Search Results (985)

The efficient processing of nepheline ores and red mud requires mineralogical preconditioning strategies that enhance aluminum recovery while controlling the potassium behavior in alkaline systems. In this study, carbonate–hydrothermal activation of a nepheline ore–red mud mixture (1:1 mass ratio) was investigated as a targeted approach for phase redistribution prior to alkaline leaching. Thermochemical activation in NaHCO₃ solution at 260 °C resulted in the partial destabilization of primary aluminosilicate phases and the formation of secondary carbonate-bearing and hydroxide phases. Quantitative phase analysis revealed the formation of cancrinite (~19 wt.%) and boehmite (~2.8 wt.%), indicating dissolution–reprecipitation processes accompanied by aluminum redistribution and carbonate incorporation. A two-stage alkaline leaching process with controlled calcium addition was applied to evaluate the effect of phase transformation on the extraction performance. The activated material exhibited enhanced reactivity, resulting in an alumina recovery of 92%–94% and selective potassium removal of 82%–87%. The results demonstrate that carbonate–hydrothermal activation acts as an effective mineralogical preconditioning strategy, transforming structurally stable aluminosilicates into more reactive phases. The proposed approach provides a basis for improving the efficiency and selectivity of integrated alumina recovery from complex aluminosilicate raw materials and industrial waste. Full article

Accurate prediction of corrosion-fatigue crack growth rate in aluminum alloys is critical for the safety assessment of aerospace structures. Conventional empirical fracture-mechanic models often struggle to capture multiphysics coupling effects, whereas purely data-driven machine-learning models may lack physical interpretability and generalize poorly beyond the training distribution. To address this challenge, this study proposes a physics-guided knowledge-based XGBoost (KBXGB) model. Based on a comprehensive dataset comprising 2786 experimental records, Permutation Feature Importance was utilized to identify 11 key features, including the stress intensity factor range, stress ratio, frequency, and environmental parameters. The KBXGB framework learns the residual between physics-based empirical models (e.g., the Paris and Walker laws) and measured experimental data, recasting the complex nonlinear mapping into a correction of the systematic deviations of the physical models, thereby achieving deep integration of domain knowledge and data-driven learning. Test results demonstrate that the KBXGB model achieves a coefficient of determination (R²) of 0.9545 and a reduced Mean Relative Error (MRE) of 1.61% on the test set, outperforming standard XGBoost and traditional regression models. Crucially, in independent extrapolation validation, the standard XGBoost model failed (R² = 0.2858) with non-physical staircase artifacts, whereas the KBXGB model maintained high predictive fidelity (R² = 0.8646) and successfully reproduced physical crack growth trends. The proposed approach effectively mitigates the “black-box” limitations of machine learning in sparse data regions, offering a high-precision and physically robust tool for corrosion fatigue-life prediction under complex service conditions. Full article

The study addresses a selected issue in industrial cooling, that is, how to transport heat more efficiently when the process involves fiber spinning and extrusion, in which conventional fluids usually cannot work. We considered a ternary nanofluid that passed around a porous stretching cylinder and particularly considered the synergistic effect of quadratic thermal buoyancy, and the thermally generated double-diffusive heat and solute (TGDHS) effect. Through the Casson fluid model and considering the magnetic fields, radiations, and nonlinear chemical reactions, we reduced complex PDEs to simple ODEs. The results were evident using the BVP4C numerical method. Although in reality, magnetic fields and thermal radiation become a retarding force, the quadratic thermal buoyancy is the driving force behind accelerating the flow. An important trade-off that we discovered is that a heavier Casson fluid reduces heat and mass transfer. The addition of Nimonic 80A, AA7072, and AA7075 nanoparticles to ethylene glycol consistently enhances heat transfer, outperforming the base fluid by 7.8% even at low concentrations. While AA7072 and AA7075 drive significant increases of over 16%, Nimonic 80A offers a much more marginal contribution of 1.23%. Consequently, the Nusselt number is far more sensitive to the concentration of the aluminum alloys than to the Nimonic 80A. Finally, this work demonstrates that the most significant parameter in intensifying convective heat and mass transfer in such industrial systems is the strong forces of buoyancy. 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

