Showing papers by "Leibniz University of Hanover published in 2022"
TL;DR: This work uses phononic thin plate systems for robust energy harvesting application relying on zero-dimensional cavities confined by the Kekule distorted topological vortices and shows that the proposed energy harvesting system is highly robust against symmetry-preserving defects, and is less influenced even for symmetry-breaking defects at moderate perturbation level.
36 citations
TL;DR: Following a taxonomy development approach, 22 empirically and conceptually grounded design dimensions contingent on chatbots’ temporal profiles are compiled, and three time-dependent chatbot design archetypes are abstracted: Ad-hoc Supporters, Temporary Assistants, and Persistent Companions.
36 citations
TL;DR: In this article, the thermal expansion of several carbon-based nanosheets on the basis of machine-learning interatomic potentials (MLIPs) is explored, where passively trained MLIPs over inexpensive AIMD trajectories enable the examination of complex nanomembranes over wide range of temperatures.
23 citations
TL;DR: In this article, the authors present a brief survey on some of the latest developments in the area of reliability-based design optimization of structural systems under stochastic excitation, which can be grouped into three main categories, namely, sequential optimization approaches, search based techniques, and schemes based on augmented reliability spaces.
21 citations
TL;DR: The proposed methodology to fulfil the challenging expectation in stochastic model updating to calibrate the probabilistic distributions of parameters without any assumption about the distribution formats is developed by employing staircase random variables and the Bhattacharyya distance.
20 citations
TL;DR: In this article , state-of-the-art models based on the machine-learning interatomic potentials (MLIPs) are employed to explore the mechanical/failure and heat transport properties of various BC2N monolayers under ambient conditions.
20 citations
TL;DR: In this article, an efficient multilevel Monte Carlo (MLMC) method for the topology optimization of flexoelectric structures is presented, where GA based integer-valued optimization is used to obtain the optimal topological design.
Abstract: We present an efficient multilevel Monte Carlo (MLMC) method for the topology optimization of flexoelectric structures. A flexoelectric composite consisting of flexoelectric and purely elastic building blocks is investigated. The governing equations are solved by Non-Uniform Rational B-spline (NURBS)-based isogeometric analysis (IGA) exploiting its higher order continuity. Genetic algorithms (GA) based integer-valued optimization is used to obtain the optimal topological design. The uncertainties in the material properties and the volume fraction of the constituents are considered to quantify the uncertainty in the electromechanical coupling effect. Then, a multilevel hierarchy of computational meshes is obtained by a uniform refinement according to a geometric sequence. We estimate the growth rate of the simulation cost, in addition to the rates of decay in the expectation and the variance of the differences between the approximations over the hierarchy. Finally, we determine the minimum number of simulations required on each level to achieve the desired accuracy at different prescribed error tolerances. The results show that the proposed method reduces the computational cost in the numerical experiments without loss of the accuracy. The overall computation saving was in the range 2.0–3.5.
20 citations
TL;DR: In this paper , the thermal expansion of several carbon-based nanosheets on the basis of machine-learning interatomic potentials (MLIPs) is explored, where passively trained MLIPs over inexpensive AIMD trajectories enable the examination of complex nanomembranes over wide range of temperatures.
18 citations
TL;DR: In this paper, two different inverse design schemes were proposed to study phononic crystal beams using reinforcement learning algorithm to effectively and inversely design the structural parameters to maximize the bandgap width and employ the tandem-architecture neural network to solve the training-difficulty problem caused by inconsistent data.
Abstract: The development of phononic crystals, especially their interaction with topological insulators, allows exploration of the anomalous properties of acoustic/elastic waves for various applications. However, rapidly and inversely exploring the geometry of specific targets remains a major challenge. In this work, we show how machine learning can address this challenge by studying phononic crystal beams using two different inverse design schemes. We first develop the theory of phononic beams using the transfer matrix method. Then, we use the reinforcement learning algorithm to effectively and inversely design the structural parameters to maximize the bandgap width. Furthermore, we employ the tandem-architecture neural network to solve the training-difficulty problem caused by inconsistent data and complete the task of inverse structure design with the targeted topological properties. The two inverse-design schemes have different adaptabilities, and both are characterized by high efficiency and stability. This work provides deep insights into the combination of machine learning, topological property, and phononic crystals and offers a reliable platform for rapidly and inversely designing complex material and structure properties.
16 citations
TL;DR: In this paper , the authors used UPLC-MS to assess metabolites heterogeneity among four major Cinnamomum species, including true cinnamon and less explored species (C. tamala).
