Search Results (24,816)

This work presents an integrated experimental and numerical determination of the aerodynamic (lift) characteristics of an ogive forebody equipped with all moving canards. Experimental testing was conducted in the subsonic custom-made wind tunnel of the Vlatacom Institute at a nominal free stream velocity of 32 m/s (and Mach number M = 0.09). Aerodynamic loads on the canards were measured using a custom one-component force balance, while free stream flow properties were obtained via a calibrated Pitot–Prandtl probe on the full-scale geometry model. On the numerical side, RANS simulations were performed in ANSYS Fluent using the k-ω SST turbulence model. Two geometric representations were considered: (a) a high-fidelity configuration explicitly resolving the physical gap between the canard and ogive, and (b) a simplified configuration with the gap removed. Boundary conditions, Reynolds number, and operating parameters were matched to the wind tunnel conditions to enable a strict one-to-one comparison. Particular emphasis was placed on examining the effect of geometric simplification on the predicted lift characteristics. The gap-resolved configuration reproduces the experimentally measured lift curve within approximately 10% across the investigated angle-of-attack range, satisfying conventional aerodynamic validation criteria. The results confirm both the robustness of the applied RANS approach for highly three-dimensional separated flows often found in engineering applications, as well as the reliability of the experimental measurement system. Full article

To address inherent limitations of chitosan-based edible films, including inadequate mechanical strength and poor moisture resistance, cellulose nanofibers (CNF) were employed as a synergistic film-forming component to partially substitute chitosan in the fabrication of ternary composite films (denoted as CSTF-CNFs). This approach was based on a previously developed chitosan matrix modified with tannin-Fe³⁺ nanoparticles (TF). It was hypothesized that CNF could function as a reinforcing scaffold to improve the dispersion of TF within the film matrix and, through hydrogen bonding and physical entanglement, form an interpenetrating fiber network with chitosan, thereby enhancing the structural and barrier properties of the films. The present study systematically evaluated the influence of varying CNF substitution ratios (0–30%) on the physicochemical characteristics of the resulting composite films and their performance in tomato preservation. The results demonstrated that an appropriate CNF incorporation facilitated the formation of a dense, cross-linked network with chitosan and TF via hydrogen bond interactions, significantly improving both mechanical strength and water resistance. Among all formulations, the CSTF-CNF20 film exhibited optimal comprehensive performance, achieving the highest tensile strength of 27.60 MPa. Moreover, its swelling ratio markedly decreased from 675.5% (CSTF-CNF0) to 120.9%, while the water contact angle increased to 113.7°, and the DPPH radical scavenging activity remained above 85%. Tomato preservation assays revealed that, in comparison with the untreated control and polyethylene film-wrapped groups, the application of CSTF-CNF20 coating effectively mitigated the decline in weight loss and firmness, preserved surface color integrity, and resulted in the highest L* value alongside the lowest soluble solids content. These findings suggest that the synergistic integration of CNF with nano-scale metal–phenolic networks offers a viable strategy for developing high-performance chitosan-based edible films. The CSTF-CNF20 composite film holds significant promise for application in the postharvest preservation of fruits and vegetables. Full article

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

Carbon quantum dots (CQDs) and stimuli-responsive hydrogels are advanced functional materials whose hybridization yields CQD-enhanced stimuli-sensitive hydrogels, opening new interdisciplinary avenues for smart material applications. This review systematically summarizes the latest advances in these composites, focusing on synthetic strategies, structure–property modulation mechanisms, and practical applications. Distinct from existing reviews that either investigate CQDs or hydrogels independently or discuss their composites in a single research field, this work features core novelties in integration strategy, application scope and critical analysis: it systematically compares the advantages, limitations and applicable scenarios of three typical CQD–hydrogel integration approaches (physical entrapment, in situ synthesis, covalent conjugation), comprehensively covers the multi-field application progress of the composites and conducts in-depth cross-field analysis of their common scientific issues and technical bottlenecks. By incorporating CQDs, the composites achieve remarkable performance optimizations: 40% improved mechanical toughness, sub-ppm-level heavy metal-sensing sensitivity, and over 80% organic dye photocatalytic degradation efficiency, addressing pure hydrogels’ inherent limitations of insufficient strength and single functionality. These enhancements enable sophisticated applications in biomedical field (real-time biosensing, controlled drug delivery), environmental remediation (pollutant detection/degradation), energy storage, and flexible electronics. The synergistic interplay between CQDs and hydrogels facilitates precise single/multi-stimulus responsiveness (pH, temperature, light), a pivotal advance for precision medicine and intelligent environmental monitoring. Despite promising progress, the large-scale practical application of CQD–hydrogel composites still faces prominent challenges: the difficulty in scalable fabrication with the uniform dispersion of CQDs in hydrogel matrices, poor long-term stability of most composites under physiological cyclic stress (service life < 6 months in practical tests), and low accuracy in discriminating multi-stimuli in complex real-world matrices. Future research should prioritize biomass-based eco-friendly CQD synthesis, machine learning-aided multimodal responsive systems, and 3D bioprinting for scalable manufacturing. Full article