Accurate prediction of mineral concentrations from sparse exploration data is important for resource estimation. This study evaluates hybrid prediction models combining machine learning (ML) and geostatistics to predict aluminum (Al) concentrations. Twelve hybrid configurations were generated by combining six ML backbones—Random Forest, XGBoost, AdaBoost, ResNet, U-Net, and Spatial Transformer Network—with Ordinary Kriging (OK) and Universal Kriging (UK). Model performance was evaluated using 10-fold spatial cross-validation (CV) to reduce spatial leakage, and hyperparameters were tuned by grid-search CV within the training folds. For the hybrid models, residual kriging was fitted using cross-fitted out-of-fold residuals to reduce optimistic bias and prevent information leakage. The results showed no consistent performance separation between OK and UK variants. More importantly, the effect of integration was backbone dependent rather than uniformly beneficial. RF-based predictions showed the strongest overall out-of-sample performance, whereas hybrid gains for other backbones were generally modest. After multiple-comparison correction, most differences between standalone and hybrid models were not statistically significant. These findings indicate that increasing model complexity through hybridization does not guarantee improved accuracy and highlight the importance of spatially explicit, bias-aware evaluation when selecting prediction strategies for mineral resource exploration. Full article

The crack-free welding of aluminum alloy is very significant in manufacturing lightweight components since aluminum alloys are susceptible to solidification cracking during welding. This work reviews the current understanding and advancements in the study of solidification cracking during welding aluminum alloys. Solidification cracks typically form near the end of the solidification process within the mushy zone, a region behind the molten pool where the material is partially solid and partially liquid. The complex high-temperature metallurgical behaviors in this zone play a crucial role in crack initiation. Research on the cracking resistance of filler materials has progressed significantly, offering valuable insights for mitigating cracks. The review covers the mechanisms of solidification cracking, influencing factors, and the cracking resistance of filler materials. It also highlights future research directions to deepen the understanding of metallurgical mechanisms and to support practical engineering solutions for controlling solidification cracks in welding aluminum alloys. Full article

Gypsum-based fire protection relies on thermally activated dehydration, where chemically bound water is released and evaporated, thereby providing an endothermic heat sink that delays heat penetration through assemblies. In parallel, inorganic hydrated salts are increasingly used as flame-retardant additives in gypsum-based systems to enhance heat absorption over specific temperature ranges. Fire simulation tools and performance-based fire engineering approaches require reliable kinetic data and reaction enthalpies that can be implemented as coupled thermal–chemical source terms. However, additive-specific kinetic datasets remain limited, particularly under restricted vapor exchange conditions representative of porous construction materials. This work investigates the thermal decomposition behavior and dehydration kinetics of Aluminum Trihydrate (Al(OH)₃, ATH), Magnesium Hydroxide (Mg(OH)₂, MDH), Calcium Aluminate Sulfate (3CaO·Al₂O₃·3CaSO₄·32H₂O, CAS), and Magnesium Sulfate Heptahydrate (MgSO₄·7H₂O, ESM) with emphasis on vapor-restricted conditions representative of confined porous systems. Differential scanning calorimetry (DSC) experiments were conducted at three heating rates (2, 10, and 20 K/min for MDH, CAS and ESM and 20, 40 and 60 K/min for GB-ATH) up to 600 °C using pinhole crucibles to simulate autogenous vapor pressure. The thermal analysis indicates that ATH and MDH exhibit predominantly single-step dehydration behavior, while ESM shows a complex multi-step mechanism. Although CAS presents a single dominant thermal peak in the DSC signal, the isoconversional analysis reveals a multi-stage reaction behavior, demonstrating that peak-based interpretation alone may be insufficient for such systems. Kinetic parameters were determined using both model-free (Starink) and model-fitting approaches in accordance with the recommendations of the Kinetics Committee of the International Confederation for Thermal Analysis and Calorimetry (ICTAC). All reactions were consistently described using the Avrami–Erofeev model as an effective phenomenological representation of the conversion behavior. The extracted kinetic triplets were validated through numerical simulations, showing good agreement with experimental conversion and reaction rate data. The resulting kinetic parameters and dehydration enthalpies provide a physically consistent dataset for the description of dehydration processes under restricted vapor exchange. These results support the development of thermochemical models for gypsum-based systems; however, their transferability to full-scale assemblies remains subject to validation under coupled heat- and mass-transfer conditions. Full article

