacteria by contact killing, which is aided by a nonfouling block. The synthesis of block copolymers of 4‐vinyl pyridine (VP), dimethyl 4‐vinylbenzyl phosphonate (DMVBP), and 4‐vinylbenzyltrimethyl ammonium chloride (TMA) is achieved on the multigram scale via RAFT polymerization with good end group retention and narrow dispersities. By polymer analogous reactions, poly(4‐vinyl pyridinium propane sulfonate‐ block ‐4‐vinylbenzyl phosphonic acid‐ block ‐4‐vinylbenzyl trimethylammonium chloride) (P(VSP 64 ‐ b ‐PA 14 ‐ b ‐TMA 64 )) is obtained. The antifouling properties against the model protein pepsin and the salt‐induced surface regeneration are shown in surface plasmon resonance (SPR) experiments, while independently the antibacterial and antifouling properties of coated titanium substrates are successfully tested in preliminary microbiological assays against Staphylococcus aureus ( S. aureus ) and Escherichia coli ( E. coli ). This strategy may contribute to the development of long‐term effective antibacterial implant surface coatings to suppress biomedical device‐associated infections.

Studying and understanding many‐body interactions, particularly electron‐boson interactions, is essential for a deeper elucidation of fundamental physical phenomena and the development of novel material functionalities. Here, this aspect is explored in the weak itinerant ferromagnet LaCo2P2 by means of momentum‐resolved photoelectron spectroscopy (ARPES) and first‐principles calculations. The detailed ARPES patterns enable to unveil bulk and surface bands, spin splittings due to Rashba and exchange interactions, as well as the evolution of bands with temperature, which altogether creates a solid foundation for theoretical studies. The latter has allowed to establish the impact of electron‐boson interactions on the electronic structure, that are reflected in its strong renormalization driven by electron‐magnon interaction and the emergence of distinctive kinks of surface and bulk electron bands due to significant electron‐phonon coupling. Our results highlight the distinct impact of electron‐boson interactions on the electronic structure, particularly on the itinerant d states. Similar electronic states are observed in the isostructural iron pnictides, where electron‐boson interactions play a crucial role in the emergence of superconductivity. It is believed that further studies of material systems involving both magnetically active d‐ and f‐sublattices will reveal more advanced phenomena in the bulk and at distinct surfaces, driven by a combination of factors including Rashba and Kondo effects, exchange magnetism, and electron‐boson interactions.

Metal–Organic frameworks (MOFs) are promising candidates for advanced photocatalytically active materials. These porous crystalline compounds have large active surface areas and structural tunability and are thus highly competitive with oxides, the well‐established material class for photocatalysis. However, due to their complex organic and coordination chemistry composition, photophysical mechanisms involved in the photocatalytic processes in MOFs are still not well understood. Employing electron paramagnetic resonance (EPR) spectroscopy and time‐resolved photoluminescence spectroscopy (trPL), the fundamental processes of electron and hole generation are investigated, as well as capture events that lead to the formation of various radical species in UiO‐66, an archetypical MOF photocatalyst. A manifold of photoinduced electron spin centers is detected, which is subsequently analyzed and identified with the help of density‐functional theory (DFT) calculations. Under UV illumination, the symmetry, g‐tensors, and lifetimes of three distinct contributions are revealed: a surface O2‐radical, a light‐induced electron‐hole pair, and a triplet exciton. Notably, the latter is found to emit (delayed) fluorescence. The findings provide new insights into the photoinduced charge transfer processes, which are the basis of photocatalytic activity in UiO‐66. This sets the stage for further studies on photogenerated spin centers in this and similar MOF materials.

Ein Mindestziel von Schulsport ist der Erwerb von motorischen Basiskompetenzen, um Schulkindern die Teilhabe an der Sport- und Bewegungskultur zu ermöglichen (Herrmann et al., 2016). Im Sinne einer Didaktik zum Anfassen werden in diesem Beitrag die Förderkonzepte von zwei schulischen Interventionsstudien vorgestellt, die auf den Erwerb von motorischen Basiskompetenzen abzielen.

In this study, we develop a novel multi-fidelity deep learning approach that transforms low-fidelity solution maps into high-fidelity ones by incorporating parametric space information into an autoencoder architecture. This method’s integration of parametric space information significantly reduces the amount of training data needed to effectively predict high-fidelity solutions from low-fidelity ones. In this study, we examine a two-dimensional steady-state heat transfer analysis within a heterogeneous materials microstructure. The heat conductivity coefficients for two different materials are condensed from a 101 $\times$ 101 grid to smaller grids. We then solve the boundary value problem on the coarsest grid using a pre-trained physics-informed neural operator network known as Finite Operator Learning (FOL). The resulting low-fidelity solution is subsequently upscaled back to a 101 $\times$ 101 grid using a newly designed enhanced autoencoder. The novelty of the developed enhanced autoencoder lies in the concatenation of heat conductivity maps of different resolutions to the decoder segment in distinct steps. Hence the developed algorithm is named microstructure-embedded autoencoder (MEA). We compare the MEA outcomes with those from finite element methods, the standard U-Net, and an interpolation approach as an upscaling technique. Our analysis shows that MEA outperforms these methods in terms of computational efficiency and error on representative test cases. As a result, the MEA serves as a potential supplement to neural operator networks, effectively upscaling low-fidelity solutions to high-fidelity while preserving critical details often lost in traditional upscaling methods, such as sharp interfaces features lost in the context of interpolation approaches.