The Zhongdian Arc is an important copper polymetallic ore cluster in China’s Sanjiang Tethyan Metallogenic Domain, and the Langdu deposit is a representative porphyry–skarn Cu deposit in this region. This study aims to constrain the timing of magmatic activity at the Langdu deposit. It also seeks to reveal the magma’s physical–chemical properties and evolution, and to identify the factors controlling mineralization. To achieve these objectives, this study used LA-ICP-MS zircon U-Pb dating and elemental analysis, combined with halogen and trace element data from apatite. Zircon U–Pb dating shows that the Langdu intrusions were emplaced at ca. 216 Ma in a continental arc setting associated with the westward subduction of the Garzê–Litang oceanic crust during the Late Triassic. Geochemical and mineralogical features indicate that the Langdu intrusions are I-type granite. They originated from partial melting of the mantle wedge metasomatized by subduction fluids. During their ascent, these magmas experienced fractional crystallization dominated by amphibole, titanite, rutile, and monazite. Geochemical records from zircon and apatite further reveal that the ore-forming magma of the Langdu intrusions exhibited high oxygen fugacity (ΔFMQ = +1.53), elevated H₂O content (avg. 7.63 wt.%), and enrichment in S (avg. 560 ppm) and Cl (avg. 2141 ppm). This Cl-rich magma experienced fluid exsolution during its early evolutionary stage. This provided the necessary conditions for metal extraction and transport. In summary, the key factors controlling the formation of the Langdu porphyry–skarn Cu deposit are high-oxygen-fugacity magma enriched in water and volatiles (S and Cl), coupled with efficient fluid exsolution. This understanding is important for better understanding regional metallogeny and for guiding mineral exploration. Full article

ZnO can be easily obtained using different salts as precursors, and many examples are present in the literature describing the effect of several additives in the synthesis. In this paper, we study the effects of the addition of polyphenols present in the residues of the wine supply chain. The polyphenols are extracted from grape pomace and fractionated, exploiting a membrane-based process equipped with polysulfone ultrafiltration membranes (cut-off 1 kDa and 5 kDa) that can separate the plethora of molecules into larger than 5 kDa and smaller than 1 kDa. The extract and its fractions after the ultrafiltration process were used as additives for the thermal precipitation synthesis of ZnO from Zn acetate. The chemical and physical properties were studied with the aim of understanding the characteristics that influence the activity of the photocatalysts. To this purpose, a commercial system was used for comparison, and the photoactivity was analyzed with a caffeine solution upon irradiation, exploiting the UVA and VIS electromagnetic radiation for the activation of the catalytic materials. The kind of polyphenol fraction affects the surface behaviors of the nanoparticles. Morphology, presence of trapped hole/electron centers, and acidity/basicity of the surface sites of ZnO appear to be the most relevant features in the efficiency towards caffeine degradation. Full article

