Search Results (51)

In this paper, a coupled negative bias temperature instability (NBTI)/hot carrier degradation (HCD) failure model is proposed on the 2-D voltage plane for aging simulation of SRAM circuits. According to the physical mechanism of failure, based on the reaction–diffusion and hot carrier energy-driven theory, revised degradation models of threshold voltage shift (∆Vth) for the NBTI and HCD are established, respectively, with explicit expressions for gate voltage (V_G)/drain voltage (V_D). An NBTI/HCD coupling model is built on the 2-D {V_G, V_D} voltage plane with a weighting factor in the form of V_G and V_D power law. The model also takes into account the AC effect and long-term saturation behavior. The predicted ∆Vth under various stress conditions shows an average relative error of 11.6% with experimental data across the entire bias space. SRAM circuit simulation shows that the read static noise margin (RSNM) and write static noise margin (WSNM) have a maximum absolute error of 4.2% and 3.1%, respectively. This research provides a valuable reference for the reliability simulation of nanoscale integrated circuits. Full article

In recent years, hybrid polymer/POSS (Polyhedral Oligomeric Silsesquioxane) systems have attracted particular attention, combining the advantages of organic and inorganic components. This paper reports on the thermal and thermo-oxidative degradation and weathering processes of these materials, as well as their impact on mechanical, chemical, and morphological properties. The paper discusses the physical and chemical changes occurring during degradation, the mechanisms of autoxidation, and the influence of environmental factors such as UV radiation, temperature, and humidity. Particular attention is paid to the role of POSS nanoparticles in polymer stabilization—their barrier function, free radical scavenging, and oxygen diffusion limitation. Methods for analyzing ageing processes are presented, including thermogravimetry coupled with infra-red spectroscopy (TG-FTIR), mechanical property testing, and yellowness index assessment. Material durability prediction models and their importance in designing composite lifespans in the context of the circular economy are also discussed. It is demonstrated that the appropriate type and concentration of POSS (typically 2–6 wt.%) can significantly improve polymer composites’ resistance to heat, radiation, and oxidizing agents, extending their service life and enabling more sustainable lifecycle management of products. Full article

In lithium-ion battery electrodes, repeated lithium insertion and extraction generate compositional gradients and volumetric changes that produce evolving stress fields and eigenstrains, accelerating mechanical degradation. While existing diffusion-induced stress models often capture only elastic behavior, they rarely provide a closed-form analytical treatment of irreversible deformation or its connection to cyclic degradation. In this work, a transparent analytical framework is developed for a planar electrode that explicitly couples lithium diffusion with elastic-plastic deformation, eigenstrain formation, and fracture-aware stress relaxation. The framework provides a means to quantitatively model the evolution of residual stress gradients, revealing the formation of a damaging tensile state at the electrode surface after delithiation and demonstrating how path-dependent irreversible deformation establishes a degradation memory. A parametric study is used to demonstrate the framework’s capability to clarify the influence of diffusivity and yield strength on residual stress development. This framework, which unifies diffusion, plasticity, and fracture in closed-form mechanical relations, provides new physical insight into the origins of chemo-mechanical degradation and offers a computationally efficient tool for guiding the design of durable next-generation electrode materials where chemo-mechanical strains are moderate. Full article

A prediction methodology for the mid-span deflection of fatigue-damaged RC beams subjected to chloride-induced corrosion is proposed, incorporating the coupled effects of fatigue stress levels and localized pitting corrosion in steel reinforcement. The reliability of the methodology is validated through experimental comparisons. The effects of fatigue stress are quantified via two mechanisms: degradation of the concrete elastic modulus and the development of fatigue-induced cracks in the steel reinforcement, which reduces its effective cross-sectional area. Pitting corrosion is simplified as equivalent surface cracks. To determine the chloride concentration within the concrete cover for predicting steel pit depth, a 3D meso-scale model is developed to simulate chloride ingress in fatigue-damaged concrete. The concrete is treated as a three-phase composite composed of coarse aggregate, mortar matrix, and the interfacial transition zone (ITZ), and each phase has its own diffusion coefficient. Based on previous chloride concentration tests, the effect of fatigue loading is considered by the accelerated and depth-dependent diffusion coefficients. Based on the meso-scale simulation results, mid-span deflections of fatigue-damaged RC beams under varying chloride exposure durations are predicted. The findings conclusively demonstrate that, under prolonged chloride erosion, the mechanical stress state remains the predominant factor governing structural deformation, overshadowing time-dependent corrosion effects. Full article

