Search Results (518)

Synthetic dyes frequently persist through conventional wastewater treatment, motivating the use of advanced oxidation processes capable of breaking down these stable molecules. Metal-doped carbon dots (CDs) offer a tuneable platform for catalytic dye degradation in water, although their performance varies strongly with operating conditions. The aim of this work was to determine how temperature, H₂O₂ dosage, and pH influence the catalytic behaviour of Fe-, Cu-, Zn-, and Mg-doped CDs during the degradation of methylene blue (MB) and rhodamine B (RB), optimised using a Taguchi L27 orthogonal array design. Temperature and oxidant loading were the dominant factors: higher temperatures accelerated reactions through Arrhenius-type kinetics, while increasing H₂O₂ availability improved removal until excessive levels began to suppress •OH generation. Across all condition sets, apparent rate constants spanned 7.0 × 10⁻⁴–2.65 × 10⁻² min⁻¹, with t₅₀ values of 26–217 min and t₉₀ extending from ~86 min to >700 min; final decolourisation ranged from ~17% to nearly 100%. pH played a secondary role, mainly affecting dye speciation and surface adsorption. Dopant identity shifted the optimum operating region for each catalyst: Fe- and Cu-CDs achieved complete or near-complete removal of both dyes at pH 7 and 50 °C with relatively low H₂O₂ dosage (0.5–1.0 mL); Zn-CDs reached equivalent performance at pH 7 and 25 °C but required higher oxidant loading (1.5 mL of H₂O₂), reflecting their photo-induced rather than thermally driven activation mechanism; Mg-CDs performed comparably under the same conditions as Fe- and Cu-CDs. The resulting condition–catalyst map highlights the operating regimes that maximise efficiency while minimising chemical input, providing a practical framework for selecting carbon-dot-based catalysts for water treatment applications. Full article

This study investigates the thermo-mechanical response of geocell-reinforced concrete pavements through scaled model tests and three-dimensional finite element analyses. Static, thermal, traffic, and coupled temperature–loading tests were conducted to clarify the deformation evolution, strain distribution, and damage-related response of the reinforced structure. The results show that, under static loading, pavement settlement evolves through three stages, namely initial compaction, plastic development, and stable strengthening, indicating progressive mobilization of geocell confinement. Under thermal loading, slab strain exhibits pronounced spatial and temporal non-uniformity, and the slab center is identified as the thermally sensitive zone. Under coupled temperature–loading conditions, both strain and settlement show a non-monotonic response near 1.1–1.3 kN, suggesting a potential damage-initiation range. Post-test crack observations further provide direct qualitative evidence that local cracking damage occurred in the slab under representative loading conditions. Under traffic loading, permanent deformation accumulates with load repetitions and is highly sensitive to load amplitude, indicating a load-sensitive transition in cumulative deformation behavior rather than a definitive fatigue threshold. Numerical results further show that geocell reinforcement reduces central settlement by 17.4% relative to plain concrete pavement and by 7.6% relative to doweled pavement, while producing a smoother deflection basin and a more uniform stress distribution. Parametric analyses indicate that the optimum geocell height is approximately one-third of the slab thickness; beyond this range, the marginal reinforcement benefit decreases. Overall, the results demonstrate that geocell reinforcement can effectively improve load transfer, deformation compatibility, and thermo-mechanical stability of concrete pavements under the investigated conditions. Full article