The evolution toward sixth-generation (6G) networks envisions humans as active nodes within a fully interconnected digital ecosystem, supported by data collected from in-body and on-body sensors. Since many of these devices are not equipped to connect directly to 6G networks, Wireless Body Area Networks (WBANs) serve as an essential intermediate layer. However, conventional radio-frequency technologies face limitations in terms of energy efficiency, security, and data integrity, motivating the adoption of lightweight security mechanisms. Physical Layer Security (PLS), and in particular Physical Key Extraction (PKE), offers a promising solution by enabling legitimate devices to derive shared cryptographic keys from the reciprocal properties of the communication channel. Galvanic coupling (GC) communication has recently emerged as an on-body transmission technology alternative to radio-frequency (RF), which exploits low-power electrical signals propagating through biological tissue. Building on prior feasibility studies, this work proposes a PKE framework tailored to GC channels, integrating a lightweight key reconciliation method, based on Hamming (7,4) error-correction codes, and evaluating system performance through dedicated reliability and security Key Performance Indicators (KPIs). Results reveal a trade-off shaped by electrode placement and channel quantization parameters. Among the ones tested, the optimal configuration is achieved with a 3 cm transverse inter-electrode spacing at both transmitter and receiver, and a 3 cm longitudinal separation between transmitter and receiver, by quantizing the channel impulse response with two quantization bits. While this work focuses on validating the method in controlled conditions in order to establish a reliable study framework, future developments will focus on enhanced reconciliation, privacy amplification, and analysis of the GC channel considering physiological and environmental variations. Full article

Fungal decay fundamentally alters moisture transport in wood through complex bio-physical coupling mechanisms that remain poorly understood. Brown-rot fungi such as Coniophora puteana (Schumach.: Fr.) P. Karst. degrade wood through chelator-mediated Fenton (CMF) chemistry, producing hydroxyl radicals that depolymerise cellulose and hemicellulose before significant mass loss. This diffusion-dependent process requires elevated moisture content and leads to structural degradation. However, existing models fail to capture the interaction between boundary-driven fungal colonization, decay-induced property changes, and multi-phase multi-Fickian moisture redistribution, particularly the separate evolution of bound- and free-water phases during decay. Here, we present a transport-response bio-hygrothermal finite element model that couples boundary-driven Monod-type fungal colonization kinetics with multi-phase moisture transport (free water, bound water, vapor) in decaying wood. Although fungal biomass evolution is simulated via a reaction–diffusion equation, decay progression is not derived from biomass–substrate interaction but prescribed independently as an experimentally informed input. The model incorporates decay-modified sorption isotherms, permeability evolution, and boundary-driven biomass influx, along with associated moisture transport, into the governing equations. The model is validated against low-field nuclear magnetic resonance (LF-NMR) measurements of C. puteana decay in Scots pine over 35 days. The model successfully reproduces the experimentally observed moisture evolution: a peak free-water content of 50%–70% during weeks 1–2, followed by a progressive decline, while bound water remains remarkably constant despite advancing decay. Monte Carlo uncertainty quantification demonstrates hierarchical parameter control: bound water is governed solely by thermodynamic factors, while free water responds to interacting biological and physical processes. Time-resolved correlation analysis shows a fundamental transition from colonization-dominated (weeks 1–2) to transport-dominated (weeks 3–5) moisture control, quantitatively explaining the experimentally observed shift from accumulation to depletion. This transport-response framework for analyzing moisture behavior under externally defined decay progression establishes quantitative parameter hierarchies that may inform the development of future substrate-coupled bio-hygrothermal models. Full article

This research aimed to evaluate the effects of formulation and process conditions on the physical and structural properties of starch–brewers’ spent grain films. Three factors were considered: BSG amounts (0, 1, 3, 5%), a possible ultrasonication pre-treatment, and different microwave gelatinization treatments (450 W for 80 and 90 s; 900 W for 45 and 50 s). An increase in BSG is responsible for increases in moisture (10.72 → 23.40%), water absorption (67.65 → 95.73%), density (0.90 → 1.27 g/cm³), browning index (5.86 → 85.88), UV blocking capacity (82.42% → 99.96% for UV_A; 61.28% → 99.86% for UV_B), and degradability in the first 7 days (58.72 → 66.57%), but dramatically decreases the Young’s modulus and tensile strength (fallen to 2.90 N/mm² and 0.21 N/mm², at 5% BSG). Sonication contributes to increased browning index (36.17 → 37.24), UV blocking capacity, solubility (49.35 → 51.49%) and Young’s modulus (4.40 → 4.77 N/mm²). The most severe microwave treatment (900 W, 50 s) minimizes moisture (15.83%) and water absorption (80.89%) and maximizes density (1.21 g/cm³), browning index (37.52), and Young’s modulus (5.37 N/mm²). SEM micrographs allow us to observe that the film surface appears rough, and the structure becomes increasingly porous as BSG % increases. The regression analysis indicates that the quadratic model effectively describes the relationships between the three factors and each of the most important properties of the films; it is suitable for predicting film behavior and optimizing their characteristics depending on the desired use. Full article

