Search Results (796)

Proton exchange membrane fuel cells (PEMFC) are recognized as promising next-generation energy technology. Yet, their performance is critically limited by inefficient gas transport and water management in conventional flow channels. Current rectangular gas channels (GC) restrict reactive gas penetration into the gas diffusion layer (GDL) due to insufficient longitudinal convection. At the same time, the complex multiphase interactions at the mesoscale pose challenges for numerical modeling. To address these limitations, this study proposes a novel cathode channel design featuring laterally contracted fin-shaped barrier blocks and develops a mesoscopic multiphase coupled transport model using the lattice Boltzmann method combined with the volume-of-fluid approach (LBM-VOF). Through systematic investigation of multiphase flow interactions across channel geometries and GDL surface wettability effects, we demonstrate that the optimized barrier structure induces bidirectional forced convection, enhancing oxygen transport compared to linear channels. Compared with the traditional straight channel, the optimized composite channel achieves a 60.9% increase in average droplet transport velocity and a 56.9% longer droplet displacement distance, while reducing the GDL surface water saturation by 24.8% under the same inlet conditions. These findings provide critical insights into channel structure optimization for high-efficiency PEMFC, offering a validated numerical framework for multiphysics-coupled fuel cell simulations. Full article

Neuromechanobiology has emerged as a multidisciplinary field at the interface of neuroscience and mechanobiology, aiming to elucidate how mechanical forces influence the development, organization, and function of the nervous system. This review offers a comprehensive overview of the historical evolution of the discipline, its molecular and biophysical foundations, and the experimental strategies employed to investigate it. Recent advances have revealed the pivotal roles of substrate stiffness, mechanical signaling, and force transduction in neural stem proliferation, axon guidance, synapse formation, and neural circuit maturation. All these effects originate at the molecular level and extend to the mesoscopic scale. Disrupted mechanotransduction has been increasingly implicated in neurodevelopmental disorders and neurodegenerative diseases, underscoring its clinical relevance. Key unresolved questions and future directions are also highlighted, with emphasis on the need for integrative approaches to decipher the complex interplay between mechanical forces and neural function. Full article

Fatigue strength is vital for engineering applications of aluminum alloys. Accurate models incorporating mesoscopic defect-representing features are one of the issues for accurate fatigue strength prediction. A fatigue life prediction method based on meso-defect-representing features is proposed in this study. Based on staged fatigue damage, meso-defect data was obtained by X-ray CT. After 3D reconstruction and simplification, porosity, shape, and location were selected as the meso-defect-representing features using correlation coefficient analysis. Weights of meso-defect features were determined through FEM simulation. A mesoscopic damage variable incorporating the weights of porosity, shape, and location for meso-defect was defined. Correlation between fatigue life and meso-defect features was established through the mesoscopic damage variable. Experimental verification results showed that the prediction method is an effective method for fatigue life assessment. Full article

As the retrieval of unconventional oil and gas resources extends to the deep and ultra-deep domains, the issue of cement sheath failure in shale oil wellbores seriously endangers wellbore safety, making it imperative to uncover the relevant damage mechanism and develop effective assessment approaches. In response to the limitations of conventional finite element methods in representing mesoscopic damage, in this study, we determined the mesoscopic parameters of cement paste via laboratory calibrations; constructed a 3D casing–cement sheath–formation composite model using the discrete element method; addressed the restriction of the continuum assumption; and numerically simulated the microcrack initiation, propagation, and interface debonding behaviors of cement paste from a mesomechanical viewpoint. The model’s reliability was validated using a full-scale cement sheath sealing integrity assessment apparatus, while the influences of fracturing location, stage count, and internal casing pressure on cement sheath damage were analyzed systematically. Our findings indicate that the DEM model can precisely capture the dynamic evolution features of microcracks under cyclic loading, and the results agree well with the results of the cement sheath sealing integrity evaluation. During the first internal casing pressure loading phase, the microcracks generated account for 84% of the total microcracks formed during the entire loading process. The primary interface (casing–cement sheath interface) is fully debonded after the second internal pressure loading, demonstrating that the initial stage of cyclic internal casing pressure exerts a decisive impact on cement sheath integrity. The cement sheath in the horizontal well section is subjected to high internal casing pressure and high formation stress, resulting in more frequent microcrack coalescence and a rapid rise in the interface debonding rate, whereas the damage progression in the vertical well section is relatively slow. Full article

