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Controlled flexibility of zinc benzimidazole/graphene oxide nanoribbon hybrid fillers for high-performance hydrogen separation membrane
No full textTwo-dimensional Zn2(bim)3 MOF nanosheets offer promising channels for hydrogen separation, but ligand gateopening limits selectivity at near-ambient temperatures. Here, graphene oxide nanoribbons (GONR) were incorporated to form Zn2(bim)3/GONR composites with controlled framework flexibility. CO2 adsorption isotherms and Far-IR analysis revealed hindered ligand motion, as the S-shaped isotherm shifted to Type I and new low-frequency peaks appeared. The hybrid composites also showed an increase in the pore volume of sub-10 & Aring; pores, which is beneficial for selective hydrogen transport. Upon incorporation into highly permeable polymers, the hybrid fillers enabled the mixed matrix membranes (MMMs) to achieve a H2/N2 selectivity of 26 (18% higher than pristine Zn2(bim)3/6FDA-DAM) with a H2 permeability of 1226 Barrer for 6FDA-DAM-based MMMs, and a selectivity of 14 (47% higher than pristine Zn2(bim)3/PIM-1) while maintaining a high permeability of 5346 Barrer for PIM-1-based MMMs. Furthermore, the Zn2(bim)3/GONR composite exhibited a filler enhancement index (Findex) of 0.78, positioning it among the most effective 2D MOF fillers for H2/N2 separation. These findings demonstrate that hybridizing MOF nanosheets with GONR is an effective strategy to control framework flexibility and leverage this controlled flexibility to achieve high-performance mixed matrix membranes for hydrogen separation.
Deep learning-based real-time damage assessment of lithium-ion batteries under dynamic impact
No full textLithium-ion batteries are widely used in diverse applications due to their high energy density and long service life. However, minor mechanical impacts during operation often induce hidden internal damage, creating safety and performance risks. If such damage is not addressed promptly, it can lead to fire, explosion, or other severe consequences. This study presents a real-time lithium-ion battery (LIB) damage detection and assessment method based on a deep learning framework that integrates acoustic emission (AE) techniques, convolutional neural networks (CNN), and long short-term memory networks (LSTM). A drop-weight impact test rig is designed to simulate low-velocity mechanical impacts and to collect AE signals across multiple impact energy levels. Signal pre-processing, including Savitzky-Golay smoothing, normalization, and Gaussian noise-based data augmentation, enhances model robustness. The proposed CNN-BiLSTM model achieves an average accuracy of 95 % in classifying four damage levels. Furthermore, electrochemical performance characterization, including internal resistance and capacity decay after cycling, validates the reliability of the AE-based classification results. This approach provides a feasible, non-destructive, and intelligent solution for monitoring the health status of lithium-ion batteries under dynamic mechanical impacts, contributing to improved safety and extended service life.
Defect-driven plasticity in irradiated nanotwinned Cu
No full textIn this study, we demonstrate defect-driven plasticity in proton-irradiated nanotwinned Cu through in-situ nano-tensile testing. Contrary to conventional radiation hardening, our results reveal that radiation-induced defects can facilitate softening and improve ductility. This is achieved by intentionally disturbing interfacial coherency through radiation-induced defects, where local lattice discontinuities on twin boundaries serve as dislocation nucleation sites. Furthermore, it was found that these discontinuities are readily eliminated by partial dislocations gliding along twin boundaries, thereby mitigating radiation-induced hardening. This study advances our understanding of deformation behavior mediated by twin boundaries under irradiation and offers mechanistic insights into defect-driven plasticity in nanotwinned metals.