While macroscopic stress significantly impacts the performance of metallic components, the underlying atom–electron coupling mechanisms governed by distinct crystal symmetries remain insufficiently understood. To address this gap, this work systematically investigates the structural and electronic evolution of representative metallic materials under applied stress. Experimentally, X-ray diffraction (XRD) revealed complex macroscopic residual stress distributions in cold rolled titanium alloy and silicon steel. Motivated by these engineering observations, first-principles density functional theory (DFT) calculations were conducted to uncover the underlying physical mechanisms. Specifically, the responses of face-centered cubic (FCC) aluminum and copper, body-centered cubic (BCC) iron, and hexagonal close-packed (HCP) titanium crystals were investigated under tension and compression using the RPBE functional. Stress-dependent elastic properties, density of states (DOS), band structures, and phonon spectra were calculated. Results show that tension softens all metals (Al becomes mechanically unstable), whereas compression stiffens their lattices. Electronically, tensile loading sharpens DOS peaks near the Fermi level and shifts conduction bands closer to it, whereas compression smooths DOS peaks and shifts bands away. Phonon analysis indicates Cu and Ti remain dynamically stable, while Al and Fe exhibit phonon mode softening under high tension. These stress-induced changes highlight crucial atom–electron coupling mechanisms, providing a theoretical basis for tailoring metallic performance via stress engineering. Full article

The effects of environmental factors on zooplankton within the marginal ice zone (MIZ) of the Barents Sea remain poorly understood. To address this knowledge gap, we investigated mesozooplankton communities across the central, northern, and northeastern regions in April 2016. Abundance and biomass ranged from 90 to 997 individuals m⁻³ and from 1.1 to 48.6 mg dry mass m⁻³ (0.3 to 13.6 g dry mass m⁻²), respectively. Oithona similis was the most abundant taxon, while calanoid copepods, including Calanus spp., Metridia longa, and Pseudocalanus spp., dominated total biomass. The spatial distribution of mesozooplankton communities was closely linked to the physical properties of water masses. Cluster analysis identified two distinct assemblages associated with Polar Front Water and Arctic Water. Redundancy analysis and generalized linear models identified temperature, mean salinity, mean chlorophyll a concentration, and sea ice concentration as significant predictors of abundance and biomass. The dominance of older life stages within major copepod taxa indicated a winter status for the mesozooplankton community. Regional and temporal comparisons of mesozooplankton biomass with prior May–June data from central and northwestern areas highlighted higher productivity in the northern and northeastern MIZ. This increase is potentially related to the warming trends observed in the Arctic since the 2000s. Our research provides novel insights into Arctic marine zooplankton assemblages and serves as a valuable baseline for future ecological monitoring and modeling of the Barents Sea ecosystem in the context of global climate change. Full article

To address the pronounced self-aggregation of highly loaded silica in the aqueous phase and the substantial filler loss occurring during the flocculation stage of latex compounding, this study introduces disaggregated and activated sepiolite possessing a spatial confinement effect as both a suspension stabilizer and a synergistic reinforcing component. On this basis, a multiscale natural rubber (NR)/silica/sepiolite composite system was constructed via a latex compounding route. Rheological characterization combined with static sedimentation observations revealed that the percolation threshold of the sepiolite is approximately 0.8 wt%. When the sepiolite content exceeds 1.0 wt%, its fibrous morphology enables the formation of a continuous three-dimensional network, which physically constrains silica particles and effectively suppresses their sedimentation and self-aggregation in the aqueous medium. Guided by this percolation behavior, a stable silica/sepiolite hybrid slurry was subsequently wet-mixed with natural rubber latex, and the influence of sepiolite loading on silica retention during flocculation, as well as on the resulting composite properties, was systematically examined. The results demonstrate that incorporation of sepiolite reduces filler loss during flocculation, with the loss rate decreasing from 4.7% to 1.1%. The Payne effect, SEM, dynamic and static mechanical analyses indicate that an appropriate sepiolite dosage promotes dispersion of silica within the rubber matrix while simultaneously strengthening filler–rubber interfacial interactions. Accordingly, tensile and tear strengths are increased from 32.1 to 35.5 MPa and from 92.3 to 133.4 N·mm⁻¹, respectively, while wet skid resistance is preserved and both rolling resistance and wear resistance are further improved. The findings of this work establish a practical and efficient strategy for the wet preparation of high-performance NR/silica composites. Full article