This work investigates the mechanisms of information transfer underlying causal relationships between brain regions during resting-state conditions in patients with schizophrenia (SCZ). A large fMRI dataset including healthy controls and SCZ patients was analyzed to estimate directed information flow using local Transfer Entropy (TE). Four functional interaction patterns—referred to as rules—were identified between brain regions: activation in the same state (ActS), activation in the opposite state (ActO), turn-off in the same state (TfS), and turn-off in the opposite state (TfO), indicating a dynamics toward converging (Acts/Tfs = S) and diverging (ActO/TfO = O) states of brain regions. These interactions were integrated within a multiplex network framework, in which each rule was represented as a directed network layer. Our results reveal widespread alterations in the functional architecture of SCZ brain networks, particularly affecting schizophrenia-related systems such as bottom-up sensory pathways and associative cortical dynamics. An imbalance between S and O rules was observed, leading to reduced network stability. This shift results in a more randomized functional network organization. These findings provide a mechanistic link between excitation/inhibition (E/I) imbalance and mesoscopic network dysconnectivity, in agreement with previous dynamic functional connectivity and Dynamic Causal Modeling (DCM) studies. Overall, our approach offers an integrated framework for characterizing directed brain communication patterns and psychiatric phenotypes. Future work will focus on systematic comparisons with DCM and other functional connectivity methods. Full article

Scientific inquiry is fundamental to science education, encompassing the processes through which students construct scientific knowledge and develop thinking skills. However, the unfolding of these inquiry processes and their relation to performance remain underexplored. Drawing on process data from a structured simulation-based assessment task, this study investigated the inquiry processes of 259 fourth-grade students. We applied a multi-analytic approach including sequential pattern mining, entropy analysis, and process mining to capture macro- and micro-level behavioral patterns and examine their associations with task performance operationalized by effectiveness and efficiency. Macro-level analyses revealed that effective students generally organized their inquiry processes into more iterative cycles of evidence collection, demonstrating a more dedicated approach before committing to a final response. Micro-level analyses further indicated that effective and efficient students showed better strategic coordination during experimentation. Together, these findings provide a multi-level characterization of elementary students’ scientific inquiry processes and link inquiry patterns to task effectiveness and efficiency. The study also underscores the potential of process data from simulation-based assessments for diagnosing inquiry skills and informing the design of personalized scaffolds in elementary science education. Full article

Alzheimer’s disease (AD) is considered one of the leading causes of death in the United States, and there is no effective cure for it. Understanding the neuropathological mechanisms underlying AD is essential for identifying early, reliable biomarkers and developing effective therapies. In this paper, we report on a comprehensive multimodal study of AD pathology using the 5xFAD mouse model. We employed light-scattering techniques, Partial Wave Spectroscopy (PWS) and Inverse Participation Ratio (IPR), to detect nanoscale structural alterations in brain tissues, nuclear components, and mitochondria. To support the light-scattering experiments, behavior, and histopathological studies were conducted. These analyses revealed significant increases in structural heterogeneity and mass density fluctuations in the brains of 5xFAD mice compared with Non-transgenic controls. Behavioral assessment performed using the Novel Object Recognition test demonstrated memory impairment in 5xFAD mice, reflected by a reduced recognition index. Histopathological analysis further revealed increased amyloid beta plaques and microglia activation in the hippocampus and cortex of 5xFAD mice compared with Non-transgenic controls. An increase in structural disorder within brain tissues can be attributed to higher mass density fluctuations, likely arising from macromolecular rearrangement driven by amyloid beta aggregation and neuroinflammatory responses as the disease progresses. Our findings suggest that PWS and IPR-derived metrics provide sensitive biophysical indicators of early cellular and subcellular disruption, offering potential as quantitative biomarkers for the detection of AD. Full article

A multiscale model is developed to investigate the mechanical behavior and failure of in situ particle reinforced titanium matrix composites (PTMCs). Through the microstructural observation of the heterogeneous microscopic and mesoscopic structures in the in situ TiB/Ti55531 composites, multiscale heterogeneous models coupled to the finite element method are employed to simulate the mechanical behaviors and failures. In the atomic scale, molecular dynamics (MD) simulations are applied to determine the traction-separation (T-S) responses of the cohesive zone model (CZM) describing the Ti/TiB interface. Then, the mesoscale representative volume element (RVE) model with heterogeneous structure, including the Ti55531 matrix, the TiB particles, and their interfaces represented by the parameterized CZM, is established. The volume fraction and distribution morphology of TiB particles result from the microstructural analysis of titanium matrix composites. The simulation results show that the Young’s modulus, tensile strength and elongation of multiscale are in excellent agreement with experimental results. The stress transfer, damage evolution and fracture behavior of the TiB particles in the composites are also analyzed using this multiscale approach. Full article

