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Tailored Redox‐Active Catholytes Enabling High‐Rate and High‐Loading All‐Solid‐State Lithium‐Sulfur Batteries
No full textAll-solid-state lithium-sulfur batteries (ASSLSBs) hold great promise for next-generation electrochemical energy storage due to sulfur\u27s high theoretical specific capacity and low cost. However, sluggish sulfur conversion kinetics and severe volume variations during cycling, as well as poor ionic percolation in composite cathodes, limit their practical viability. To overcome these challenges, we herein introduce solid electrolytes of nominal composition LiSiPSI (with x = 0, 0.2, 0.4), possessing high ionic conductivities of ≥ 7 mS cm at room temperature. We show that increasing iodine content alters the phase composition and triggers reversible redox activity in these materials. If implemented as catholytes, this enables very fast sulfur conversion kinetics, ultimately leading to ASSLSBs with exceptional performance. The cells achieve 86% sulfur utilization at a rate of C/2 and at 45°C and offer high-rate capability by delivering 1175 mAh g at 5C and 590 mAh g at 15C. Furthermore, the synergistic effects of ionic percolation and redox-activity enable record areal capacities up to 14 mAh cm with a sulfur loading of 10 mg cm. Taken together, our findings provide new strategies for designing redox-active catholytes for application in advanced ASSLSBs and further strengthen the redox-mediating role of iodine therein
Leveraging molecular descriptors and explainable machine learning for monomer conversion prediction in photoinduced electron transfer-reversible addition-fragmentation chain transfer polymerization
No full textThis study presents a molecular descriptor-based machine learning (ML) architecture for predicting monomer conversion in photoinduced electron transfer-reversible addition-fragmentation chain transfer (PET-RAFT) polymerization systems. Unlike traditional polymer informatics approaches that treat polymers as single units or use one-hot encoding for reaction components, we decompose each PET-RAFT system into its individual parts: monomer, RAFT agent, and photocatalyst. Next, each element was separately encoded using 2D molecular descriptors derived from SMILES. Using a literature-sourced dataset of 152 PET-RAFT systems, we systematically trained (with fivefold cross-validation, CV) and evaluated 10 ML algorithms. CatBoost showed greater stability across CV-folds (SD = ± 0.07) and was identified as the top performer for monomer conversion prediction (R2 = 0.84; RMSE = 10.04 pps; MAE = 8.16 pps). SHapley Additive exPlanations (SHAP) analysis revealed mechanistically interpretable structure–property-performance relationships, highlighting that monomer topological complexity, electronic polarization, and molecular weight together account for over 60% of the model’s predictive power. External validation confirmed CatBoost’s ability to generalize to unseen (meth)acrylates and (meth)acrylamides (MAE = 8.03), with comparable performance to that of the training set. In practice, the learned descriptor-conversion mapping enables fast in silico screening and component ranking, highlighting actionable descriptor ranges and potentially accelerating design-build-test cycles for high-conversion PET-RAFT
Effect of hip bracing on stair walking biomechanics and pain in patients with mild-to-moderate hip osteoarthritis: an intervention study
No full textBackground: Bracing is a conservative treatment method for hip osteoarthritis (HOA) and has shown favourable effects on pain and functional capacity. However, biomechanical analyses of brace effects remain sparse and are limited to level walking. Stair walking is more demanding than level walking in terms of movement coordination and joint loads. This study, therefore, aimed to investigate the effect of hip bracing on pain perception and biomechanics of the hip, pelvis, and trunk during stair walking in individuals with HOA.
Methods: Hip, pelvis, and trunk biomechanics and pain during stair ascent and descent were assessed before and after one week of hip bracing in 20 individuals with unilateral mild-to-moderate HOA. Differences between the bracing conditions were analysed with dependent t-tests, and Pearson\u27s correlations were used to analyse the correlation between brace-induced alterations in pain score and biomechanical parameters.