Detecting underwater debris is important for monitoring the marine environment but remains challenging due to poor image quality, visual noise, object occlusions, and diverse debris appearances in underwater scenes. This study proposes UDD-YOLO, a novel detection framework that, for the first time, applies a diffusion-based model to underwater image enhancement, introducing a new paradigm for improving perceptual quality in marine vision tasks. Specifically, the proposed framework integrates three key components: (1) a Cold Diffusion module that acts as a pre-processing stage to restore image clarity and contrast by reversing deterministic degradation such as blur and occlusion—without injecting stochastic noise—making it the first diffusion-based enhancement applied to underwater object detection; (2) an AMC2f feature extraction module that combines multi-scale separable convolutions and learnable normalization to improve representation for targets with complex morphology and scale variation; and (3) a Unified-IoU (UIoU) loss function designed to dynamically balance localization learning between high- and low-quality predictions, thereby reducing errors caused by occlusion or boundary ambiguity. Extensive experiments are conducted on the public underwater plastic pollution detection dataset, which includes 15 categories of underwater debris. The proposed method achieves a mAP50 of 81.8%, with 87.3% precision and 75.1% recall, surpassing eleven advanced detection models such as Faster R-CNN, RT-DETR-L, YOLOv8n, and YOLOv12n. Ablation studies verify the function of every module. These findings show that diffusion-driven enhancement, when coupled with feature extraction and localization optimization, offers a promising direction for accurate, robust underwater perception, opening new opportunities for environmental monitoring and autonomous marine systems. Full article

Unsymmetrical dimethylhydrazine (UDMH), a highly toxic rocket propellant, remains a significant environmental concern in Kazakhstan due to repeated rocket stage falls near the Baikonur Cosmodrome. This study integrates chemical analysis with stochastic contamination transport modeling to evaluate the persistence and migration of UDMH transformation products (TPs) in soils collected 15 years after the rocket crash. Vacuum-assisted headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (Vac-HS-SPME-GC-MS) was used to determine five major TPs. Among these, pyrazine (PAN) and 1-methyl-1H-pyrazole (MPA) were consistently detected at concentrations ranging from 0.04–2.35 ng g⁻¹ and 0.06–3.48 ng g⁻¹, respectively. Stochastic simulations performed with HYDRUS-1D indicated that the long-term persistence of these compounds is mainly controlled by physical nonequilibrium transport processes, including diffusion-limited exchange, weak sorption, and slow inter-domain mass transfer, rather than by degradation. Sensitivity analysis demonstrated that low dispersivity and diffusion coefficients enhance solute retention within immobile domains, maintaining residual levels over extended periods. The results demonstrate the efficacy of combined long-term monitoring and predictive modeling frameworks for assessing contamination dynamics in rocket impact zones. Full article

