Search Results (68)

The synergistic utilization of waste plastics and tires in asphalt modification is a highly promising sustainable strategy. However, the differential impacts of distinct plastic molecular architectures on the performance and network evolution of rubber-modified asphalt remain fundamentally unclear. This study systematically investigated the physical, rheological, and microstructural properties of composite asphalts modified with desulfurized rubber powder (DRP) and four representative plastics: polyethylene (PE), styrene–isoprene–styrene (SIS), styrene–ethylene–butylene–styrene (SEBS), and styrene–butadiene–styrene (SBS). Furthermore, the pavement performance of the asphalt mixtures prepared via dry and wet methods was comparatively evaluated. Microstructural and spectroscopic analyses revealed that the composite modification was primarily governed by physical blending and swelling. The non-polar, semi-crystalline PE resulted in severe phase separation and extreme low-temperature brittleness. Conversely, the saturated hydrogenated mid-blocks of SEBS endowed the asphalt with the highest high-temperature rutting resistance but severely compromised its low-temperature stress relaxation. Remarkably, SBS interacted synergistically with DRP to form a highly homogeneous and densely interwoven three-dimensional network, thereby achieving an optimal viscoelastic balance, outstanding storage stability, and superior low-temperature ductility. Pavement performance tests further demonstrated that the wet method significantly outperformed the dry method for block copolymers by facilitating sufficient pre-swelling. Overall, the SBS-DRP composite-modified asphalt prepared via the wet method exhibited the most exceptional and balanced comprehensive pavement performance, providing a robust theoretical foundation for the sustainable and high-value recycling of multi-source solid wastes in paving engineering. Full article

Lithium-ion batteries (LIBs) are pivotal for energy storage in electric vehicles and renewable systems, but how to effectively monitor their conditions and ensure their operational reliability is still a concern today. This study employs electrochemical impedance spectroscopy (EIS) to systematically investigate the evolution of impedance characteristics in nickel–cobalt–manganese oxide (NCM) lithium-ion batteries (LIBs) under varying states of charge (SOCs), states of health (SOHs), temperatures, and mechanical compression displacements. Results reveal that higher SOC and temperature reduce impedance by enhancing ion kinetics and interfacial activity, with $R_{ct}$ (charge transfer resistance) exhibiting a U-shaped dependence on SOC, minimized at 40–60%. As SOH declines from 100% to 80%, $R_{SEI}$ (SEI film resistance) and $R_{ct}$ increase progressively, reflecting SEI thickening and electrode degradation. Mechanical compression (0–8 mm) elevates all resistances, particularly $R_{ct}$ at high SOC, due to structural deformation and hindered diffusion. DRT (distribution of relaxation times) spectra highlight amplified low-frequency peaks with aging and low SOC, underscoring diffusion limitations. These findings elucidate multi-scale failure mechanisms, from interfacial polarization to structural instability, providing a framework for non-invasive health monitoring and lifetime prediction. Full article

Fiber-reinforced polymer (FRP) composites used in marine environments suffer progressive degradation due to hydrothermal aging, which undermines their structural, physical and morphological integrity. In this study, a novel polyester-based nano-gelcoat reinforced with hexagonal boron nitride (h-BN) nanoparticles was developed as an advanced FRP composite coating for marine applications. Glass fiber/epoxy laminates coated with h-BN/polyester nano-gelcoat were subjected to accelerated hydrothermal aging (immersion in 80 °C artificial seawater for 90 days). Mechanical (tensile/flexural tests), viscoelastic (creep and stress relaxation), thermal (DSC/TGA), and morphological (optical microscopy/SEM) analyses were performed on aged and unaged samples. The h-BN-enhanced nano-gelcoat increased the composite’s resistance to hydrothermal aging. In particular, the optimally doped nano-gelcoat (~1 wt% h-BN) retained the highest tensile and flexural strength and modulus, reducing the property losses seen in the unreinforced system by about half (flexural strength 531.29 MPa vs. 1070.52 MPa for the uncoated laminate). Thermal analysis indicated elevated decomposition onset temperatures and higher char yields with h-BN, confirming improved thermal stability. Morphological observations revealed well-dispersed h-BN at 1 wt% with minimal microcracking, whereas higher filler loadings led to agglomeration. Additionally, a TOPSIS-based multi-criteria decision-making (MCDM) analysis was performed across mechanical, viscoelastic, and thermal metrics, which identified the 1 wt% h-BN coating as the most balanced formulation after hydrothermal aging. Full article