Abstract: The present study aimed to assess metabolites heterogeneity among four major Cinnamomum species, including true cinnamon (Cinnamomum verum) and less explored species (C. cassia, C. iners, and C. tamala). UPLC-MS led to the annotation of 74 secondary metabolites belonging to different classes, including phenolic acids, tannins, flavonoids, and lignans. A new proanthocyanidin was identified for the first time in C. tamala, along with several glycosylated flavonoid and dicarboxylic fatty acids reported for the first time in cinnamon. Multivariate data analyses revealed, for cinnamates, an abundance in C. verum versus procyandins, dihydro-coumaroylglycosides, and coumarin in C. cassia. A total of 51 primary metabolites were detected using GC-MS analysis encompassing different classes, viz. sugars, fatty acids, and sugar alcohols, with true cinnamon from Malaysia suggested as a good sugar source for diabetic patients. Glycerol in C. tamala, erythritol in C. iners, and glucose and fructose in C. verum from Malaysia were major metabolites contributing to the discrimination among species.
16 citations
TL;DR: In this article, a novel HOF-30-based Mixed-Matrix Membrane (MMM) was proposed by blending HOF30 crystals with PolyImide (PI) for hydrogen separation.
25 Mar 2022
TL;DR: In this paper , the authors investigate the wake properties and the power output of very large potential wind farms in the German Bight for different turbine spacings, stabilities and boundary layer heights.
Abstract: Abstract. Germany's expansion target for offshore wind power capacity of 40 GW by the year 2040 can only be reached if large portions of the Exclusive Economic Zone in the German Bight are equipped with wind farms. Because these wind farm clusters will be much larger than existing wind farms, it is unknown how they will affect the boundary layer flow and how much power they will produce. The objective of this large-eddy simulation study is to investigate the wake properties and the power output of very large potential wind farms in the German Bight for different turbine spacings, stabilities and boundary layer heights. The results show that very large wind farms cause flow effects that small wind farms do not. These effects include, but are not limited to, inversion layer displacement, counterclockwise flow deflection inside the boundary layer and clockwise flow deflection above the boundary layer. Wakes of very large wind farms are longer for shallower boundary layers and smaller turbine spacings, reaching values of more than 100 km. The wake in terms of turbulence intensity is approximately 20 km long, in which longer wakes occur for convective boundary layers and shorter wakes for stable boundary layers. Very large wind farms in a shallow, stable boundary layer can excite gravity waves in the overlying free atmosphere, resulting in significant flow blockage. The power output of very large wind farms is higher for thicker boundary layers because thick boundary layers contain more kinetic energy than thin boundary layers. The power density of the energy input by the geostrophic pressure gradient limits the power output of very large wind farms. Because this power density is very low (approximately 2 W m−2), the installed power density of very large wind farms should be small to achieve a good wind farm efficiency.
TL;DR: In this article, the authors used two different greases bearing experiments to gain an understanding of the mechanism of wear initiation and found that starvation seems to be a major contribution to wear appearing in the investigated operating conditions (2°-45° osc. angle, 0,2-5 Hz frequency).
TL;DR: In this article, the authors proposed an automated approach to train the computer on a predefined classification scheme (taxonomy), which will be called the virtual human factors classifier, which should support human experts to analyse accident reports for organisational, technological, and individual factors that may trigger human errors.
TL;DR: A novel methodology for dealing with missing data using intervals comprising the lowest and highest possible probability values is proposed, which allows to keep track of the associated uncertainty on the available data.
01 Jan 2022-Journal of Materials Chemistry C. Materials for Optical, Magnetic and Electronic Devices
TL;DR: Using first-principles calculations mechanical, thermal transport, electronic and photocatalytic properties of penta-PdPS, pdPSe and pdPTe monolayers are explored as discussed by the authors .
Abstract: Using first-principles calculations mechanical, thermal transport, electronic and photocatalytic properties of penta-PdPS, -PdPSe and -PdPTe monolayers are explored.
TL;DR: In this paper , a nanocrystalline thiophosphate Li10GeP2S12 (LGPS) was synthesized by high-energy ball-milling and probed the Li+ ion transport parameters.