Understanding the relationship between state-of-charge (SOC) and anisotropic lithium diffusion is essential for improving the durability of Ni-rich layered oxide cathodes. However, quantitative insights into directional lithium diffusivity and its influence on mechanical degradation remain limited. In this study, molecular dynamics (MD) simulations were performed for LiNi_xMn_yCo_zO₂ (NMC) compositions with varying nickel content and SOC levels to reveal composition- and direction-dependent lithium transport behavior. The numerical indices in NMC compositions (e.g., NMC111, NMC532, NMC811) indicate the relative molar ratios of Ni, Mn, and Co, respectively, in LiNi_xMn_yCo_zO₂. The results show that lithium diffusion is enhanced at low SOC, owing to the abundance of vacant sites, while diffusion along the out-of-plane (c-axis) direction is strongly constrained, particularly in Ni-rich systems. To bridge the atomistic and continuum scales, the SOC-dependent anisotropic diffusivities obtained from MD simulations were incorporated into a chemo-mechanical finite-element model of an NMC811 particle. The coupled analysis demonstrates that anisotropic and SOC-dependent diffusion accelerates lithium depletion and stress localization, elucidating the origin of particle cracking in Ni-rich cathodes. This multiscale framework provides quantitative parameters and mechanistic understanding critical for designing durable next-generation lithium-ion batteries. Full article

We develop and analyze a reaction-diffusion model describing the early spatial dynamics of viral infection in tissue, incorporating key components of the innate immune system: inflammatory cytokines and circulating macrophages. The system couples three spatial partial differential equations (for uninfected cells, infected cells, and virus particles) with two ordinary differential equations (for cytokines and activated macrophages), and it includes time delays related to intracellular viral replication. In the absence of macrophage degradation, we derive analytical expressions for the total viral load and the wave speed, and we identify explicit immune control thresholds in terms of the virus replication number and the strength of the immune response. In the presence of macrophage degradation, simulations reveal that increasing macrophage turnover accelerates wave propagation and increases viral burden. These results highlight the critical role of innate immune feedback, modulated by effector degradation, in shaping the spatial outcome of infection. Depending on the values of viral replication number and the strength of the immune response, infection can be immediately suppressed, or it can propagate with gradual extinction due to the time-dependent immune response, or it can persistently propagate in the tissue in the form of a reaction-diffusion wave. Full article

The construction of large-scale infrastructure, such as power facilities, requires extensive use of reinforced concrete. The durability degradation of reinforced concrete structures in chloride environments involves multi-physics coupling effects, chloride ion diffusion, rebar corrosion, and concrete damage. Existing models neglect the coupling mechanisms among these processes and the influence of mesoscale structural characteristics. Therefore, this study proposes a diffusive–mechanical coupled phase field by integrating the phase field, chloride ion diffusion, and mechanical equivalence for rebar corrosion, establishing a multi-physics coupling analysis framework at the mesoscale. The model incorporates heterogeneous meso-structure of concrete and constructs a dynamic coupling function between the phase field damage variable and chloride diffusion coefficient, enabling full-process simulation of corrosion-induced cracking under chloride erosion. Numerical results demonstrate that mesoscale heterogeneity significantly affects crack propagation paths, with increased aggregate content delaying the initiation of rebar corrosion. Moreover, the case with corner-positioned rebar exhibits earlier cracking compared to the case with centrally located rebar. Furthermore, larger clear spacing delays delamination failure. Comparisons with the damage mechanics model and experimental data confirm that the proposed model more accurately captures tortuous crack propagation behavior, especially suitable for evaluating the durability of reinforced concrete components in facilities such as transmission tower foundations, substation structures, and marine power facilities. This research provides a highly accurate numerical tool for predicting the service life of reinforced concrete power infrastructure in chloride environments. Full article

Rapid urbanization threatens water ecosystem services (WESs) in China’s Greater Bay Area. This study employs a Markov-FLUS land-use simulation coupled with the InVEST model to project land-use patterns for 2035 under four scenarios—Natural Development (ND), Farmland Protection (FP), Economic Priority (EP), and Ecological Protection (EcoP)—and evaluates their impacts on water yield, soil retention, and total phosphorus (TP) export. Under ND and FP scenarios, modest gains in water yield (+32.25% and +32.13%) and soil retention (+46.16% and +45.91%) are achieved, but TP control remains limited (−0.05% and +4.82%). In contrast, the EP scenario drives severe declines in water yield (−13.39%) and soil retention (−2.11%) alongside a TP surge (+5.87%), evidencing ecological degradation under high-intensity development. Conversely, the EcoP scenario yields substantial improvements, water yield +50.67%, soil retention +70.94%, and TP export −8.17%, reflecting the synergistic “multiplier effect” of combined woodland and water-body restoration. Spatially, urban cores and agricultural margins exhibit divergent service responses, underscoring the need for differentiated management. We developed a spatial priority map by integrating the predicted WES changes under the Ecological Protection scenario with indicators of urban proximity and pollution risk. This map identifies critical intervention zones. We propose targeted spatial optimization—strict protection of sensitive ecological zones, green transformation in urban expansion areas, and diffuse pollution controls in agricultural peripheries—to reconcile development with ecosystem resilience. Full article