Dynamic mechanical thermal analysis (DMTA) is widely used to assess the effects of process- and environment-induced changes in polymer matrix composites, with the glass transition temperature (T_g) often reported from the tan d peak at a single excitation frequency. However, such an approach neglects the inherently kinetic nature of the glass transition and may obscure thermally induced changes in relaxation response. Multi-frequency DMTA was employed to investigate the evolution of glass transition response of a wet-layup unidirectional carbon/epoxy composite subjected to thermal aging at temperatures ranging from 66 °C to 260 °C for periods up to 72 h, using unexposed (23 °C) results as an ambient baseline reference. Tests were conducted using a single cantilever mode in both longitudinal and transverse configurations over a range of excitation frequencies from 0.3 to 30 Hz. Results demonstrate that thermal exposure affects not only the absolute value of the glass transition temperature, but also its frequency sensitivity and directional dependence. A frequency sensitivity parameter and a directional amplification factor are introduced to quantify frequency–direction coupling. While post-cure-dominated aging regimes exhibit relatively stable coupling behavior, degradation-dominated conditions at elevated temperatures and longer periods of thermal exposure lead to pronounced increases in transverse frequency sensitivity, which reflects early evolution of matrix- and interphase-level deterioration. These findings highlight the value of multi-frequency DMTA with tests in both primary directions for the mechanistic assessment of effects of thermo-oxidative response in polymer matrix composites. Full article

The mitigation of traffic noise is essential for the development of sustainable and livable urban environments, a goal that is directly contingent on addressing tire-pavement interaction noise (TPIN) as the dominant acoustic pollutant at medium to high vehicle speeds. This comprehensive review addresses a critical gap in the literature by systematically analyzing the damping properties of pavement systems through a unified, multi-scale framework—from the molecular-scale viscoelasticity of asphalt binders to the composite performance of asphalt mixtures. The analysis begins by synthesizing state-of-the-art testing and characterization methodologies, which establish a clear connection between macroscopic damping performance and the underlying viscoelastic mechanisms coupled with the microscopic morphology of the binders. Subsequently, the review critically assesses the influence of critical factors, such as polymer modifiers including rubber and Styrene-Butadiene-Styrene (SBS), temperature, and loading frequency. This examination elucidates how these variables govern molecular mobility and relaxation processes to ultimately determine damping efficacy. A central and synthesizing conclusion emphasizes the paramount importance of the asphalt binder’s properties, which serve as the primary determinant of the composite mixture’s overall acoustic performance. By delineating this structure-property-performance relationship across different scales, the review consolidates a foundational scientific framework to guide the rational design and informed material selection for next-generation asphalt pavements. The insights presented not only advance the fundamental understanding of damping mechanisms in pavement materials but also provide actionable strategies for creating quieter and more sustainable transportation infrastructures. Full article