Plasma glucose spikes affect cardiac autonomic modulation resulting in a decrease of heart rate variability (HRV). We hypothesize that a later chronotype or a higher morning plasma melatonin level is associated with larger decreases of HRV following an early high glycaemic index (GI) breakfast. In persons with an early (n = 21) or a late (n = 15) chronotype who consumed a high GI breakfast at 7 a.m. glucose data were continuously monitored. Time domain HRV parameters were calculated from blood volume pulses derived by wireless wrist worn multisensor. HRV changes (values after minus values before breakfast) were associated with chronotype by multivariable linear regression adjusted for age, sex and baseline levels. Morning plasma melatonin levels were determined from samples drawn on the run-in day. Time domain parameters indicate a higher HRV before high GI breakfast in both chronotypes. A later chronotype tended to be associated with smaller decreases of mean interbeat intervals (p = 0.08) only; no associations were seen with morning melatonin levels. This exploratory analysis in a small sample provides a first indication that in young healthy adults later chronotype might be associated with reduced ANS activation following a high GI breakfast. Future studies should elucidate whether this indicates parasympathetic or sympathetic inhibition.

Lithium niobate (LNO) and lithium tantalate (LTO) see widespread use in fundamental research and commercial technologies reaching from electronics over classical optics to integrated quantum communication. The mixed crystal system lithium niobate tantalate (LNT) allows for the dedicate engineering of material properties by combining the advantages of the two parental materials LNO and LTO. Vibrational spectroscopies such as Raman spectroscopy or (Fourier transform) infrared (IR) spectroscopy are vital techniques to provide detailed insight into the material properties, which is central to the analysis and optimization of devices. This work presents a joint experimental–theoretical approach allowing to unambiguously assign the spectral features in the LNT material family through both Raman and IR spectroscopy, as well as providing an in‐depth explanation for the observed scattering efficiencies based on first‐principles calculations. The phononic contribution to the static dielectric tensor is calculated from the experimental and theoretical data using the generalized Lyddane–Sachs–Teller relation and compared with the results of the first‐principles calculations.

Zusammenfassung In diesem Beitrag wird ein Ansatz zur Identifikation eines vollständigen Satzes piezoelektrischer Materialparameter basierend auf der Messung der elektrischen Impedanz anhand eines einzelnen Probekörpers durch Lösung eines inversen Problems vorgestellt. Vorangegangene Arbeiten zeigen, dass die Regularisierung des zur Lösung eingesetzten Optimierungsverfahrens aufgrund der großen Anzahl an zu bestimmenden Materialparametern anspruchsvoll ist. Darauf aufbauend wird vorgestellt, inwiefern datenbasierte Methoden des maschinellen Lernens zu einer robusteren und effizienteren Lösung des inversen Problems beitragen können. Insbesondere eine verbesserte Startwertschätzung für den gradientenbasierten Optimierungsprozess basierend auf einem neuronalen Netz steht im Mittelpunkt dieses Beitrags. Dieses Netz wird mithilfe synthetischer Daten trainiert und approximiert die Inverse eines Simulationsmodells für die elektrische Impedanz bei gegebenen piezoelektrischen Materialparametern. Die synthetischen Trainingsdaten werden generiert, indem ein klassisches Simulationsmodell, das auch für die Lösung des inversen Problems eingesetzt wird, ausreichend oft mit randomisierten Materialparametern ausgewertet wird. Mithilfe des neuronalen Netzes werden Werte für Materialparameter bestimmt, die, basierend auf einer Auswertung der Zielfunktion des inversen Problems, das physikalische Verhalten besser beschreiben als die in vorangegangenen Arbeiten verwendeten, analytisch ermittelten Werte. Dadurch kann die Komplexität und der Rechenaufwand des anschließenden Optimierungsverfahrens signifikant reduziert werden.

This article explores the increasing interest in the role of emotions in the late modern working world. We examine how emotional regimes are characterised and which unintended consequences accompany the set of normative emotions. We illustrate this with the example of the German gaming industry, which is seen as being at the vanguard of the late modern world of work. Our aim is to show that this is a multifaceted but (organisation-)specific emotional regime. We use selected qualitative data material from the DFG project ‘The regime of emotions as a strategy? An analysis of economic subfields – emotions, emotional capital and gender in the late modern working world’ (duration 2020–2024). With reference to Patulny and Olson (2019), we examine the emotions that define this specific emotional regime, which prove to be (1) complex and ambivalent, (2) individualised, (3) commodified and (4) digitally mediated. Another relevant element of the emotional regime is (5) reflexive emotional management. We illustrate that late modern emotional regimes create paradoxes such as the passion for game development and the unquestioned acceptance of overtime, which can lead to exhaustion. We also reveal prevailing gender inequalities, which are often not immediately obvious in the seemingly egalitarian gaming industry.

The application of data analytics to product usage data has the potential to enhance engineering and decision-making in product planning. To achieve this effectively for cyber-physical systems (CPS), it is necessary to possess specialized expertise in technical products, innovation processes, and data analytics. An understanding of the process from domain knowledge to data analysis is of critical importance for the successful completion of projects, even for those without expertise in these areas. In this paper, we set out the foundation for a toolbox for data analytics, which will enable the creation of domain-specific pipelines for product planning. The toolbox includes a morphological box that covers the necessary pipeline components, based on a thorough analysis of literature and practitioner surveys. This comprehensive overview is unique. The toolbox based on it promises to support and enable domain experts and citizen data scientists, enhancing efficiency in product design, speeding up time to market, and shortening innovation cycles.