Serotonin 2C receptors inhibit hypothalamic CRH neurons to suppress appetite
No full textObjectives: The serotonin 2C receptor (Htr2c) is one of the plausible targets for the development of appetite suppressants. Previous studies have demonstrated the complexity of neuronal circuitry underlying the appetitesuppressing effects of Htr2c stimulation. To develop a safe and effective anti-obesity medication targeting Htr2c, we need to better understand how Htr2c agonists suppress appetite. In this study, we focused on the effects of Htr2c agonists on corticotropin-releasing hormone (CRH) neurons to identify the contribution of humoral components to the suppression of fasting-induced food intake. Methods: We used the Crh-ires-cre mice to fluorescently label CRH neurons for whole-cell patch-clamp recordings (Crh-ires-cre::tdTomato mice) and to delete Htr2c selectively in CRH neurons by breeding with Htr2cflox/Y mice (Crh-ires-cre::Htr2cflox/Y mice). We also injected Htr2c-targeting short hairpin RNA (shRNA) into the paraventricular nucleus of the hypothalamus (PVH) of Crh-ires-cre mice to knock down Htr2c selectively in CRH neurons within the PVH (CRHPVH neurons). Using these model mice, we tested the effects of WAY161503, a selective Htr2c agonist, on CRH neuronal activity ex vivo as well as fasting-induced food intake and plasma corticosterone (CORT) levels in vivo. Results: WAY161503 inhibited the activity of CRHPVH neurons. The appetite-suppressing effects of WAY161503 were significantly attenuated when Htr2c was deleted selectively in CRHPVH neurons. On the other hand, WAY161503 promoted the reduction of plasma CORT levels during fasting-induced refeeding via Htr2c expressed by CRHPVH neurons. Importantly, when mice were pretreated with RU486, a glucocorticoid receptor antagonist that blocks CORT action, WAY161503 suppressed food intake whether CRHPVH neurons expressed functional Htr2c or not. Finally, we characterized the expression of single-minded 1 (Sim1) messenger RNA (mRNA), Crh mRNA, and Htr2c mRNA in PVH neurons, which may help to explain the effects of Htr2c stimulation on fastinginduced refeeding. Conclusions: Our results demonstrate that Htr2c expression in the CRHPVH neurons is necessary for the appetitesuppressing effects of WAY161503 during fasting-induced refeeding. Importantly, we found that WAY161503 suppresses the hypothalamic-pituitary-adrenal (HPA) axis and promotes the reduction of plasma CORT levels, thereby enabling the appetite-suppressing effects of Htr2c stimulation during fasting-induced refeeding. To our knowledge, this study is the first to highlight the necessity of coordination between neural and humoral pathways for the suppression of fasting-induced food intake by Htr2c agonists.
Oxidation behavior and passivation mechanism of the T2 phase in Mo-Si-B alloys
No full textThe oxidation behavior of Mo5SiB2 (T2) phase was studied at 400-900 degrees C to examine their passivation mechanism which is critical for oxidation resistance in high temperature Mo-Si-B alloys. The oxide layer formation behavior of T2-rich Mo-Si-B alloy prepared by mechanical alloying was characterized using X-ray diffraction, scanning electron microscopy, and atom probe tomography. The results revealed that no effective passivation layer is formed and continuous B loss occurred before thermal oxidation of Si. Efficient passivation borosilicate layer can be formed above 900 degrees C, which further hinders oxygen penetration into the metal substrate and mass loss even for the temperature high enough for MoO3 volatilization. Si and B content profile in different oxidation temperatures showed that passivation layer formation attributed to the Si diffusion and oxidation towards Bdepleted surface region. It is suggested that T2 phase is crucial for effective oxidation resistance property of Mo-Si-B alloys.
Real-Time Communication Relay Planning With a Low-Complexity Network Quality Prediction Model in Dynamic Indoor Missions
No full textRelay robots are crucial for extending communication when a client robot performs long-range missions. However, existing network quality prediction models and relay planning methods often struggle with real-time operation due to their high computational cost and poor adaptability to frequently changing missions. To address this, we propose a real-time communication relay system featuring two key contributions. First, a low-complexity network quality prediction model using Kalman filter-based Gaussian process regression achieves efficient online inference with constant-time updates (similar to 0.02s). Second, a hierarchical relay planning strategy, employing a Monte Carlo tree search-based sequential planner, generates communication-aware trajectories satisfying network constraints at discrete steps. Real-world experiments validate our system's effectiveness, demonstrating near-continuous network availability (99.1% channel reliability) and boosting the packet delivery ratio from a baseline of 44.7% to 73.7% . Our integrated approach offers a practical and robust solution for dynamic indoor missions.
