Search Results (6,149)

Solid-state batteries (SSBs) present a promising advancement in energy storage technology, with the potential to achieve higher energy densities and enhanced safety compared to conventional lithium-ion batteries. However, their commercialisation is hindered by technical limitations and fragmented research efforts that predominantly focus on materials or individual performance parameters. This narrow scope limits SSB design and optimisation, potentially delaying the transition to commercial cells. Addressing these challenges requires a systematic framework that integrates key design and performance considerations. This study introduces a modelling framework that addresses these challenges by offering a systematic approach to SSB design. The model streamlines the design process by enabling users to define material selections and cell configurations while calculating key performance indicators (KPIs), such as energy density, power density, and resistance, as well as the specifications required for cell manufacturing. A material compatibility validation feature ensures appropriate selection of anode, cathode, and electrolyte materials, while an integrated sensitivity analysis (SA) function identifies critical design parameters for performance optimisation. The model’s accuracy and applicability were validated through comparisons with experimental data, established design frameworks, and the reverse-engineering of commercial SSB prototypes. Results show that the model predicts energy densities within a ±4% deviation in most cases. Additionally, the application of SA highlights its effectiveness in refining design parameters and optimising cell configurations. Despite certain limitations, the model remains a valuable tool in the early stages of battery concept development. It offers researchers and industry professionals a practical means to assess the feasibility of SSB designs and support future scale-up and industrialisation efforts. Full article

As a candidate material for turbine blades in aerospace engines, Nb-Si-based alloys have attracted significant research attention due to their high melting point and low density. However, their poor high-temperature oxidation resistance limits practical applications. Different alloying elements, including Ti, Mo, and Hf, were added to Nb-Si-based alloys to study the microstructural evolution of alloys. Additionally, the oxidation behavior and the oxidation kinetics of different alloys, as well as the morphology and microstructure of oxide scale and interior alloys at 1523 K from 1 h to 20 h were analyzed systematically. The current findings indicated that the Mo element is more conducive to promoting the formation of high-temperature precipitates of β-Nb₅Si₃ than the Ti and Hf elements. Inversely, the Ti element tends to cause the transition from high-temperature-phase β-Nb₅Si₃ to low-temperature-phase α-Nb₅Si₃, while the Hf element improves the appearance of the γ-Nb₅Si₃ phase but inhibits the other phases and refines the primary Nbss effectively. Noteworthily, compared with the oxidation weight gain of different alloys, Nb-16Si-20Ti-5Mo-3Hf-2Al-2Cr alloy has excellent high-temperature oxidation resistance, in which the oxidation products are TiNb₂O₇, Nb₂O₅, SiO₂, TiO₂, and HfO₂. It can be determined that in the oxidation process, the Ti element will preferentially form an oxide film of TiO₂, thereby wrapping around the matrix phases, protecting the matrix, and improving the antioxidant capacity, while the Hf element can form an infinite solid solution with the matrix and consume the small number of oxygen atoms entering the matrix, so as to achieve the effect of improving the oxidation resistance. Full article

Regarding the discretization issue in the process of a simultaneous approach for dynamic optimization problems, a configuration strategy based on the density function has been proposed for the finite element distribution of dynamic optimization problems. By utilizing the error at the non-collocation points, a bilevel problem has been constructed and solved, and the number and distribution of the finite elements have been evaluated. For the inner problem in the bilevel problem, a new smoothing function has been introduced to improve the solution accuracy of the inner problem. The grid density function has been constructed using the error at the non-collocation points on each finite element. Finally, the grid density function has been used to update the positions of the finite element endpoints, with the aim of reallocating the finite elements. Finally, two case studies have been provided to specifically demonstrate the role of the proposed method in reducing the optimization problem scale with required solution accuracy. Full article

Porcine reproductive and respiratory syndrome virus (PRRSV) continues to be a major threat to the global swine industry, causing significant economic losses. To address this, we developed a scalable recombinant adeno-associated virus (rAAV)-based strategy for the delivery of soluble viral receptors (SVRs) to treat and potentially eliminate PRRSV infections. This strategy involves fusing the virus-binding domains of two key cellular receptors, sialoadhesin (Sn4D) and CD163 (SRCR5-9), with an Fc fragment. We then used an insect cell–baculovirus expression vector system to produce the rAAV-SRCR59-Fc/Sn4D-Fc vector. Through a series of optimizations, we determined the best conditions for rAAV production, including a baculovirus co-infection ratio of 0.5:1.0, an initial insect cell density of 2.0 × 10⁶ cells/mL, a fetal bovine serum concentration of 2%, and a culture temperature of 30 °C. Under these optimized conditions, we achieved a high titer of rAAV-SRCR59-Fc/Sn4D-Fc in a 2 L bioreactor, reaching 5.4 ± 0.9 × 10⁹ infectious viral particles (IVPs)/mL. Notably, in vitro neutralization assays using a Transwell co-culture system demonstrated a 4.3 log reduction in viral titers across genetically diverse PRRSV-2 strains, including VR2332, JXA1, JS07, and SH1705. Collectively, this study provides a robust platform for large-scale rAAV production and highlights the potential of SVR-based gene therapy to address the antigenic diversity of PRRSV-2. Full article