To investigate the mechanical behavior and energy evolution characteristics of various rock materials under impact loading, dynamic impact tests were conducted on five representative rock types using a split Hopkinson pressure bar (SHPB) apparatus, combined with X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. The dynamic mechanical response, energy characteristics, mineral composition, and associated microstructural features of these typical rocks were systematically analyzed. The results show that basalt exhibits the highest peak strength, followed by blue sandstone and granite; all three display typical brittle failure characteristics, whereas red sandstone and green sandstone demonstrate greater ductility and plastic deformation capacity. By introducing the energy-time density index, the energy-time density of the rocks ranks from strongest to weakest as follows: green sandstone, red sandstone, granite, blue sandstone, and basalt. An innovative dynamic strength–energy-time density mapping model was established to elucidate the clustering and distinguishing characteristics of these rock materials. Assay results and mesoscopic images confirm the relationship between mineral composition and the fine structure of rock fragmentation mechanisms, highlighting that the critical transition from intergranular to transgranular fracture is the key mechanism governing impact pulverization. Furthermore, fractal analysis reveals that higher fractal dimensions are associated with more complex microcrack structures and may correlate with the corresponding energy dissipation intensity. These findings provide profound insight into the failure mechanisms of rocks under dynamic loading, offering significant theoretical value and engineering application prospects, particularly in fields such as mining excavation and rock mass stability assessment. Full article

This paper provides a comprehensive review of the dynamic tensile behavior of concrete, focusing on its implications for seismic-resistant and impact-prone structures such as dams. The present work distinguishes itself in the following ways: providing the first comprehensive synthesis explicitly focused on large-aggregate dam concrete behavior across the seismic strain rate range (${10}^{-4}$ to ${10}^{-2}$ s⁻¹), which is critical yet underrepresented in the existing literature; integrating recent experimental and numerical advances regarding moisture effects, load history, and cyclic loading—factors that are essential for dam safety assessments; and critically evaluating current design guidelines for concrete dams against state-of-the-art research to identify gaps between engineering practice and scientific evidence. Through the extensive synthesis of experimental data, numerical simulations, and existing guidelines, the study examines key factors influencing dynamic tensile strength, including strain rate effects, crack evolution, testing techniques, and material variables such as moisture content, load history, and aggregate size. Experimental results from spall tests, split Hopkinson pressure bar configurations, and cyclic loading protocols are analyzed, revealing dynamic increase factors ranging from 1.1 to over 12, depending on the strain rates, saturation levels, and preloading conditions. The roles of inertial effects, free water (via the Stefan effect), and microstructural heterogeneity in enhancing or diminishing tensile performance are critically evaluated. Numerical models, including finite element, discrete element, and peridynamic approaches, are discussed for their ability to simulate crack propagation, inertia-dominated responses, and moisture interactions. The review identifies and analyzes current design guidelines. Key conclusions emphasize the necessity of integrating moisture content, load history, and mesoscale heterogeneity into dynamic constitutive models, alongside standardized testing protocols to bridge gaps between laboratory data and real-world applications. The findings advocate for updated engineering guidelines that reflect recent advances in rate-dependent fracture mechanics and multi-scale modeling, ensuring safer and more resilient concrete infrastructure under extreme dynamic loads. Full article

The simulation of seismic wave attenuation and dispersion in a fractured medium and the analysis of the influencing factors have an important guiding role for fracture detection and characterization. In this paper, for the fractured medium saturated with fluid, the finite element numerical simulation method of the Lamé–Navier and Navier–Stokes equations is investigated and compared with the numerical simulation method based on Biot’s equation. Biot’s method is more suitable for simulating fractured media at the mesoscopic scale, whereas for microscopic media, the Lamé–Navier and Navier–Stokes equations demonstrate distinct advantages. Meanwhile, the numerical simulation method is employed to analyze the influencing factors of connectivity of symmetrical fractures, effective compression length of seismic waves, and fluid viscosity. This analysis further elucidates the mechanisms and change characteristics of seismic wave attenuation and dispersion, providing theoretical guidance for the detection of fractures and fluids. Full article