Structural adhesives in aeronautical applications are routinely exposed to complex loading histories that generate time-dependent deformation, making accurate prediction of their viscoelastic response essential for reliable assessment of joint integrity. This work presents an integrated experimental and modeling study of the aerospace-grade epoxy adhesive 3M Scotch-Weld EC-2216 using multi-step creep and stress-relaxation tests performed at room temperature and controlled loading rates, combined with fractional viscoelastic modeling. Unlike traditional single-step characterizations, the multi-step protocol employed here captures the cumulative loading effects and fading-memory dynamics that govern the adhesive’s mechanical response. The experimental data were analyzed using fractional Maxwell, Voigt–Kelvin, and Zener formulations. Statistical evaluation based on the Bayesian Information Criterion (BIC) consistently identified the Fractional Zener Model (FZM) as the most robust representation of the stress-relaxation behavior, effectively capturing both the unrelaxed and relaxed modulus. The results demonstrate that EC-2216 exhibits hierarchical relaxation mechanisms and history-dependent viscoelasticity that cannot be accurately described by classical integer-order models. Overall, the study validates the use of fractional operators to represent the broad and hierarchical relaxation spectra typical of toughened aerospace epoxies and provides a rigorous framework for durability assessment and predictive modeling of adhesively bonded structures. Full article

Lacustrine shale oil in the Chang 7 Member of the Ordos Basin is controlled by a multi-scale pore–throat system in which oil occurrence, spontaneous imbibition, and pore-structure evolution are tightly coupled. In this study, nitrogen adsorption and micro-computed tomography (μCT) were employed to characterize pore-size distribution and connectivity, whereas nuclear magnetic resonance (NMR) T₂ relaxation was utilized to classify oil occurrence states, and X-ray diffraction (XRD) and total organic carbon (TOC) analyses were performed to determine mineralogical and organic compositions. Spontaneous imbibition experiments were conducted at 60 °C and subsequently extended to temperature–pressure sequence tests. The Chang 7 shale exhibits a stratified pore system in which micropores, mesopores, and macropores jointly define a three-tier “micropore adsorption–mesopore confinement–macropore mobility” pattern. As pore size and connectivity increase, the equilibrium imbibed mass and initial imbibition rate both rise, while enhanced wettability (contact angle decreasing from 81.2° to 58.7°) further strengthens capillary uptake. Temperature elevation promotes imbibition, whereas increasing confining pressure suppresses it, revealing a “thermal enhancement–pressure suppression” behavior. μCT-based network analysis shows that imbibition activates previously ineffective pore–throat elements, increasing coordination number and connectivity and reducing tortuosity, which collectively represents a capillary-driven structural reconfiguration of the pore network. When connectivity exceeds a threshold of about 0.70, the flow regime shifts from interface-dominated to channel-dominated. Building on these observations, a multi-scalecoupling framework and a three-stage synergistic mechanism of “pore-throat activation–energy conversion–structural reconstruction” are established. These results provide a quantitative basis for predicting imbibition efficiency and optimizing capillary-driven development strategies in deep shale oil reservoirs. Full article

Transistor-level Integrated Circuit (IC) design is fundamental to modern electronics, yet it remains one of the most expertise-intensive and time-consuming stages of chip development. As circuit complexity continues to rise, the need to automate this low-level design process has become critical to sustaining innovation and productivity across the semiconductor industry. This study presents a fully automated methodology for transistor-level IC design using a novel framework that integrates Grammatical Evolution (GE) with Cadence SKILL code. Beyond automation, the framework explicitly examines how symmetry and asymmetry shape the evolutionary search space and resulting circuit structures. To address the time-consuming and expertise-intensive nature of conventional integrated circuit design, the framework automates the synthesis of both digital and analogue circuits without requiring prior domain knowledge. A specialised attribute grammar (AG) evolves circuit topology and sizing, with performance assessed by a multi-objective fitness function. Symmetry is analysed at three levels: (i) domain-level structural dualities (e.g., NAND/NOR mirror topologies and PMOS/NMOS exchanges), (ii) objective-level symmetries created by logic threshold settings, and (iii) representational symmetries managed through grammatical constraints that preserve valid connectivity while avoiding redundant isomorphs. Validation was carried out on universal logic gates (NAND and NOR) at multiple logic thresholds, as well as on a temperature sensor. Under stricter thresholds, the evolved logic gates display emergent duality, converging to mirror-image transistor configurations; relaxed thresholds increase symmetric plateaus and slow convergence. The evolved logic gates achieve superior performance over conventional Complementary Metal–Oxide–Semiconductor (CMOS), Transmission Gate Logic (TGL), and Gate Diffusion Input (GDI) implementations, demonstrating lower power consumption, a reduced Power–Delay Product (PDP), and fewer transistors. Similarly, the evolved temperature sensor exhibits improved sensitivity, reduced power, and Integral Nonlinearity (INL), and a smaller area compared to the conventional Proportional to Absolute Temperature (PTAT) or “gold” circuit, without requiring resistors. The analogue design further demonstrates beneficial asymmetry in device roles, breaking canonical structures to achieve higher performance. Across all case studies, the evolved designs matched or outperformed their manually designed counterparts, demonstrating that this GE-based approach provides a scalable and effective path toward fully automated, symmetry-aware integrated circuit synthesis. Full article