Flash floods, typically triggered by natural events such as heavy rainfall, snowmelt, and dam failures, are characterized by abrupt onset, destructive power, unpredictability, and challenges in mitigation. This study investigates the spatial distribution patterns and driving mechanisms of rainstorm-induced flash flood disasters in the Yarlung Tsangpo River Basin (YTRB) by integrating topography, hydrometeorology, human activity data, and historical disaster records. Through a multi-method spatial analysis framework—including kernel density estimation, standard deviation ellipse, spatial autocorrelation (Moran’s I and Getis–Ord Gi*), and the optimal parameter geographic detector (OPGD) model (integrating univariate analysis and interaction detection)—we reveal multiscale disaster dynamics across county, township, and small catchment levels. Key findings indicate that finer spatial resolution (e.g., small catchment scale) enhances precision when identifying high-risk zones. Temporally, the number of rainstorm-induced flash floods increased significantly and disaster-affected areas expanded significantly from the 1980s to the 2010s, with a peak spatial dispersion observed during 2010–2019, reflecting a westward shift in disaster distribution. Spatial aggregation of flash floods persisted throughout the study period, concentrated in the central basin. Village density (TD) was identified as the predominant human activity factor, exhibiting nonlinear amplification through interactions with short-duration heavy rainfall (particularly 3 h [P3] and 6 h [P6] maximum precipitations) and GDP. These precipitation durations demonstrated compounding risk effects, where sustained rainfall intensity progressively heightened disaster potential. Topographic and ecological interactions, particularly between elevation (DEM) and vegetation type (VT), further modulate disaster intensity. These findings provide critical insights for risk zonation and targeted prevention strategies in high-altitude river basins. Full article

Micro Light Emitting Diode (Micro-LED) technology, characterized by exceptional brightness, low power consumption, fast response, and long lifespan, holds significant potential for next-generation displays, yet its commercialization hinges on resolving challenges in high-density interconnect fabrication, particularly micrometer-scale bump formation. Traditional fabrication approaches such as evaporation enable precise bump control but face scalability and cost limitations, while electroplating offers lower costs and higher throughput but suffers from substrate conductivity requirements and uneven current density distributions that compromise bump-height uniformity. Emerging alternatives include electroless plating, which achieves uniform metal deposition on non-conductive substrates through autocatalytic reactions albeit with slower deposition rates; ball mounting and dip soldering, which streamline processes via automated solder jetting or alloy immersion but struggle with bump miniaturization and low yield; and photosensitive conductive polymers that simplify fabrication via photolithography-patterned composites but lack validated long-term stability. Persistent challenges in achieving micrometer-scale uniformity, thermomechanical stability, and environmental compatibility underscore the need for integrated hybrid processes, eco-friendly manufacturing protocols, and novel material innovations to enable ultra-high-resolution and flexible Micro-LED implementations. This review systematically compares conventional and emerging methodologies, identifies critical technological bottlenecks, and proposes strategic guidelines for industrial-scale production of high-density Micro-LED displays. Full article

This study presents a comprehensive framework for evaluating urban heat resilience, incorporating urban climatology models, their characteristics, and simulation programs. Utilizing the local climate zone (LCZ) classification method, this research explores how urban geomorphology influences the thermal characteristics of the area. This study integrates spatial data at different “levels of detail” (LOD), from the meso- to building scales, emphasizing the significance of detailed LOD 3 models acquired through 3D laser scanning. The results demonstrate the ability of these models to identify urban heat islands (UHIs) and to simulate urban planning scenarios, such as increasing green spaces and optimizing building density, to mitigate the UHI effect. The ST3D 3D model of the city of Split, represented using an LOD 2 object model, is utilized for meso- and local-scale analyses, while LOD 3 models derived from laser scanning provided in-depth insights at the building scale. The case studies included the Faculty of Civil Engineering, Architecture, and Geodesy building on the University of Split campus and the old town hall in the densely built city center. This framework highlights the advantages of integrating GIS and BIM technology with urban climate analyses, offering tools for data-driven decision-making and fostering sustainable, climate-resilient urban planning. Full article