Lipid-based nanomedicines are already widely used in antitumor therapy and gene delivery. However, their complex structural features demand advanced mesoscopic structural characterization tools for effective research and development (R&D) and quality control. Synchrotron small-angle X-ray scattering (SAXS) is a powerful, non-invasive technique for probing nanoscale membrane organizations, monitoring in situ dynamic membrane assembly, and exploring the interactions of components in lipid-based drug delivery systems, including liposomes, lipoplexes, lipid nanoparticles (LNPs), and lyotropic liquid crystals (LLCs). Recent advances in high-flux synchrotron facilities, high-frequency detectors, and automated SAXS data processing pipelines permit a detailed structural characterization of lamellarity, bilayer spacing, internal phases, core–shell morphology, as well as “pump-probe” dynamic process studies for lipid nanomedicines. Though major challenges remain in sample polydispersity and model fitting, the advances in time-resolved synchrotron SAXS, high-throughput automation, and artificial intelligence (AI)-assisted modeling are rapidly reducing this barrier. This review summarizes SAXS methodology and introduces representative case studies in the field of lipid nanomedicines. The performance of BioSAXS beamline BL19U2 in the Shanghai synchrotron radiation facility (SSRF) and prospects of AI-guided drug screening at BL19U2 are highlighted to advance intelligent R&D and quality control for lipid nanomedicines. Full article

This paper presents the detailed comparisons of solute macro-segregation pictures predicted by different meso-macroscopic simulations, based on the single-domain enthalpy–porosity approach coupled with distinct models of flow resistance in the two-phase zone. In the first, the whole zone is treated as a Darcy’s porous medium (EP model); in the other two, the columnar and equiaxed grain structures are distinguished using either the coherency point (EP-CP model) approach or by tracking a virtual surface of columnar dendrite tips (EP-FT model). The simplified 2D model of a solidifying cast in a centrifuge is proposed, and calculations are performed for the Pb-48wt. % Sn cast at various hypergravity levels and rotation angles. It is shown, in the example of Sn-10wt. % Pb alloy, that the predicted macro-segregation strongly depends on the mesoscopic model used, and the EP-FT simulation (validated with the AFRODITE benchmark) provides the most realistic solute inhomogeneity pictures. The EP-FT model is further used to investigate the impact of the hyper-gravity level and the cooling direction on the compositional nonuniformity developing in centrifuge casting. The hyper-gravity level visibly impacts the macro-segregation extent. The region of almost uniform solute distribution in the slurry zone rises with the increased effective gravity, though the solute channeling is more severe for higher gravity and rotation angles. A-channeling and V-channeling were observed for angles between the gravity vector and cooling direction lower than 120° and higher than 120°, respectively. Full article

With the advancement of aerospace equipment toward high-speed and heavy-duty applications, conventional forced lubrication systems are facing significant challenges in terms of reliability and adaptability to complex operating conditions. Porous medium materials, owing to their unique self-lubricating and oil-retention capabilities, are regarded as an ideal lubrication solution. However, their seepage behavior is governed by the strong coupling effects of microscopic pore structures and fluid physicochemical properties, the mechanisms of which remain inadequately understood, thereby severely constraining the design and application of high-performance lubricating materials. To address this, this paper systematically reviews recent research progress on seepage behavior in porous media, with the aim of establishing a correlation between microstructural characteristics and macroscopic performance. Starting from the characterization of porous media, this work comprehensively analyzes the structure–seepage relationships in porous polymers, metal foams, and porous ceramics, and constructs a multi-scale theoretical framework encompassing macroscopic continuum theories, mesoscopic lattice Boltzmann methods (LBM), pore network models, and microscopic molecular dynamics. The advantages and limitations of experimental measurements and numerical simulation approaches are also compared. In particular, this study critically highlights the current neglect of key interfacial parameters such as surface wettability and pore roughness, and proposes an in-depth investigation into the seepage mechanisms of polyimide porous cage materials based on LBM. Furthermore, the potential application of emerging research paradigms such as data-driven approaches and intelligent computing in seepage studies is discussed. Finally, it is emphasized that future efforts should focus on developing deeply integrated cross-scale simulation methodologies, strengthening multi-physics coupling and artificial intelligence-assisted research, and advancing the development of intelligent porous lubricating materials with gradient structures or stimulus-responsive characteristics. This is expected to provide a solid theoretical foundation and technical pathway for the rational design and optimization of high-performance lubrication systems. Full article