Calcium–silicate–hydrate (C-S-H), the primary binding phase governing cement paste cohesion, undergoes progressive physicochemical transformation upon carbonation—a process that critically dictates concrete durability in atmospheric environments. When CO₂ penetrates the porous cement matrix, it triggers a cascade of degradation mechanisms: calcium leaching decalcifies the C-S-H structure, inducing polymerization of silicate chains from dimeric to longer-chain configurations, while concurrent precipitation of calcium carbonate and amorphous silica gel fundamentally reconstitutes the nanoscale architecture. These nanoscale alterations propagate to macroscopic property evolution, manifesting as initial strength and stiffness gains due to pore-filling carbonation products followed by eventual deterioration as the cohesive binding network deteriorates. This review synthesizes current understanding of carbonation-induced structural evolution, examining the coupled influences of environmental parameters—CO₂ concentration, relative humidity, and temperature—alongside C-S-H intrinsic chemistry (Ca/Si ratio, aluminum substitution, and alkali content) on reaction kinetics and material performance. However, significant knowledge gaps persist: predictive models for in-service carbonation rates remain elusive due to the disconnect between idealized laboratory conditions and the heterogeneous, cracked reality of field concrete; the causal linkage between nanoscale C-S-H alteration and macroscale cracking patterns along with physical performance is poorly resolved, and most mechanistic studies rely on synthetic C-S-H, neglecting the compositional complexity of real Portland cement systems. We further propose emerging protection strategies, including surface barrier coatings and low-carbon alternative binders (geopolymers, calcium sulfoaluminate cements, carbon-negative materials such as recycled cement), which demonstrate enhanced carbonation resistance. Future research priorities include developing effective coating barriers for carbonation protection, developing operando characterization techniques for real-time reaction monitoring, deploying machine learning algorithms to bridge atomistic simulations with structural-scale predictions, and establishing long-term field performance databases to validate laboratory-derived degradation models. Full article

Toxic aluminum (Al) and iron (Fe) alter the hormonal balance of plants, leading to metabolic disorders and growth inhibition. Plants adapt to abiotic stress by optimizing phytohormone biosynthesis. However, the impact of toxic Al and Fe on plant hormonal status is poorly understood. Pea cultivar Sparkle and its mutant E107 (brz), accumulating Al and Fe due to disfunction of metal transporter gene OPT3, were cultivated in hydroponics supplemented or not with 80 µM of AlCl₃ or 300 µM of FeCl₃. Root and shoot biomass of E107 decreased due to Al or Fe treatments approximately by 30%, whereas growth of Sparkle was not affected. The Al and Fe content in the roots and shoots of the metal-treated mutant was circa twice that of Sparkle. Treatment with Al and Fe reduced the content of nutrients (Ca, K, Mg, S) in roots and/or shoots in both genotypes. Compared with Sparkle, untreated E107 possessed lower IAA and higher ethylene and tZR contents in roots but lower GA3, DHZ and tZ content in shoots. Mutant E107 had: lower GA3 and ethylene but higher DHZ, tZ and tZR contents in Al-treated roots; higher ABA, SA, IAA, GA3, DHZ, and tZ contents in Al-treated shoots; lower ABA and SA but higher JA, GA3, DHZ and ethylene contents in Fe-treated roots; higher ABA, SA, IAA, GA3, DHZ, and tZ contents in Al-treated shoots; higher ABA, JA, and GA3 but lower ethylene and tZR contents in Fe-treated shoots. Metal toxicity mainly reduced the content of phytohormones in roots and increased it in shoots. Hormonal disturbances were more significant in E107 than in Sparkle, and the effect of Al was stronger than Fe. Thus, toxic Al and Fe lead to complex, metal- and organ-specific changes in the hormonal status of E107. Hormonal changes might be associated with both defense reactions and the toxic effects of metals on plants. Full article

Stellite alloys are widely used in the aerospace field owing to their excellent high-temperature strength and thermal fatigue resistance. However, with the rapid development of the aerospace industry, there is an urgent demand to further enhance the mechanical properties and thermal fatigue resistance of Stellite alloys. In the present study, we prepared a conventional CoCrW alloy (classified as a Stellite alloy) and a novel CoCrWAlNi alloy, which was formulated by introducing aluminum and nickel into the CoCrW matrix, using the direct laser deposition technique. Their microstructural characteristics, mechanical properties, and thermal fatigue performance were systematically investigated. The results indicated that the additions of aluminum and nickel contribute to stabilizing the γ-Co phase. Compared with the CoCrW alloy, the CoCrWAlNi alloy exhibited higher elongation at fracture. In situ observation was employed to study the initiation and propagation of thermal fatigue cracks. Meanwhile, the effects of oxidation on thermal fatigue resistance were analyzed through experimental tests and theoretical calculations based on the Huntz model. Finally, an optimized thermal fatigue mechanism tailored for cobalt-based alloys was established, which yields deeper insights into the failure mechanisms of these alloys under complex thermal-cycling fatigue conditions. Full article