Urban parks, a type of urban green space, help mitigate environmental pollution and climate change by absorbing and storing atmospheric carbon. Optimizing their carbon-sink capacity requires thoughtful plant community design considering multiple factors. This study analyzed South Korean urban parks using QGIS and i-Tree Eco, integrating satellite imagery with field surveys at both spatial and tree scales. Park spaces were classified into six types based on the biotope criteria established in this study. Random forest regression was applied to each type to identify key variables influencing annual carbon sequestration and storage. The relationship between maturity and sequestration was examined for ten dominant tree species, offering insights for plant selection. Higher tree coverage and more deciduous species were linked to efficiency in carbon sequestration and storage. While variable importance varied slightly across biotope types, tree density was most influential for sequestration, and diameter at breast height and age were key for storage. These findings provide integrated insights into short-term sequestration and long-term storage, as well as strategic directions for structuring plant communities in urban ecosystems. The study offers empirical evidence for designing carbon-efficient urban parks, contributing to sustainable landscape strategies. Full article

The globose scale (GS) Sphaerolecanium prunastri (Boyer de Fonscolombe) (Hemiptera: Coccidae) is a serious pest affecting plants within the Rosaceae, notably wild apricot, Armeniaca vulgaris (Lamarck). Following its initial detection in 2019, more than 80% of valleys with wild apricots have become affected in the Ili River Basin of the Tianshan Mountains in Xinjiang, China. This study assessed GS population dynamics post invasion and its effects on the growth and reproductive traits of wild apricot trees from 2019 to 2024. Nymph densities have decreased but remain high, with densities per 20 cm of shoots of 986 (1^st-instar nymphs) and 120 (2^nd-instar nymphs) in 2024, respectively. Damage has declined, with high damage rankings decreasing from 24% to 11% of wild apricot trees. However, the mortality of trees was higher (25%) in infested than non-infested areas (13%). Interestingly, GS feeding stimulated the growth of spring shoots but significantly reduced the reproductive capacity of wild apricots. Heavily infested trees exhibited increased shoot length (2–3 times), decreased fruit yield (20-fold), lower flowering percentage (8-fold), and reduced flower bud density (2-fold) compared to non-infested trees. Overall, despite a decrease in damage severity, wild apricot forests remain threatened by GS. Implementing integrated pest management (IPM) strategies is essential for effective GS management and the recovery of wild apricot forests. Full article

Forest soil respiration plays a crucial role in the global carbon cycle. However, accurately estimating regional soil carbon fluxes is challenging due to the spatial heterogeneity of soil respiration at the stand level. This study examines the spatial variation of soil respiration and its driving factors in subtropical coniferous and broad-leaved mixed forests in southern China, aiming to provide insights into accurately estimating regional carbon fluxes. The findings reveal that the coefficient of variation (CV) of soil respiration at a scale of 50 m × 50 m is 18.82%, indicating a moderate degree of spatial variation. Furthermore, 52% of the spatial variation in soil respiration can be explained by the variables under investigation. The standardized total effects of the main influencing factors are as follows: soil organic carbon (0.71), diameter at breast height within a radius of 5 m (0.31), soil temperature (0.27), and soil bulk density (−0.25). These results imply that even in relatively homogeneous areas with flat terrain, fine-scale soil respiration exhibits significant spatial heterogeneity. The spatial distribution of woody plant resources predominantly regulates this variation, with root distribution, shading effects, and changes in soil physical and chemical properties being the main influencing mechanisms. The study emphasizes the importance of simulations at different microscales to unravel the potential mechanisms governing macroscopic phenomena. Additionally, it highlights the need for incorporating a more comprehensive range of variables to provide more meaningful references for regional soil carbon flux assessment. Full article

This study examines abrupt changes in system dynamics, focusing on a Hassell-type density-dependent model with an Allee effect. It aims to analyze tipping points leading to extinction and bistability, including chaotic dynamics. Key methods include computing the topological entropy and Lyapunov exponents when varying the carrying capacity, the intrinsic growth rate and the initial conditions, providing a detailed characterization of chaotic regimes. Meanwhile, we derive an inverse square-root scaling law near a saddle-node bifurcation using a complex analysis. This study uniquely integrates chaos theory, a bifurcation analysis and scaling laws into a density-dependent ecological model with an Allee effect, revealing how chaotic regimes, bistability and an analytically derived inverse square-root scaling law near extinction shape the tipping point dynamics and critical transitions in ecological systems. Full article