Accurate structural analysis is essential for the design and optimization of aircraft wings; however, repeated high-fidelity finite element analysis (FEA) becomes computationally expensive when embedded in iterative design loops. This study presents a machine learning-based surrogate modeling framework for the efficient analysis and optimization of metallic commercial wing structures. A detailed Airbus A320-like wing model was developed and analyzed in ANSYS 2023 R1 under modal, static, and eigenvalue buckling conditions. The general dimensions of the Airbus A320 wing were used only as a reference; the resulting model is a conceptual benchmark rather than a one-to-one geometric replica or a validated digital twin of a specific aircraft wing. Using Latin Hypercube Sampling, 340 high-fidelity samples were generated, with 300 samples used for training and validation and 40 retained as an independent holdout set. The proposed Pyramidal Deep Regression Network (PDRN), a deep learning-based surrogate model whose architecture is automatically tuned using Bayesian Optimization, was benchmarked against Artificial Neural Networks (ANNs), Ensemble Learning, Support Vector Regression (SVR), and Gaussian Process Regression (GPR). On the unseen test set, the PDRN achieved the best overall predictive performance, with RMS errors of 0.8% for mass, 3.1% for the first natural frequency, 11.5% for load factor, and 11.4% for safety factor. To evaluate its practical utility, the trained PDRN was embedded into a PSO-based optimization framework for mass minimization under minimum safety factor, load factor, and first-frequency constraints. The surrogate-guided optimum was verified in ANSYS and remained feasible, yielding a mass of 10,485 kg, a first natural frequency of 1.4142 Hz, a load factor of 1.307, and a safety factor of 1.158. Compared with direct ANSYS in-the-loop optimization, the proposed workflow reached a comparable feasible design with substantially fewer high-fidelity evaluations. These results demonstrate that the PDRN provides an accurate and computationally efficient surrogate for rapid wing analysis and constraint-driven structural optimization. Full article

This study develops a unified multiscale–machine learning framework to interpret and predict thermo-mechanical wear regime transitions in MWCNT- and nanoclay-reinforced bio-based epoxy composites. A physics-informed master wear formulation integrating real contact mechanics, geometry-dependent shear transfer, interfacial adhesion energetics, and fracture-controlled matrix detachment was combined with interpretable machine learning analytics on a unified tribological dataset. In the CNT system, increasing loading from 0.1 to 0.4 wt.% enhanced interfacial adhesion energy density from 0.00813 to 0.01906 J/m², resulting in a monotonic reduction in the wear rate from 0.00918 to 0.00613 mm³/N·m (~33% reduction). In contrast, nanoclay exhibited an optimum behavior, with a minimum wear at 0.25 wt.% (0.000093 mm³/N·m; 7.9% reduction vs. neat clay baseline), followed by deterioration at a higher loading due to dispersion loss. The unified probabilistic regime classification of low-wear conditions (k < 0.007 mm³/N·m) achieved an ROC − AUC = 0.9256 and balanced accuracy = 94.3%, with thermo-mechanical severity identified as the dominant regime-switching driver. Reinforcement identity significantly modulated regime stability, confirming distinct shear transfer (Carbon Nano Tubes(CNT)) and confinement/tribofilm (clay) mechanisms within a common mathematical framework. By enabling the durability-oriented design of bio-based tribological systems and extending component service life through predictive stability mapping, this work contributes to resource-efficient materials engineering and reduced lifecycle waste, supporting Sustainable Development Goals SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). Full article

Enzyme-Assisted Extraction (EAE) in Natural Deep Eutectic Solvents (NaDES) was investigated as a green approach to extract bioactive compounds from the pseudofruit of Rosa canina L. Initially, the thermal stability of protease (Neutrase^®) was evaluated at different temperatures (30–80 °C) in the NaDES Choline Chloride: Glycerol (1:2 molar ratio) (ChCl: Gly) with 20% (w/w) water as a cosolvent and in a buffer solution of the same pH. Kinetic and thermodynamic analyses revealed that ChCl:Gly markedly enhanced enzyme stability, extending half-life by up to 13-fold at 30–50 °C by increasing the enthalpic barrier to deactivation. EAE in NADES parameters, including enzyme loadings and extraction time, were optimized based on total phenolic (TPC) and flavonoid content (TFC), yielding maximum values of 135.75 ± 0.33 mg GAE/g DW and 65.05 ± 0.58 mg CAE/g DW, respectively. Extracts obtained under optimal conditions exhibited enhanced antioxidant, antidiabetic (α-amylase and α-glucosidase inhibition), anti-aging (tyrosinase inhibition), and antibacterial (inhibition of Escherichia coli growth) activities, outperforming enzyme-free extracts in all cases. The optimum extract also significantly reduced A431 cell viability (27–40%, p < 0.05). Overall, EAE in NaDES improved both enzyme stability and extraction efficiency, offering a sustainable and effective alternative for producing bioactive plant extracts. Full article