The authors frame greenhouse operation as a Controlled Environment Agriculture (CEA) challenge involving multiple interdependent targets: air temperature and humidity, CO₂ enrichment, photoperiod-constrained lighting, and irrigation under dynamic and limited energy availability. We propose a knowledge-driven, multi-objective Model Predictive Controller whose cost function integrates expert priorities elicited via an online Analytic Hierarchy Process (AHP) survey; these AHP-derived weights parameterize the controller’s objectives and are solved over two 72 h seasonal episodes, so the MPC can anticipate renewable availability and coordinate HVAC, (de)humidification, CO₂ dosing, LED lighting, and irrigation alongside dispatch from photovoltaic and wind sources, battery storage, and the grid. By embedding the physical interdependence of climate variables directly into the decision layer, the controller schedules energy-intensive actions around renewable peaks and avoids counterproductive actuator conflicts. Seasonal case studies (summer/high solar and winter/low solar) demonstrate robust performance: temperature tracking errors of SMAPE 2.25%/3.05% and CO₂ SMAPE 3.72–3.92%; humidity control with SMAPE 7.04–8.56%; lighting and irrigation following setpoints with low NRMSE (0.08–0.14). Summer energy was 59% renewable; winter was only 13%, increasing grid reliance to 77.5% (peaks: 4.57 kW/6.92 kW for 197.7/181.5 kWh). Under water or energy scarcity, the controller degrades gracefully, protecting high-priority agronomic variables while allowing bounded relaxation on lower-priority targets. This expert-informed, predictive, and resource-aware orchestration offers a scalable route to precision greenhouse control within the food–water–energy nexus. Full article

This study addresses the stability and deformation control of the Xiling auxiliary shaft in the Sanshandao Gold Mine during excavation, under the complex geological conditions of high in situ stress, high pore pressure, and elevated geothermal gradients. A thermal–hydraulic–mechanical (THM) coupling numerical model is developed to investigate the stress distribution, deformation mechanisms, and long-term stability of the surrounding rock under multi-physical interactions. Meanwhile, the influence of excavation rate on rock stability is analyzed. The results indicate that excavation induces significant stress redistribution, with stress concentrations in high-elastic-modulus strata, where the maximum compressive and tensile stresses reach 15.9 MPa and 14.1 MPa, respectively. The maximum displacement occurs in low-stiffness rock layers (around 1400 m depth), with a total magnitude of 1139 mm, primarily resulting from unloading relaxation, pore pressure reduction, and thermal contraction. Excavation rate strongly affects the temporal evolution of deformation: faster excavation leads to greater instantaneous displacements, whereas slower excavation suppresses displacement due to the sustained influence of thermal contraction. Based on these findings, particular attention should be paid to the low-stiffness strata near 1400 m depth during the construction of the Xiling auxiliary shaft. A combined support system consisting of high-prestress rock bolts, lining, and grouting is recommended for deformation-concentrated zones, while excavation rates should be optimized to balance efficiency and safety. Furthermore, long-term monitoring of temperature, pore pressure, and displacement is essential to achieve dynamic risk control. These results provide valuable theoretical and engineering insights for the safe construction and stability management of deep mine shafts. Full article