Abstract: Solids with extraordinarily high Li+ dynamics are key for high performance all-solid-state batteries. The thiophosphate Li10GeP2S12 (LGPS) belongs to the best Li-ion conductors with an ionic conductivity exceeding 10 mS cm-1 at ambient temperature. Recent molecular dynamics simulations performed by Dawson and Islam predict that the ionic conductivity of LGPS can be further enhanced by a factor of 3 if local disorder is introduced. As yet, no experimental evidence exists supporting this fascinating prediction. Here, we synthesized nanocrystalline LGPS by high-energy ball-milling and probed the Li+ ion transport parameters. Broadband conductivity spectroscopy in combination with electric modulus measurements allowed us to precisely follow the changes in Li+ dynamics. Surprisingly and against the behavior of other electrolytes, bulk ionic conductivity turned out to decrease with increasing milling time, finally leading to a reduction of σ20°C by a factor of 10. 31P, 6Li NMR, and X-ray diffraction showed that ball-milling forms a structurally heterogeneous sample with nm-sized LGPS crystallites and amorphous material. At -135 °C, electrical relaxation in the amorphous regions is by 2 to 3 orders of magnitude slower. Careful separation of the amorphous and (nano)crystalline contributions to overall ion transport revealed that in both regions, Li+ ion dynamics is slowed down compared to untreated LGPS. Hence, introducing defects into the LGPS bulk structure via ball-milling has a negative impact on ionic transport. We postulate that such a kind of structural disorder is detrimental to fast ion transport in materials whose transport properties rely on crystallographically well-defined diffusion pathways.
TL;DR: A methodology based on compressive sampling is developed for incomplete wind time-histories reconstruction and extrapolation in a single spatial dimension, as well as for related stochastic field statistics estimation and low rank matrices and nuclear norm minimization are developed.
TL;DR: In this paper, the authors triangulate logfiles of a large German online video platform for educational videos with behavioral data from a laboratory study and the objective characteristics of the selected videos to understand the potential motives for why participants pause educational videos while watching such videos online.
Abstract: With the recent surge in digitalization across all levels of education, online video platforms gained educational relevance. Therefore, optimizing such platforms in line with learners’ actual needs should be considered a priority for scientists and educators alike. In this project, we triangulate logfiles of a large German online video platform for educational videos with behavioral data from a laboratory study and the objective characteristics of the selected videos. We aim to understand the potential motives for why participants pause educational videos while watching such videos online. Our analyses revealed that perceived difficulties in comprehension and meaningful structural breakpoints in the videos were associated with increased pausing behavior. In contrast, pausing behavior was not associated with the videos’ formal structural features highlighted in the video platform. Implications of these findings and the potentials of our methodological approach for theory and practice are discussed.
TL;DR: In this article, multivariate procedures using Partial Least Squares Regression and Random Forest Regression were applied to quantify relationships between soil heavy metal concentration (Cr, Cu, Ni, Zn) and reflectance data of highly contaminated Technosols from a former sewage farm near Berlin, Germany.
TL;DR: The Py-Fmas project as mentioned in this paper provides an open-source Python package for the accurate simulation of the $z$-propagation dynamics of ultrashort optical pulses in nonlinear waveguides.
TL;DR: In this article, the authors conducted a full factorial incubation experiment using soil samples from a grassland site in the Tibetan Plateau, where a freeze-thaw cycle was imposed to these soils by continuously changing temperature, from −5 to 10°C.
Abstract: In alpine environments, the decomposition rate of soil organic carbon (SOC) is controlled by several biotic and abiotic factors, which mostly change simultaneously and often lead to freezing and thawing cycles. However, it is highly uncertain whether the temperature sensitivity of decomposition around the freezing point of water is similar as in higher temperature ranges. In this study, we conducted a full factorial incubation experiment using soil samples from a grassland site in the Tibetan Plateau. A manipulative freeze-thaw cycle was imposed to these soils by continuously changing temperature, from −5 to 10 °C. Additional treatments included 4 levels of soil moisture at 15, 30, 60 and 90% of water-filled pore space (WFPS), and two levels of O2 concentration at 0 and 20%. We fitted the Arrhenius equation into the flux data to estimate the activation energy (Ea) and base flux rate (A) for each treatment level. Then, we predicted the dependence and sensitivity of decomposition rate (k) by implementing the Dual Arrhenius and Michaelis-Menten (DAMM) model using a Bayesian optimization approach. While soil temperature had the strongest control on SOC decomposition rate at all soil moisture and O2 levels, its intrinsic temperature sensitivity (Δk/ΔT) remained nearly constant across the entire temperature range except around 0 °C. We found that Ea was higher in nearly dry or anoxic conditions, suggesting that in these extremes more energy is required for microbial activity to take place. These intrinsic sensitivities revealed that temperature (energy) is the main factor that limits decomposition in cold environments provided that moisture and oxygen are sufficiently available. Intrinsic sensitivities with respect to soil moisture and oxygen concentration were only relevant at very narrow ranges, when soils were almost dry or partially anoxic, and small changes within these narrow ranges may lead to very strong changes in decomposition rates.
TL;DR: In this article , a stylized model of urban luminosity and empirical evidence suggest that these "top lights" can be characterized by a Pareto distribution or similarly heavy-tailed distributions.
