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High‐Rate FA‐Based Co‐Evaporated Perovskites: Understanding Rate Limitations and Practical Considerations to Overcome Their Impact
No full textVapor phase deposition methods are readily able to achieve uniform coverage of large-area substrates and are widely considered promising for industrial-scale perovskite solar cell fabrication. However, as perovskite-silicon tandem solar cells approach commercialization, practical considerations of manufacturing throughput come into play. Here, it is shown that the inherent sublimation characteristics of the organic precursor formamidinium iodide (FAI) make increasing the deposition rate of FA-based co-evaporated perovskites negatively impact replicability and lead to a substantial decrease in power conversion efficiency (PCE). These losses are linked to reduced film homogeneity and the emergence of carbon-rich regions within the perovskite layer. To mitigate these rate-induced effects, two approaches are explored: source layout optimization and material preconditioning. Utilizing dual FAI sources rather than a single FAI source reduces the relative PCE drop from ≈23%rel to ≈9%rel at a deposition rate of ≈18 nm min−1 (14.8% PCE @ maximum power point (MPP)) compared to the baseline rate of 5 nm min−1 (16.2% PCE @MPP). Alternatively, preconditioning a single FAI source reduces the performance losses from ≈31%rel to ≈26%rel at a deposition rate of ≈21 nm min−1. These findings underscore the importance of tailored source strategies to enable high-rate FA-based co-evaporated perovskites without compromising device performance
Grey-box modelling for tool wear prediction in milling: Fusion of finite element insights, time-resolved cutting signals and metaheuristic feature selection
No full textReliable prediction of tool wear is essential for ensuring productivity, quality, and cost-efficiency in modern machining operations. This study presents a hybrid Grey-Box machine learning framework that combines White-Box finite element simulation outputs (interface temperature, relative sliding velocity), process parameters (feed speed, cutting velocity, depth of cut), dynamic time-series features from cutting force measurements, with a Black-Box machine learning model to predict tool wear in high-speed milling. The experimental campaign involved TiN-coated and uncoated carbide tools under dry machining conditions, with flank and rake wear measured after each cutting pass. Finite element simulations were conducted to extract localized thermomechanical features, such as interface temperature and relative sliding velocity, which were used as physically meaningful inputs. A two-step feature selection method—based on analysis of variance (ANOVA) and the whale optimization algorithm (WOA)—was employed to identify the most relevant input features. Among the machine learning models tested, the gradient boosting regressor (GBR) showed the highest accuracy, achieving coefficient of determination (R2) scores of 0.953 for rake wear and 0.920 for flank wear. Omitting White-Box features resulted in significantly lower performance, confirming their critical role. The model\u27s predictions were also in line with the unseen test cases. These results highlight the effectiveness of combining simulation-informed features with empirical data for tool condition monitoring, offering a scalable and interpretable approach for predictive maintenance in smart manufacturing
Effects of Triaxial Sample Scaling on the Mechanical Behavior of Alluvial Gravels
No full textIn practical engineering, mechanical characterization of coarse gravels is usually performed on small-scale specimens, where the maximum particle size is limited to fit the material in standard testing devices. This implies altering the original particle size distribution, which is known to influence the stress–strain behavior. However, most research on grading effects has been done after comprehensive testing on sands, and the impact of small-scaling on gravelly soils is still poorly understood. This paper presents an experimental study on the shearing response of coarse soil at different specimen scales. The main objective is to assess the impact of small-scaling methods on the stress–strain behavior of coarse alluvial soils. Scalping grading and truncation techniques were used to prepare scaled specimens. Several tests were performed using triaxial cells with specimens of 100, 150, and 800 mm diameter. The analyses indicate that volumetric dilatancy strongly decreases with specimen size, while the secant strain modulus and peak shear strength slightly increase in larger specimens. Differences are mainly related to particle size distributions and packing properties. The article discusses practical insights into the effects of small-scaling variations and their implications for characterizing gravelly materials
A cooperative covering model for optimizing critical supply networks under disruption risks