Results: Bracing increased movement velocity and reduced stair walking pain by 28%. Furthermore, increased hip extension and reduced hip flexion were found with bracing. Bracing led to a decrease in anterior pelvis tilt, resulting in a more upright pelvis position. Trunk motion was not affected by bracing. During stair ascent, frontal pelvis motion increased, while peak hip adduction and internal rotation decreased with bracing. During stair descent, increased hip extension and external rotation moments were found with bracing, while the pelvis and hip transverse range of motion were reduced. Decreased pelvis rise on the ipsilateral side during stair ascent and increased hip transverse range of motion during stair descent were moderately correlated with a decrease in pain.
Conclusions: Bracing can reduce hip pain during stair walking and mitigate some of the effects of HOA on stair walking biomechanics, making it a valuable conservative treatment option for individuals with mild-to-moderate HOA. Limiting hip internal rotation exclusively during periods of high joint loading could be a promising mechanism for reducing pain in individuals with HOA. The observed biomechanical changes are indicative of altered hip abductor muscle activity and increased joint loading. Hence, further analyses are necessary to explore the relationship between hip bracing, muscle activity, joint loading and pain
Opuntia ficus-indica (L.) Mill. Extract: From Chemical Characterization to Inflammatory Profiling and Its Potential Effects in a Zebrafish Model of Spinal Cord Injury—A Morphological and Molecular Study
No full textNatural compounds are increasingly explored for their ability to modulate multiple molecular pathways involved in inflammation and oxidative stress and for their therapeutic potential. Among these, Opuntia ficus-indica (L.) Mill. has attracted growing interest due to its rich phytochemical profile; however, the biological properties of unripe fruits remain largely unexplored. In this study, a hydroalcoholic extract obtained from unripe O. ficus-indica fruits was characterized for its chemical composition, antioxidant capacity, and concentration-dependent embryotoxic profile and subsequently investigated in a zebrafish model of spinal cord injury (SCI). UHPLC-HRMS/MS analysis identified 14 secondary metabolites, mainly flavonoids and phenylpropanoid acids. Antioxidant activity was confirmed by DPPH and ABTS assays. An embryotoxicity assessment conducted according to OECD Test Guideline 236 revealed no mortality at concentrations below 100 µg mL and an LC of 323.59 µg mL at 96 h post-fertilization, allowing the identification of non-toxic concentrations for subsequent in vivo experiments. Based on these results, the extract was tested in a larval zebrafish SCI transection model. Treated larvae showed improved locomotor recovery, particularly under continuous exposure, accompanied by modulation of molecular pathways involved in inflammation, neurotrophic support, and neurogenesis, including reduced pro-inflammatory cytokine expression and increased BDNF and Sonic Hedgehog signaling markers. Overall, these findings expand current knowledge on unripe O. ficus-indica and highlight its potential to modulate molecular pathways involved in SCI-induced damage and repair
Modellierung maschinenseitiger Einflüsse auf den Vereinzelungs- und Stapelbildungsprozess von Batteriezellen
No full textDie Transformation hin zur Elektromobilität stellt die Produktionstechnik vor große Herausforderungen. Batteriezellen müssen in kurzer Zeit in großen Stückzahlen hergestellt werden. Unklare Trends bei Materialien und Batteriezell-Designs erschweren die Entwicklung von Produktionsanlagen und deren Betrieb. Unzureichende Kenntnisse über die Wechselwirkungen zwischen Materialien, Prozessen und Maschinen führen zu hohen Ausschussraten. In diesem Zusammenhang weisen die Prozessschritte der Vereinzelung und Stapelbildung ein hohes Optimierungspotential auf.