Propolis is a resinous substance known for its bioactive compounds, such as phenolics and flavonoids, which exhibit antioxidant, antibacterial, and anti-inflammatory benefits. While it can be harvested from sources globally, it can also be derived from the native stingless Philippine kiwot bees. This study used microwave-assisted extraction (MAE) using ethanol, changing the solvent-to-sample ratio (5 mL/g, 10 mL/g, and 15 mL/g), solvent concentration (35% v/v, 65% v/v, and 95% v/v), and extraction time (60 s, 180 s, and 300 s), to determine their effects on the yield and phenolic and flavonoid contents. The extracted propolis was concentrated using rotary evaporator and freeze-dried. Phenolic content was determined through the Folin–Ciocalteu method, while flavonoid content was identified through aluminum chloride complex formation in methanol solution. Interactions between solvent-to-sample ratio and solvent concentration significantly affected yield, while extraction time did not. Results show that using MAE with ethanol as a solvent successfully extracted propolis. Lower solvent-to-sample ratios with increasing solvent concentration resulted in higher yields. Conversely, high solvent concentrations with increasing solvent-to-sample ratio decreases yield. The highest yield (0.144 g/g) was obtained with 5 mL/g solvent-to-sample ratio, 95% v/v solvent concentration, and 300 s extraction time. Phenolic and flavonoid contents of the propolis produced using the parameters resulting in the highest yield were determined to be 142.660 mg GAE/g propolis and 65.900 mg QE/g propolis, comparable to propolis extracted using traditional methods. Effects of extraction parameters were determined, which can further optimize the quantity and quality of propolis. Full article

Traditional craft education relies heavily on hands-on practice; however, novice learners often struggle with procedural complexity, material behavior, and the tacit knowledge typically transmitted through prolonged apprenticeship. This paper presents an integrated framework that combines semantic Knowledge Graphs (KGs), real-time Finite Element Method (FEM) simulation, and high-fidelity physically based rendering (PBR) to support the teaching, understanding, and preservation of traditional crafts. Craft processes are modelled as ontologically grounded KGs that capture tools, materials, actions, decision points, and common procedural errors through an extensible representation aligned with CIDOC-CRM. These semantic structures drive an interactive FEM-based simulation that enables learners to enact craft actions in a virtual environment while receiving predictive feedback and corrective guidance derived from expert-defined execution parameters. The resulting workpiece states are visualized using PBR techniques, providing perceptually accurate cues essential for assessing surface changes, deformation patterns, and material conditions. The methodology is embedded within an eLearning ecosystem that supports the generation of structured courses, multimodal exemplars, and instructional design informed by Cognitive Load Theory. A use case involving wood and aluminum carving demonstrates the system’s ability to simulate realistic tool–material interactions and produce visually interpretable outcomes. The results indicate that coupling executable semantic knowledge modelling with physically grounded simulation offers a viable pathway toward scalable, safe, and contextually rich craft training while supporting the long-term preservation of domain expertise. Full article