No full textIn disaster preparedness, strategically placing relief supplies is crucial to guarantee time-ly and adequate relief efforts. Important decisions in this process include determiningoptimal warehouse locations, assessing logistical resources, and strategically allocatingcritical supplies to distribution points. The inherent uncertainty surrounding a potentialdisaster amplifies the complexity of these decisions. We formulate a scenario-based multi-objective optimization model that integrates the advanced placement and allocation ofrelief supplies, extending the general form of a cooperative covering location problem.The proposed model maximizes demand coverage while minimizing underlying storageand logistics costs. Furthermore, the model accounts for diverse disruption scenarios us-ing stochastic programming, treating the disaster impact of individual factors as randomvariables. Given large-scale disaster situations, our model evaluates the effect of potentialdisruptions, enabling the assessment of optimal network solutions. Based on an empiricalcase study focusing on the German national food stockpiling system, we demonstrate thefeasibility of the introduced methodology in developing efficient stockpiling and prepared-ness strategies while facilitating the identification of vulnerabilities to enhance overallresilience. Our results show that incorporating stochastic factors, such as warehouse avail-ability and operability, route failure, coverage radius limitations, and demand volatility,can significantly impact the optimal network configuration and influence demand coverageand total costs. Furthermore, the methodology proves to be both scalable and feasiblewhen applied to a large-scale scenario
Wie nehmen Arbeitnehmende die Digitale Transformation und ihre Auswirkungen wahr? Validierung eines Messinstruments auf Basis der Theory of the Smart Machine
No full textDie Digitale Transformation (DT) verändert Organisationen und die Arbeitswelten von Mitarbeitenden fundamental und in praktisch allen Branchen. Zusammenfassend deuten aktuelle Studien darauf hin, dass die DT und ihre Auswirkungen einen erheblichen Einfluss auf die Wahrnehmungen der Arbeitnehmenden haben. Es fehlt jedoch ein umfassendes, detailliertes Verständnis der Faktoren, die bei der Untersuchung von DT und ihren wahrgenommenen Auswirkungen auf individueller Ebene zu berücksichtigen sind. Eine Theorie, die in der Vergangenheit explizit zur Erklärung der DT entwickelt wurde, ist die Theory of the Smart Machine (TSM). Mit der TSM können die Nutzung fortschrittlicher Informationstechnologie (IT) und digitaler Werkzeuge, die daraus resultierende DT und ihre Auswirkungen auf Organisationen und ihre Mitglieder beschrieben und begründet werden. Da bisher nur eine erste Operationalisierung und ein Vorschlag für ein Messinstrument für die TSM existieren, wird die TSM durch die Validierung eines Messinstruments für die Forschung nutzbar gemacht. Es wird über die Evaluierung eines mehrstufigen Messinstruments berichtet, mit dem Schlüsselkonzepte des TSM-Modells insbesondere mit Bezug zu den allgemeinen Bewegungsdynamiken Verfügbarmachung und Verselbstständigung getestet werden können. Dafür wurden 479 Arbeitnehmende aus verschiedenen Branchen befragt, die einschlägige Erfahrungen mit einem digitalen Transformationsprojekt gesammelt haben. Die vorliegende Arbeit leistet dabei drei wichtige Beiträge zur Forschung und Praxis. Erstens wird erstmals ein Instrument zur Messung von Schlüsselkonzepten der TSM validiert. Damit wird die Grundlage für die Untersuchung weiterer Konzepte und Beziehungen im Gesamtmodell gelegt und damit entscheidend die Validierung des gesamten TSM-Modells unterstützt. Zweitens hilft das validierte Messinstrument, verschiedene Wissensbereiche um die DT zu strukturieren, sodass weitere Theorien entwickelt werden können, sowohl auf der Mikroebene (bezogen auf Effekte der DT auf Arbeitnehmende) als auch auf der Mesoebene (bezogen auf Effekte der DT auf Organisationen). Drittens werden Erkenntnisse über relevante Faktoren der DT gewonnen, die von Forschenden und politischen Entscheidungstragenden bei der Untersuchung der DT und ihrer Auswirkungen berücksichtigt werden sollten
Investigation of the structure and dynamics of amorphous calcium carbonate by NMR: stabilization by poly-aspartate and comparison to monohydrocalcite
No full textDense amorphous phases are key intermediates in biomineralization pathways. Structural information is required to understand these pathways, but is, as per the amorphous nature, difficult to obtain. We report an investigation of amorphous calcium carbonate (ACC) with magic-angle spinning (MAS) nuclear magnetic resonance (NMR) spectroscopy. Mimicking the use of acidic proteins, we stabilize ACC against crystallization with poly-aspartate (PAsp). Spectra are in agreement with incorporation of PAsp into ACC nanoparticles and show that it forms an α-helix. The pH of the solution, from which PAsp-stabilized ACC is synthesized, affects the 13C chemical shift of carbonate in a way that is identical for additive-free ACC. Generally, we observe that the magnetic properties of the 1H and 13C nuclei in the rigid environment of ACC are similar (though not identical) to those in monohydrocalcite (MHC). This allows us to establish, based on 1H–13C correlation spectra, relaxation properties, and spin dynamics simulations, that the structural water molecules in ACC undergo 180° flips on a millisecond time scale
Symmetry-Aware Bayesian Flow Networks for Crystal Generation