Sowohl in der Forschung als auch in der Industrie werden intensive Anstrengungen unternommen, die Batterieproduktionsprozesse mithilfe von Modellen zu beschreiben. Ziel ist es, Wechselwirkungen zu quantifizieren und Optimierungsansätze abzuleiten. Dadurch sollen Prozesse effizienter betrieben und Ausschussraten gesenkt werden. Ein Großteil dieser Arbeiten befasst sich mit der Modellierung materialseitiger Einflüsse auf die relevanten Prozesse. Um jedoch umfassende Aussagen zu Ursache-Wirkungs-Beziehungen treffen zu können, ist auch die Modellierung maschinenseitiger Einflüsse erforderlich.
Diese Arbeit leistet einen Beitrag zur Ergänzung bestehender Modellierungsaktivitäten um die maschinenseitigen Einflüsse auf die Prozesse der Vereinzelung und Stapelbildung. Zu diesem Zweck wird ein methodisches Vorgehen zur Modellentwicklung, -validierung und -anwendung vorgestellt.
Zunächst werden die Prozesse analysiert und die maschinenseitigen Einflüsse auf die entsprechenden Produktfehler aufgezeigt. Darauf folgt eine detaillierte Analyse der Maschinentechnik sowie die Bewertung der jeweiligen Komponenten innerhalb der Anlagen. Auf dieser Grundlage wird festgelegt, welche Komponenten mit hoher Detailtiefe modelliert werden müssen und welche vernachlässigt werden können. Die konkrete Modellierung und Verknüpfung von Teilmodellen erfolgt im Rahmen einer Systemsimulation. Anschließend werden die Teilmodelle parametrisiert. Die Modellgüte wird durch experimentelle Untersuchungen an den Maschinen validiert.
Abschließend werden entsprechende Anwendungsszenarien vorgestellt. Dabei wird das Potenzial der Modellnutzung in den Phasen der Maschinenentwicklung, Inbetriebnahme und des Betriebs aufgezeigt. Bei der Entwicklung neuer Vereinzelungs- und Stapelbildungsmaschinen können Schwachstellen frühzeitig identifiziert und die Einflüsse verschiedener Komponenten auf die Prozessqualität analysiert werden. Im Rahmen der Inbetriebnahme werden die Auswirkungen von Einstellparametern dargestellt. So können optimal angepasste Einstellungen für das jeweils zu verarbeitende Material virtuell ermittelt werden. Im Betrieb werden die Modelle durch Kopplung mit der realen Maschinensteuerung genutzt, wodurch sich neue Möglichkeiten für den Einsatz virtueller Sensoren ergeben.
Diese Arbeit dient als Leitfaden für ähnliche Aktivitäten und soll Maschinenbauern sowie Zellherstellern dabei helfen, eigene Modelle zu entwickeln
Transforming Relational Model Queries to Triple Graph Grammars
No full textViews are an important part of model-driven development processes, as they allow developers to work on abstractions of potentially complex system models. The definition of model-view transformations is, however, difficult, especially if bidirectional synchronization between models and views is required. In this work, we present a relational operator model for specifying queries on heterogeneous models, as well as a transformation of our operators to triple graph grammars. View definition approaches can use our operator model as transformation backend and leverage the inherently bidirectional and incremental transformation operationalization of triple graph grammars. We further present an initial evaluation using a prototype implementation of our operator model and transformation, and discuss possibilities to extend the operator model
Retrieval-Augmented Generation in the Knowledge-Based Design of Complex Mechatronic Drive Systems
No full textPorosity and permeability prediction from petrographic point-counting data using machine learning: Applications to Rotliegendes and Buntsandstein reservoirs
No full textMachine learning approaches are widely used in geosciences. However, one widely available dataset in reservoir geology remains underrepresented in published works: petrographic data from classical point-counting analyses. Such data are widely available for reservoir lithology characterization, often in combination with routine core analysis data (porosity and permeability). Since porosity and permeability in siliciclastic rocks are controlled by the detrital and authigenic composition and samples record effects of compaction during diagenesis, these datasets are often linked to assess reservoir quality controls.