This study employs a multi-technique approach to elucidate how graphene incorporation affects phase formation, microstructure, and thermal behavior in PVA-assisted sol–gel synthesized Ca_2.95Eu_0.05Co₄O_x nanomaterials. XRD confirms the preservation of the primary phases (hexagonal CaCO₃ and cubic CoO) alongside a distinct graphene (002) reflection; a systematic low-angle shift of the calcite (104) peak evidences partial relaxation of residual lattice strain with increasing graphene content, while Scherrer analysis indicates tunable crystallite size. Raman spectroscopy corroborates graphene incorporation through pronounced D (~1300 cm⁻¹) and G (~1580 cm⁻¹) bands and supports the XRD-identified phase coexistence via cobalt-oxide and calcite vibrations in the 200–700 cm⁻¹ region, also indicating increased defect/disorder with graphene loading. SEM shows grain refinement, denser/bridged lamellar textures, and reduced porosity at low–moderate graphene contents (1–3 wt.%), contrasted by agglomeration-driven heterogeneity at higher loadings (5–7 wt.%). EDX reveals increasing carbon with Ca/Co redistribution at accessible surfaces, and TG–DSC corroborates the removal of oxygen-containing groups and oxidative combustion of graphene at mid temperatures. Collectively, Raman–XRD-consistent evidence demonstrates that graphene provides a tunable handle over lattice strain, crystallite size, and grain-boundary architecture, establishing a processing–composition basis for optimizing functional (e.g., electrical/thermoelectric) performance. Full article

Thermal oxidative aging failure of high-temperature vulcanized silicone rubber (HTV) in high-voltage insulators is the core hidden danger of power grid security. In this study, terahertz time domain spectroscopy (THz-TDS) and attenuated total reflection infrared spectroscopy (ATR-FTIR) were combined to reveal the quantitative structure–activity relationship between dielectric response and chemical group evolution of HTV during accelerated aging at 200 °C for 80 days. In this study, HTV flat samples were made in the laboratory, and the dielectric spectrum of HTV in the range of 0.1 THz to 0.4 THz was extracted by a terahertz time–domain spectrum platform. ATR-FTIR was used to analyze the functional group change trend of HTV during aging, and the three-stage evolution of the dielectric real part (0.16 THz), the dynamics of the carbonyl group, the monotonic rise of the dielectric imaginary part (0.17 THz), and the linear response of silicon-oxygen bond breaking were obtained by combining the double Debye relaxation theory. Finally, three aging stages of HTV were characterized by dielectric loss angle data. The model can warn about the critical point of early oxidation and main chain fracture and identify the risk of insulation failure in advance compared with traditional methods. This study provides a multi-scale physical basis for nondestructive life assessment in a silicon rubber insulator. Full article

Addressing the urgent need for lightweight and reusable energy-absorbing materials in aviation impact resistance, this study introduces an innovative multi-directional braided metamaterial design enabled by 4D printing technology. This approach overcomes the dual challenges of intricate manufacturing processes and the limited functionality inherent to traditional textile preforms. Six distinct braided structural units (types 1–6) were devised based on periodic trigonometric functions (Y = A sin(12πX)), and integrated with shape memory polylactic acid (SMP-PLA), thereby achieving a synergistic combination of topological architecture and adaptive response characteristics. Compression tests reveal that reducing strip density to 50–25% (as in types 1–3) markedly enhances energy absorption performance, achieving a maximum specific energy absorption of 3.3 J/g. Three-point bending tests further demonstrate that the yarn amplitude parameter A is inversely correlated with load-bearing capacity; for instance, the type 1 structure (A = 3) withstands a maximum load stress of 8 MPa, representing a 100% increase compared to the type 2 structure (A = 4.5). A multi-branch viscoelastic constitutive model elucidates the temperature-dependent stress relaxation behavior during the glass–rubber phase transition and clarifies the relaxation time conversion mechanism governed by the Williams–Landel–Ferry (WLF) and Arrhenius equations. Experimental results further confirm the shape memory effect, with the type 3 structure fully recovering its original shape within 3 s under thermal stimulation at 80 °C, thus addressing the non-reusability issue of conventional energy-absorbing structures. This work establishes a new paradigm for the design of impact-resistant aviation components, particularly in the context of anti-collision structures and reusable energy absorption systems for eVTOL aircraft. Future research should further investigate the regulation of multi-stimulus response behaviors and microstructural optimization to advance the engineering application of smart textile metamaterials in aviation protection systems. Full article