No full textThe discovery of new crystalline materials is essential to scientific and technological progress. However, traditional trial-and-error approaches are inefficient due to the vast search space. Recent advancements in machine learning have enabled generative models to predict new stable materials by incorporating structural symmetries and to condition the generation on desired properties. In this work, we introduce SymmBFN, a novel symmetry-aware Bayesian Flow Network (BFN) for crystalline material generation that accurately reproduces the distribution of space groups found in experimentally observed crystals. SymmBFN substantially improves efficiency, generating stable structures at least 50 times faster than the next-best method. Furthermore, we demonstrate its capability for property-conditioned generation, enabling the design of materials with tailored properties. Our findings establish BFNs as an effective tool for accelerating the discovery of crystalline materials
Learning Boltzmann Generators via Constrained Mass Transport
No full textEfficient sampling from high-dimensional and multimodal unnormalized probability distributions is a central challenge in many areas of science and machine learning. We focus on Boltzmann generators (BGs) that aim to sample the Boltzmann distribution of physical systems, such as molecules, at a given temperature. Classical variational approaches that minimize the reverse Kullback-Leibler divergence are prone to mode collapse, while annealing-based methods, commonly using geometric schedules, can suffer from mass teleportation and rely heavily on schedule tuning. We introduce Constrained Mass Transport (CMT), a variational framework that generates intermediate distributions under constraints on both the KL divergence and the entropy decay between successive steps. These constraints enhance distributional overlap, mitigate mass teleportation, and counteract premature convergence. Across standard BG benchmarks and the here introduced ELIL tetrapeptide, the largest system studied to date without access to samples from molecular dynamics, CMT consistently surpasses state-of-the-art variational methods, achieving more than 2.5x higher effective sample size while avoiding mode collapse
Magma differentiation and rare metal mineralization of the Qongjiagang area, Himalayan orogen: evidence from trace element and boron isotope compositions of tourmaline
No full textStrongly fractionated granitic-pegmatitic rocks of the Qongjiagang area, Himalayan orogen are associated with economically important rare metal (e.g., Li, Nb, Be, and Ta) mineralization. Tourmaline is a frequent mineral in both the granites and pegmatites, which makes it an ideal candidate for investigating magmatic to hydrothermal processes that led to the rare metal enrichment. However, a systematic investigation of the response patterns of tourmaline compositions to differentiation processes is still lacking. In this study, we present petrographic and characteristics of the granitic-pegmatitic rock suites, as well as the chemical and boron isotopic compositions of tourmaline. Tourmalines in all samples exhibit schorl characteristics. Those from part of the muscovite granites exhibit pronounced zoning with highest MgO contents and delta B-11 ratios being recorded by their cores (delta B-11: -10.0 similar to -7.74 parts per thousand) whereas the rims trend to lower delta B-11 (-11.8 similar to -10.5 parts per thousand). The delta B-11 ratios of the tourmaline rims are close to those of unzoned tourmalines of the other muscovite granite samples (-12.5 similar to -12.1 parts per thousand). Tourmalines of tourmaline granite (-14.5 similar to -13.9 parts per thousand) and barren pegmatite (-14.0 similar to -12.9 parts per thousand) exhibit comparably low delta B-11 ratios. The ones in beryl pegmatites are characterized by the lowest delta B-11 ratios (-15.7 similar to -14.3 parts per thousand) but at the same time high concentrations of B2O5 and enrichment in Pb, Nb, Ta, and the light rare earth elements. Notably, tourmalines of spodumene pegmatites show variable delta B-11 ratios (-14.6 similar to -10.3 parts per thousand) and at the same time high values of Al2O3, B2O5, Li, Be, Sn, Cr, La, Ce, Pb, and Zn, yet low SiO2. The textural position, as well as element and B isotope composition of tourmaline suggest a magmatic origin of all tourmalines, and record the magma differentiation from early muscovite granite to the subsequently formed pegmatites, with tourmaline of the spodumene pegmatite being subsequently altered by hydrothermal fluids. The variable composition of the tourmalines records independent enrichment of different rare metals during differentiation. Following this, tourmaline is a useful tool to reconstruct rare metal enrichment and mineralization processes controlled by granite magma differentiation and during interaction with externally derived fluids at the magmatic-hydrothermal transition
Positive integrands from Feynman integrals in the Minkowski regime
No full textWe present a method for rewriting dimensionally regulated Feynman parameter integrals in the Minkowski regime as a sum of real, positive integrands multiplied by complex prefactors. This representation eliminates the need for contour deformation, allowing for direct numerical or analytic evaluation of the integrals. We develop an algorithm to construct such representations for a broad class of integrals and demonstrate its generalisation through selected examples. Our approach is applied to integrals up to three loops, including cases with internal masses and off-shell external legs. The resulting expressions are suitable for evaluation using existing techniques, such as sector decomposition, where we observe performance gains of up to four orders of magnitude in certain cases