Photoinitiated CVD antifouling coatings enable long-term stability of flexible multifunctional neural probes for chronic neural recording
No full textFlexible neural probes with integrated recording, optical stimulation, and drug delivery capabilities offer unprecedented access to neural circuit dynamics. However, their long-term utility is compromised by foreign body responses that isolate recording sites from target neurons. This study introduces photoinitiated chemical vapor deposition (piCVD) as a transformative approach to neural interface stability through ultrathin (<100 nm) antifouling coatings. Unlike conventional hydrogel coatings that impair electrical signal transmission, our piCVDapplied poly(2-hydroxyethyl methacrylate-co-ethylene glycol dimethacrylate) coating maintains electrical functionality by preserving low impedance while providing superior anti-fouling properties. In vitro protein adsorption studies demonstrated near-complete resistance to both albumin and fibrinogen compared to uncoated surfaces, with the coating maintaining stability even after 24 h of sonication-durability unachievable with conventional wet-chemistry methods. When evaluated in mouse models over three months, the coated probe maintained high-quality spontaneous neural recordings and optically evoked potentials throughout the study period, with signal-to-noise ratios improving from 18.0 at week 1-20.7 at week 13. This performance significantly correlates with 66.6 % reduction in glial scarring, 84.6 % increase in neuronal preservation compared to uncoated probes. The specific combination of CVD methodology and optimized copolymer composition achieves long-term stability, representing a significant advance over the typical one-month limitation of conventional coatings. These results establish piCVD antifouling coatings as an enabling technology for chronic neural interfaces in both basic neuroscience research and emerging neuroprosthetic applications.
DBSCAN-based particle Gaussian mixture filters
No full textThis study addresses nonlinear and non-Gaussian state estimation problems where the particle filter (PF) exhibits the impoverishment issue. This issue arises from the discretisation of the continuous posterior distribution of the state and the use of importance sampling, where the true distribution of the state is unknown. In this study, we propose density-based spatial clustering of applications with noise (DBSCAN)-based particle Gaussian mixture (PGM) filters: the PGM-DS and PGM-DU filters, where DS indicates the PGM filter with DBSCAN and DU indicates the PGM filter with DBSCAN and the unscented transform (UT). These filters assume the posterior distribution of the state to be a Gaussian mixture model (GMM) and sample particles from this GMM. At every time step, the particles are clustered into multiple Gaussian components using DBSCAN, the components are updated with the Kalman/linear minimum mean squared error (LMMSE) update, and the GMM is reconstructed with the updated means and covariances. The proposed filters are tested in three numerical simulation scenarios and compared with other state-of-the-art nonlinear filters. The results show enhanced performance and robustness across the tested simulation scenarios, with lower computational cost compared to the other filters.
Assessment of seismic ground motion incoherency induced by subsurface topography using dynamic centrifuge tests
No full textSeismic ground motion incoherency due to spatial variability has a significant impact on soil-foundationstructure systems, including vital infrastructures such as nuclear power plants, long-span bridges, and pipelines. For the first time, this study employs dynamic centrifuge tests to investigate ground motion coherency functions affected by subsurface topography. A fleet of earthquakes was excited to the soil models with flat and inclined layering, each, and the coherency functions at the ground surface were compared across different separation distances and frequency bands. The analysis reveals that topography significantly influences seismic coherency, with a greater incoherency observed in the inclined layering case. Additionally, a comparison of coherency at the surface and the deeper depth indicates that the greater surface-to-bedrock depth results in a more pronounced incoherency effect. This study demonstrates the feasibility of using well-controlled physical modelling to explore seismic ground motion incoherency caused by ray-path effect. The results are expected to elucidate how local conditions affect seismic coherency, thereby enhancing infrastructure stability solutions.
CFD analysis for optimization of aerodynamic barriers for severe accident consequence mitigation at a nuclear power plant
No full textOne of the key post-Fukushima developments in nuclear safety is consideration of post-accident consequence mitigation to minimize the radiological consequences of a nuclear power plant severe accident. In our previous study, a conceptual approach based on aerodynamic barriers was successfully examined to confine and control the dispersion of fission products following a containment breach during a severe accident. This approach used a vortex-like air circulation within a defined boundary around the reactor containment with the induced flow directing the released radioactive aerosols toward strategically placed sanction intakes. To support practical implementation of the proposed aerodynamic barriers approach, this study investigated optimal configuration of the aerodynamic barriers using CFD analysis with respect to variations in environmental and accident conditions, and provide robust performance in capturing radioactive aerosols. The CFD analyses were based on coupled Euler-Lagrange method using OpenFOAM and simulated the release and transport of CsI as representative form of fission products under the influence of aerodynamic barriers. The results showed that controlling aerodynamic barrier installation distance and momentum ratio is very important to ensure radioactive aerosols capture. The results indicated that dynamic adjustment of aerodynamic barrier discharge speed and angle is important to handle changes in wind speeds. The results also indicated that successful air flow confinement and radioactive aerosol capture can be achieved with proper control of these key variables (e.g., maintaining the momentum ratio between 1 and 15) while showing minor impact of wind direction variations on the barrier performance.