With the enhancement of environmental protection awareness and the implementation of related regulations, lead-free soldering materials are gradually replacing the traditional leaded soldering materials in the field of electronics manufacturing. Sn–Ag soldering materials have become a research hotspot because of their good mechanical properties, solderability, and thermal fatigue reliability, but their high cost limits their large-scale application. The low silver content of the Sn–Ag solder reduces the cost while maintaining an excellent performance. However, as the size of electronic components shrinks and the package density increases, the solder joint spacing decreases, the potential gradient increases, and electrochemical migration (ECM) becomes a key factor affecting the reliability of solder joints. In this study, the ECM failure process was simulated by the water droplet method, and the SEM and XPS analyses were utilized to investigate the ECM mechanism of Sn1.0Ag solder alloys, and the effects of different concentrations of NaCl solutions on their ECM were investigated. The results showed that the ECM of the Sn1.0Ag solder occurred in a 0.01 M NaCl solution, the dendritic composition was pure Sn, and the white precipitate was a mixture of Sn(OH)₂ and Sn(OH)₄. With the increase in the NaCl concentration, the corrosion resistance of the Sn1.0Ag solder alloy decreases and the ECM reaction intensifies, but with a high concentration of the NaCl solution, a large amount of precipitation hinders the migration of Sn ions, resulting in the generation of no dendrites. The present study provides new insights into the ECM behavior of a low-silver-content Sn–Ag solder system. Full article

Climate change is increasing the frequency and severity of disturbances, calling for extensive salvage logging operations. This study examines fully mechanized cut-to-length operations in the northeastern Italian Alps as a response to windthrow and bark beetle outbreaks following Storm Vaia. Using high-resolution orthophotos, logging trail extent, density, and configuration were analyzed in relation to terrain and ecological sensitivity. A total of 29 forest sites, covering a worksite area of 1078 hectares, were analyzed, with a combined trail length exceeding 700 km. Results indicate an average logging trail density of 500 m/ha, and a machine-trafficked area percentage of 22%. Terrain analysis revealed that 68% of the worksite area was below a 30% slope, facilitating machinery operations, while 32% of the site required adaptive strategies for steeper terrain. Additionally, depth-to-water maps were implemented to assess sensitive zones according to different moisture conditions, revealing that one-fifth of the trafficked zones were at higher risk of soil disturbances due to potentially high moisture levels. This study provides critical baseline data on mechanized salvage logging effects at a large scale, offering insights for future data-driven decision making for efficient planning under sustainable forest management. Full article

The Lower Tagus Valley (LTV) region has the highest population density in Portugal, with over 3.7 million people living in the region. It has been struck in the past by several historical earthquakes, which caused significant economic and human losses. For a proper seismic hazard evaluation, the area needs detailed V_s30 and soil classification maps. Previously available maps are based on proxies, or an insufficient number of velocity measurements followed by coarse geological generalizations. The focus of this work is to significantly improve the available maps. For this purpose, more than 90 new S-wave seismic velocities measurements obtained from seismic refraction and seismic noise measurements, doubling the number used in previously available maps, are used to update available V_s30 and soil classification maps. The data points are also generalized to the available geological maps using local lithostratigraphic studies and, for the first time, satellite images of this area. The results indicate that lithological and thickness changes within each geological formation prevent a simple generalization of geophysical data interpretation based solely on geological mapping. The maps presented here are the first attempt to produce maps at a scale larger than 1:1,000,000 in Portugal, with direct shear wave velocity measurements. A tentative approach to produce more detailed maps using machine learning was also carried out, presenting promising results. This approach may be used in the future to reduce the number of shear wave measurements necessary to produce detailed maps at a finer scale. Full article

In recent decades, the interferometric synthetic aperture radar (InSAR) technique has emerged as a powerful tool for monitoring ground subsidence and geohazards. Various satellite SAR systems with different modes, such as Sentinel-1 and Lutan-1, have produced abundant SAR datasets with wide coverage and large historical archives, which have significantly influenced long-term deformation monitoring applications. However, large-scale InSAR data have posed significant challenges to conventional InSAR methods. These issues include the computational burden and storage of multi-temporal InSAR (MT-InSAR) methods, as well as temporal decorrelation for coherent scatterers with long temporal baselines. In this study, we propose a stepwise MT-InSAR with a temporal coherent scatterer method to address these problems. First, a batch sequential method is introduced in the algorithm by grouping the SAR dataset in the time domain based on the average coherence distribution and then applying permanent scatterer interferometry to each temporal subset. Second, a multi-layer network is employed to estimate deformation for partially coherent scatterers using small baseline subset interferograms, with permanent scatterer deformation parameters as the reference. Finally, the final deformation rate and displacement time series were obtained by incorporating all the temporal subsets. The proposed method efficiently generates high-density InSAR deformation measurements for long-time series analysis. The proposed method was validated using 9 years of Sentinel-1 data with 229 SAR images from Jakarta, Indonesia. The deformation results were compared with those of conventional methods and global navigation satellite system data to confirm the effectiveness of the proposed method. Full article