TL;DR: This paper proposes a novel entropy-based metric by utilizing the Jensen–Shannon divergence, an effective and efficient tool in solving inverse problems in presence of mixed uncertainty such as encountered in the context of imprecise probabilities.
TL;DR: ALPACA as discussed by the authors is a simulation environment for simulating hyperbolic and (incompletely) parabolic conservation laws with multiple distinct and immiscible phases, which can be expressed by a sharp-interface level-set method with conservative interface interaction.
TL;DR: In this article , a novel eco-friendly material (CS-U@PS) for persulfate slow-release to effectively degrade organic pollutants (methyl orange and pyrene) was synthesized using chitosan and urea as the encapsulated framework materials via an emulsion cross-linking method.
01 Feb 2022
TL;DR: In this paper , a high-resolution land use classification using both landscape metrics and spatial data within an urbanized region of San José, Costa Rica, at different scales (watershed, neighbourhood, object) is presented, which demonstrates the potential of green infrastructure for increasing recreational green space access, runoff reduction, and flood retentions while supporting biodiversity, validating its utility in guiding decision-making and policy generation.
Abstract: Green Infrastructure (GI) connects different types of green features via various scales, thereby supporting urban biodiversity and service provision. This study presents a methodology capable of identifying multiple functions to assess GI in less-developed countries, where such methodologies are lacking. GI was assessed based on a high-resolution land use classification using both landscape metrics and spatial data within an urbanized region of San José, Costa Rica, at different scales (watershed, neighbourhood, object). Results showed highly fragmented green spaces (often <10 ha), typically unable to support high levels of biodiversity, along with a low amount of green space per inhabitant (<7.4 m²) within the watershed. Substantially higher tree cover (x6) and tree density (x5) were found in the greenest neighbourhood in comparison to the least green neighbourhood. Potential areas for new GI in the form of green roofs (4.03 ha), permeable pavement (27.3), and potential retention areas (85.3) were determined. Several green spaces (n = 11) were identified as promising GI sites with the potential to increase provision (18.6 m²/inhabitant). The adopted methodology demonstrates the potential of GI for increasing recreational green space access, runoff reduction, and flood retentions while supporting biodiversity, validating its utility in guiding decision-making and policy generation.
TL;DR: In this paper , the effect of elevated cathode pressures and increased compression of the membrane electrode assembly on hydrogen crossover and the cell performance was analyzed using thin Nafion 212 membranes and current densities up to 3.6 A cm −2 .
Abstract: Hydrogen crossover poses a crucial issue for polymer electrolyte membrane (PEM) water electrolysers in terms of safe operation and efficiency losses, especially at increased hydrogen pressures. Besides the impact of external operating conditions, the structural properties of the materials also influence the mass transport within the cell. In this study, we provide an analysis of the effect of elevated cathode pressures (up to 15 bar) in addition to increased compression of the membrane electrode assembly on hydrogen crossover and the cell performance, using thin Nafion 212 membranes and current densities up to 3.6 A cm −2 . It is shown that a higher compression leads to increased mass transport overpotentials, although the overall cell performance is improved due to the decreased ohmic losses. The mass transport limitations also become visible in enhanced anodic hydrogen contents with increasing compression at high current densities. Moreover, increases in cathode pressure are amplifying the compression effect on hydrogen crossover and mass transport losses. The results indicate that the cell voltage should not be the only criterion for optimizing the system design, but that the material design has to be considered for the reduction of hydrogen crossover in PEM water electrolysis.
TL;DR: The proposed two principle lemmas also apply to the calculation of interaction effect indices, and the performance of the development is demonstrated by an illustrative numerical example and three engineering benchmarks with finite element models.
TL;DR: In this paper , the authors examined the impact of the COVID-19 pandemic on agricultural households' livelihoods by drawing on high-frequency phone surveys from eight African countries and a literature review.
Abstract: The COVID-19 pandemic has profound impacts on agricultural households. We discuss how these impacts might affect the underlying drivers of land-use decisions. First, we conceptually extend models of (smallholder) land-use decision-making to assess how the pandemic affects the underlying drivers of land-use decisions. We then examine effects on agricultural households’ livelihoods, by drawing on high-frequency phone surveys from eight African countries and a literature review. We find that the COVID-19 pandemic affects these households’ livelihoods substantially, reflected for instance, by reductions in various income sources. We further find that households’ coping capabilities are weakened, meaning vulnerable households have difficulties to cope with the impacts of the pandemic. Agriculture is likely to become even more important in the years to come for households with very limited resources. Accordingly, we expect more labour-intensive uses of agricultural land. However, context matters and thus impacts on land-use are likely to be very variable.