This study proposes an RBF-PSO hybrid framework for efficient inversion analysis of hydration heat parameters in mass concrete temperature fields, addressing the computational inefficiency and accuracy limitations of traditional methods. By integrating a Radial Basis Function (RBF) surrogate model with Particle Swarm Optimization (PSO), the method reduces reliance on costly finite element simulations while maintaining global search capabilities. Three objective functions—integral-type (F₁), feature-driven (F₂), and hybrid (F₃)—were systematically compared using experimental data from a C40 concrete specimen under controlled curing. The hybrid F₃, incorporating Dynamic Time Warping (DTW) for elastic time alignment and feature penalties for engineering-critical metrics, achieved superior performance with a 74% reduction in the prediction error (mean MAE = 1.0 °C) and <2% parameter identification errors, resolving the phase mismatches inherent in F₂ and avoiding F₁’s prohibitive computational costs (498 FEM calls). Comparative benchmarking against non-surrogate optimizers (PSO, CMA-ES) confirmed a 2.8–4.6× acceleration while maintaining accuracy. Sensitivity analysis identified the ultimate adiabatic temperature rise as the dominant parameter (78% variance contribution), followed by synergistic interactions between hydration rate parameters, and indirect coupling effects of boundary correction coefficients. These findings guided a phased optimization strategy, as follows: prioritizing high-precision calibration of dominant parameters while relaxing constraints on low-sensitivity variables, thereby balancing accuracy and computational efficiency. The framework establishes a closed-loop “monitoring-simulation-optimization” system, enabling real-time temperature prediction and dynamic curing strategy adjustments for heat stress mitigation. Robustness analysis under simulated sensor noise (σ ≤ 2.0 °C) validated operational reliability in field conditions. Validated through multi-sensor field data, this work advances computational intelligence applications in thermomechanical systems, offering a robust paradigm for parameter inversion in large-scale concrete structures and multi-physics coupling problems. Full article

This paper investigates data transmission in an Internet of Things (IoT) network, where multiple devices send environmental data to a remote base station through an unmanned aerial vehicle (UAV) relay. The UAV serves as an airborne intermediary that collects status information from distributed IoT devices (e.g., temperature readings in a real-time forest fire monitoring system) and forwards it to the base station. To capture the impact of data staleness, a novel Age of Information (AoI) and entropy-aware system loss is defined in terms of L-conditional cross-entropy, which quantifies the expected penalty caused by state misestimation. The scheduling problem, which aims to minimize the system loss defined by L-conditional cross-entropy, is formulated as a Restless Multi-Armed Bandit (RMAB) problem. By applying Lagrange relaxation, the objective function is decomposed into tractable sub-problems, enabling a low-complexity, gain-index-based scheduling strategy. Numerical simulations validate the effectiveness of the proposed algorithm in reducing the long-term average system loss. In particular, the gain-index-based policy achieves a significant reduction in average penalty compared to random, round-robin, periodic update, and MAX-AoI scheduling strategies, demonstrating its superior performance over these baselines. Full article