This study presents a multiphysics simulation approach to optimize graphite-buffered bilayer anodes for enhanced energy density in lithium-ion batteries, assessing the electrochemical impact of diverse inner-layer materials, including silicon, hard carbon, lithium titanate (LTO), and metallic lithium, in pure and graphite-composite forms. A coupled finite-element model implemented in COMSOL Multiphysics 6.2 was used to integrate spherical lithium diffusion, charge conservation, and the solid electrolyte interphase (SEI) formation kinetics. The evaluated anode structure consisted of a 60 µm-thick bilayer: a 30 µm graphite surface layer coupled with a 30 µm inner layer of alternative active materials. Simulations were performed using an NMC622 cathode, LiPF₆ in EC:EMC electrolyte, at room temperature, under a charge rate of 1 C, considering realistic particle sizes (graphite: 2.5 µm; Si: 0.1 µm; hard carbon: 2.5 µm; LTO: 0.2 µm; Li metal: 0.5 µm), and evaluated over 2000 cycles. The hard carbon/graphite configuration exhibited a capacity fade of 5.8% compared with 7.1% in pure graphite. Additionally, the SEI thickness decreased to 0.20 µm (from 0.25 µm), the overpotential dropped to −17 mV (from −59 mV), and the electrolyte consumption was reduced to 20.8% (from 42.9%). The analysis highlights hard carbon and LTO inner layers as optimal trade-offs between capacity and cycle stability, whereas silicon and lithium metal significantly increased the initial capacity but accelerated SEI formation and impedance growth. These findings demonstrate the graphite-buffered bilayer’s potential to decouple interfacial degradation from high-capacity materials, providing valuable guidelines for the design of advanced lithium-ion battery anodes. Full article

Moisture transport in building materials significantly influences their durability, mechanical integrity, and thermal performance. This study presents an experimental investigation of moisture permeability in a range of traditional and modern wall elements, including autoclaved aerated concrete (ACC), ceramic blocks, silicate blocks, perlite concrete blocks, and concrete units. Both vapor diffusion and capillary transport mechanisms were analyzed under controlled climatic conditions using gravimetric and hygrometric methods. Among the tested materials, autoclaved aerated concrete (AAC) was selected for detailed numerical modeling because of its high porosity, strong capillarity, and widespread use in modern construction, which make it especially vulnerable to moisture-related degradation. Based on the experimental findings, a digital twin was developed to simulate hygrothermal behavior of walls made of ACC under various environmental conditions. The model incorporates advanced moisture transport equations, capturing diffusion and capillary effects while considering real-world variables, such as relative humidity, temperature fluctuations, and wetting–drying cycles. Calibration demonstrated strong agreement with experimental data, enabling reliable predictions of moisture behavior over extended exposure scenarios. This integrated approach provides a robust engineering tool for assessing the long-term material performance of AAC, predicting degradation risks, and optimizing material selection in humid climates. The study illustrates how coupling experimental data with digital modeling can enhance the design of moisture-resistant and durable building envelopes. Full article