This study addresses the urgent need for sustainable alternatives to single-use plastics by developing biodegradable composites from peach and apple processing waste employing hot compression molding. Utilizing a definitive screening design, the impact of the process variables, including recipe composition, grinding size, pressure, temperature, and holding time, on the physical (including water resistance) and mechanical properties of the composites was systematically evaluated. Physicochemical and thermal analyses of the dried by-products indicated that processing temperatures below 150 °C prevent the degradation of lignocellulosic constituents. The results demonstrated that increasing both the molding pressure and holding time decreased the composite thickness, while enhancing the stiffness and flexural strength, with modulus of elasticity values exceeding 1000 MPa under optimal conditions. Higher molding temperatures reduced water absorption and diffusivity, particularly in lignin-rich composites, by promoting lignin softening and particle consolidation, resulting in denser structures with limited moisture transport. Biodegradability was assessed through soil burial tests over 200 days, revealing a weight loss ranging from 54.2% to 90.7% among samples, with apple-based composites exhibiting greater degradation compared to peach-based ones. Overall, the study highlights the development of a “green composite” formulation inspired by biomimetic principles, exploiting the natural self-bonding capacity of lignocellulosic biomass, where two different-in-composition biowastes are combined to produce a plastic-free composite material with possible applications in the foodservice industry. Full article

The Fiber-Reinforced Composites (FRCs) are instrumental in contemporary engineering as they offer a high weight-to-strength ratio as well as durability. They are, however, anisotropic and heterogeneous; as a result it is a major challenge to predict their mechanical properties when subjected to tensile and flexural loading. Conventional techniques such as experimental testing and finite element analysis are usually resource intensive, time consuming or simplistically constrained. In this review, we explored in detail how the data-driven machine learning (ML) models could overcome these constraints and thus constitute the paradigm shift. It is a synthesis of studies in the use of a broad range of ML techniques such as regression models, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs) and ensemble models for predicting the tensile and flexural properties of FRCs. The analysis shows that although models such as Gaussian Process Regression (GPR), Random Forest (RF) and state-of-the-art neural networks (NNs) have a very high predictive accuracy (often R² > 0.90), there are issues related to model generalization, data quality and modeling of physical principles. The paper ends with critical research gaps which include over-reliance on single-fiber systems and simulated data, while future directions include hybrid ML–physics models, multiscale modeling and exploration of a wider range of material and environmental variables to facilitate the development of safer and more efficient next-generation composites. Full article

The hole expansion ratio (HER) is a critical formability metric for advanced high-strength steels (AHSS) in automotive applications; however, its experimental determination is costly and time-consuming. This study presents a machine learning framework for HER prediction using physics-informed synthetic data generation to address data scarcity challenges. A dataset of 300 AHSS conditions was generated based on validated empirical relationships from the literature, incorporating chemical composition, microstructure fractions, and mechanical properties. Multiple machine learning algorithms were evaluated, with the optimized Gradient Boosting model achieving excellent predictive performance on an independent test set (R² = 0.80, RMSE = 5.81%, MAE = 4.93%). The feature importance analysis revealed physically meaningful rankings, with the ultimate tensile strength dominating (40.9%), followed by the bainite volume fraction (15.1%), martensite volume fraction (14.7%), and strain hardening exponent (12.4%). These rankings align with the established metallurgical understanding, thereby validating our synthetic data approach. The results demonstrate that machine learning models trained on physics-informed synthetic data can accurately predict the HER values with errors comparable to the experimental variability, providing a practical tool for accelerated AHSS design and optimization in automotive applications. Full article