Addressing the thermal management challenges of prismatic aluminum shell lithium battery modules in electric vehicles under high-rate charge–discharge conditions, this study proposes a multi-objective optimization design method for integrated biomimetic liquid cooling plates. By integrating various highly efficient heat transfer structures from nature, including fractal-tree-like networks, leaf vein branching systems, and spider web radial distribution, a novel biomimetic liquid cooling plate topology was constructed. A multi-physics coupled numerical model considering electrochemical heat generation, thermal conduction, convective heat transfer, and thermal stress deformation was established. The NSGA-II algorithm was employed to globally optimize 12 design variables including channel geometric parameters, operating conditions, and structural dimensions, achieving collaborative optimization objectives of maximum temperature minimization, temperature uniformity maximization, pressure drop minimization, and structural lightweighting. The weight coefficients for the four optimization objectives were determined through the Analytic Hierarchy Process (AHP) with verified consistency (CR = 0.02 < 0.10), ensuring rational priority allocation aligned with automotive safety standards. The optimization results demonstrated that compared to the initial design, the optimal solution reduced the maximum temperature under 3C discharge conditions by 9.9% to 34.7 °C, decreased the temperature difference by 31.3% to 3.3 °C, lowered the pressure drop by 24.6% to 2150 Pa, reduced structural mass by 4.0%, and decreased maximum stress by 16.7%. Quantitative comparison with single biomimetic structures under identical boundary conditions showed that the integrated design achieved a 3.3% lower maximum temperature and 25.7% better flow uniformity than the best-performing single structure, demonstrating the synergistic advantages of multi-biomimetic integration. These synergistic performance improvements can be attributed to the hierarchical multi-scale architecture where fractal networks provide macro-scale flow distribution, leaf vein branches ensure meso-scale coverage, and spider web radials achieve micro-scale thermal matching. Long-term cycling tests conducted at 1C/1C rate with 25 ± 1 °C ambient temperature showed that the optimized design maintained a capacity retention rate of 92.3% after 1000 charge–discharge cycles, demonstrating excellent durability. The complex biomimetic channel structure can be fabricated using selective laser melting technology with minimum feature sizes below 0.3 mm, indicating promising manufacturing feasibility. The research findings provide theoretical guidance and technical support for the engineering design of high-performance battery thermal management systems. Full article

The complexity of an extrusion die profile is determined by its geometry. Various metrics such as the complexity index, shape factor, and form factor are used to quantify how geometric intricacy affects production costs, die life, energy consumption, product quality, and overall manufacturability. Bearing geometry plays a critical role in controlling metal flow and tool life in aluminum extrusion. In this study, a simulation-based investigation is performed to investigate the influence of bearing geometry on extrusion behavior using the finite element method. Two extrusion dies are examined: A single-cavity die with uniform bearing geometry and a dual-cavity die with controlled bearing geometry modification in one cavity. The results show that the bearing modification in the dual-cavity die causes severe flow imbalance, with exit velocity deviations. This imbalance leads to localized pressure amplification, increased thermal gradients, and stress concentration in critical die regions. In contrast, the single-cavity die, due to its simple geometry, exhibits uniform flow, stable pressure evolution, and low tool stress. These findings demonstrate the high sensitivity of multi-cavity extrusion dies to bearing geometry and highlight the importance of simulation-driven die design for achieving balanced flow and improved tool performance. Full article

Metal packaging materials remain fundamental across food, beverage, pharmaceutical, cosmetic, and technical sectors owing to their combination of mechanical robustness, total light and gas barrier performance, thermal resistance, and established recyclability. Aluminum alloys, tinplate, tin-free steel (TFS/ECCS), stainless steels, metal–matrix composites (MMCs), and metal–polymer or metal–paper laminates define distinct metal-based packaging architectures whose metallurgical and interfacial design governs forming behaviour, corrosion and migration pathways, coating integrity, and mechanical reliability. In this review, these architectures are examined from a materials- and systems-oriented perspective, linking composition, microstructure, processing routes, and surface engineering to functional performance across rigid, semi-rigid, and flexible formats. The analysis also considers the ongoing transition from bisphenol A (BPA)-based epoxy linings to BPA-free and hybrid coating chemistries, the use of nano-structured metallic and metal-oxide surfaces, and the role of composite laminates in which thin metallic foils are combined with polymeric or paper-based structural layers. These material and architectural aspects are discussed together with safety, regulatory, and circularity considerations that increasingly influence the design and selection of metal-based packaging. Ion migration, coating degradation, and corrosion under realistic storage environments are considered in relation to EU, FDA, ISO, and sector-specific requirements, while attention is also paid to the contrast between well-established closed-loop recycling infrastructures for aluminum and steel and the more complex end-of-life management of coated metals and multilayer laminates. The review provides a unified framework connecting materials selection, metallurgical design, processing, performance, regulatory compliance, and sustainability in metal-based packaging systems. Applications spanning consumer goods, pharmaceuticals, cosmetics, and advanced electronics are integrated to support an overall understanding of how metallic and hybrid metal-based architectures underpin functional reliability and life-cycle sustainability. Full article