Modern power systems are increasingly complex, and the risk of transient instability is rising accordingly. Data-driven transient stability assessment (TSA) is attractive for its efficiency, yet in practice the number of unstable events is much smaller than that of stable ones, leading to severe class imbalance and degraded accuracy. This paper proposes a SHAP-guided, classifier-controlled diffusion augmentation framework to mitigate imbalance and enhance TSA. First, SHAP analysis identifies critical unstable and near-boundary samples, ensuring that augmentation targets the most informative regions of the state space. Then, a classifier-guided conditional diffusion model—with a Transformer-based denoising network—generates class-faithful synthetic trajectories that capture long-range temporal dependencies and inter-variable couplings. Case studies on the IEEE 10-machine 39-bus system show that the proposed method consistently surpasses traditional over-sampling (e.g., SMOTE/ADASYN) and deep generative baselines (e.g., CGAN/TimeGAN) in terms of accuracy, precision, recall, and F1-score. Moreover, the approach maintains strong performance under small-sample settings and shortened time-series inputs, demonstrating favorable adaptability and robustness. These results indicate that the proposed augmentation framework offers a practical and effective solution for TSA under severe class imbalance. Full article

Wetlands located in mid-to-high latitudes have undergone significant changes in recent years, compromising their patterns and functions. To understand these alterations in wetland functions, it is crucial to identify the patterns of wetland degradation and the mechanisms based on the conceptual framework of “pattern-process-function.” Our study developed a wetland damage index to analyze changes by calculating the wetland decline rate, remote sensing ecological index, and human pressure index from remote sensing images. We utilized the geographic detectors model to conduct a quantitative analysis of the driving mechanisms. Furthermore, we applied the coupling coordination model to evaluate the relationship between wetland damage and functional changes in the Greater Khingan region. The findings revealed that the wetland damage index increased by 9.86% during 2000–2023, with the damage concentrated in the central area of the study region. The primary explanatory factor for wetland damage was soil temperature during 2000–2010, but population density had become the dominant factor by 2023. The interactive explanatory power of soil temperature and population density on wetland damage was relatively high in the early stage, while the interactive explanatory power of surface temperature and population density on wetland damage was the highest in the later stage. The coupling coordination degree between the Wetland Damage Index (WDI) and Net Primary Productivity (NPP) significantly increased during 2010–2023, rising from 0.19 to 0.23. The increase in the coupling coordination degree between the WDI and Gross Primary Productivity (GPP) exhibited a trend of gradual diffusion from the center to the edge. Our research offers a scientific basis for implementing wetland protection and restoration strategies in mid-to-high latitudes wetlands. Full article

During electron beam cold hearth melting (EBCHM) of Ti-6wt%Al-4wt%V titanium alloy, aluminum volatilization causes compositional segregation in the ingot, significantly degrading material performance. Traditional methods (e.g., the Langmuir equation) struggle to accurately predict aluminum diffusion and compensation behaviors, while computational fluid dynamics (CFD), although capable of resolving multiphysics fields in the molten pool, suffer from high computational costs and insufficient research on segregation control. To address these issues, this study proposes a CFD-machine learning (backpropagation neural network, CFD-ML(BP)) approach to achieve precise prediction and optimization of aluminum segregation. First, CFD simulations are performed to obtain the molten pool’s temperature field, flow field, and aluminum concentration distribution, with model reliability validated experimentally. Subsequently, a BP neural network is trained using large-scale CFD datasets to establish an aluminum concentration prediction model, capturing the nonlinear relationships between process parameters (e.g., casting speed, temperature) and compositional segregation. Finally, optimization algorithms are applied to determine optimal process parameters, which are validated via CFD multiphysics coupling simulations. The results demonstrate that this method predicts the average aluminum concentration in the ingot with an error of ≤3%, significantly reducing computational costs. It also elucidates the kinetic mechanisms of aluminum volatilization and diffusion, revealing that non-monotonic segregation trends arise from the dynamic balance of volatilization, diffusion, convection, and solidification. Moreover, the most uniform aluminum distribution (average 6.8 wt.%, R² = 0.002) is achieved in a double-overflow mold at a casting speed of 18 mm/min and a temperature of 2168 K. Full article