Datasets from six wells, covering four regions and two large reservoir lithologies in central Europe, the Permian Rotliegendes and Triassic Buntsandstein, were used to apply machine learning to the petrographic and reservoir quality data to predict porosity and permeability. Predictions are based on point-counting data including detrital and authigenic phases, optical porosity, grain-to-IGV (GTI) and grain-to-grain (GTG) coating coverages, and granulometry. For both regression tasks, a Random Forest and a Support Vector Regression machine learning model were implemented, with performance compared and the best model selected based on coefficient of determination (R) and error metrics. Porosity predictions using a Random Forest algorithm yielded an R of 0.92, a mean average error (MAE) of 1.25%, and a root mean square error (RMSE) of 1.56%. Permeability predictions of real-scale permeability using Support Vector Regression gave an R of 0.85, MAE of 29.4 mD, RMSE of 68.3 mD, and a range-based normalized RMSE of 8.76% (real-scale). Log-transformation of measured and predicted permeability resulted in a more representative R of 0.83, MAE of 0.21, and RMSE of 0.24, reflecting its log-normal distribution. Predictions are acceptable despite the limited dataset, which reduces operator bias by using curated data. This machine learning approach may simultaneously unlock another understanding of reservoir quality controls based on SHapley Additive exPlanations (SHAP) value plots.
Further training of such models on cored reservoir sections can improve understanding of which detrital and authigenic mineral phases influence reservoir properties. Trained models could also potentially evaluate reservoir properties from cuttings, which, like well logs, are more continuous than cores while allowing diagenetic interpretation based on petrographic analysis
Shaking and pushing skyrmions: Formation of a nonequilibrium phase with zero critical current
No full textIn three-dimensional chiral magnets, skyrmions are line-like objects oriented parallel to the applied magnetic field. The efficient coupling of magnetic skyrmion lattices to spin currents and magnetic fields permits their dynamical manipulation. Here, we explore the dynamics of skyrmion lattices when slowly oscillating the field direction by up to a few degrees on millisecond timescales while simultaneously pushing the skyrmion lattice by electric currents. The field oscillations induce a shaking of the orientation of the skyrmion lines, leading to a phase where the critical depinning current for translational motion vanishes. We measure the transverse susceptibility of MnSi to track various depinning phase transitions induced by currents, oscillating fields, or combinations thereof. An effective slip–stick model for the bending and motion of the skyrmion lines in the presence of disorder explains main features of the experiment and predicts the existence of several dynamical skyrmion lattice phases under shaking and pushing representing phases of matter far from thermal equilibrium
Increasing grassland productivity and reducing environmental N losses – Multiple benefits of advanced cattle slurry separation
No full textReducing the high nitrogen (N) losses during fertilization with cattle slurry is key to reduce environmental impacts of grassland farming. We tested the hitherto unknown potential of separated versus regular unseparated slurry (control) to mitigate total N losses in a three-year experiment using 15N-labelled slurry. Slurry separation was enhanced using starch, clay minerals, and centrifugation, yielding an organic-rich solid fraction and a liquid fraction with low dry-matter content and ca 70 % ammonium-N. The use of separated slurry significantly increased plant productivity (+12 %), plant N uptake (+21 %), and total biomass harvest N export (+20 %) compared to the control. Additionally, fertilizer N retention in topsoil organic N (SON) increased by 8 %. Due to higher plant uptake, and higher soil storage of fertilizer N, total gaseous N losses from separated slurry were lower (33.5 % of added N) than from regular slurry (57.6 %). Leaching of fertilizer N remained negligible in both treatments. However, this did not apply for N2O emissions, which were of low relevance for N balance considerations, but tripled after the addition of the liquid phase of separated slurry in summer. This undesired effect however might be prevented if the solid phase is applied in summer and the liquid phase in spring when soil microbial activity is still low. In summary, separated slurry reduced N losses, increased productivity, fodder quality, and fertilizer N retention, thereby mitigating N deficits and soil N mining. Thus, with appropriate application timing, use of separated slurry can enhance both ecological and economic soil functions and ecosystem services