This research aims to establish design guidelines for a cantilever triple-layer piezoelectric harvester (CTLPH) with tip mass and tip excitation, operating under resonance conditions. The guideline is derived by combining constitutive equations with Euler–Bernoulli beam theory to identify the effective parameters of the CTLPH and, subsequently, the storage voltage after rectification using a germanium diode bridge. The analysis shows that excitation frequency, piezoelectric coefficients, geometrical dimensions, and the mechanical properties of the layers all significantly influence CTLPH performance. The effects of storage capacitance and excitation frequency were experimentally validated through the design, fabrication, and testing of a prototype. Furthermore, the LTC3588 energy storage module was employed to store the generated charge from resonance motion. An advanced non-contact optical method was employed to determine the bending stiffness of the CTLPH. The output power after the energy storage module was measured across a range of resistive loads at frequencies near the resonance condition (f = 65 Hz). Results demonstrate that both excitation frequency and external resistance affect the maximum harvested power. The developed CTLPH achieved an optimum output power of 46.18 ± 0.98 μW at an external resistance of 3 kΩ, which is sufficient to supply micropower sensors. Full article

A bioash–cement composite binder was evaluated as a low-cement stabilization material for metal-contaminated soils, with emphasis on mechanical performance and long-term leaching behavior under field conditions. Two fine soil fractions from the Näsudden area (Skellefteå, Sweden), classified as hazardous (HS) and non-hazardous (NHS), were treated in laboratory trials to optimize binder composition. An optimum formulation containing 35 wt.% bioash and 5 wt.% cement (dry basis, relative to soil) improved unconfined compressive strength (UCS) to 696 kPa (HS) and 479 kPa (NHS) after 28 days and reduced leaching of Zn, Cd, Pb, and Co. Arsenic immobilization improved in HS but decreased in NHS, while Cu and Ni leaching increased, consistent with elevated pH and dissolved organic carbon (DOC) promoting soluble complexation. The optimized binder was then applied to a third soil (“Pilot soil”) and validated at pilot scale by treating 100 tonnes of soil and constructing a 2 m high noise barrier. Parallel laboratory tests on the Pilot soil yielded UCS values of 1000 kPa and confirmed effective retention of Zn and Cd, with generally good Pb stabilization, whereas As remained the most mobile element across soil types. Two-year field monitoring showed decreasing leachate concentrations of As, Cu, Ni, Pb, and Zn over time, and field samples exhibited improved Cu and Ni retention compared with laboratory results, suggesting progressive aging effects such as carbonation and mineral transformations. Overall, the results demonstrate that bioash–cement binders can produce mechanically stable treated materials suitable for low-load applications while reducing cement demand; however, performance is strongly controlled by soil-specific chemistry (notably DOC) and field execution (mixing and compaction), and further binder optimization is required to address arsenic mobility. Full article

This study investigates the joint characteristics of a 60-layered copper foil stack using linear vibration ultrasonic welding for lithium-ion pouch cell applications. With increasing demand for high-capacity electric vehicle batteries, ensuring the reliability of multilayer electrode joints is essential. Experiments were conducted by varying vibrational amplitude, welding time, and clamping pressure. Weld quality was analyzed based on indentation profiles, joint strength, and failure modes. Results revealed that optimal welding energy (500–900 J) produced well-formed joints without surface cracks or tearing. Excessive welding energy (>900 J) led to material thinning and interfacial failure. The maximum T-peel peak load of 138.7 N was obtained at the 30th joining interface under 25 µm amplitude, 0.8 s welding time, and 1.5 bar clamping pressure. Interface-dependent optimum conditions were observed, reflecting thickness–direction variations in deformation and bonding within the 60-layer stack. Indentation length and depth correlated linearly with welding energy. Failure modes transitioned from no adhesion to tearing and button-pull types. The findings provide guidelines for optimizing welding parameters for high-quality multilayer foil joints in battery manufacturing. Full article

This study employed a gradient heat treatment strategy to efficiently acquire microstructure parameters and establish the microstructure–hardness relationship in Ti-6Al-4V-1.5Zr-1.0Nb-0.5Mo alloy, addressing the knowledge gap in rapid optimization of heat treatment windows. Gradient solution treatment in the α + β region (859–928 °C) revealed that hardness reaches a minimum at a Vα_p/Vβ_t ratio of approximately 0.5, a condition to be avoided if aging is not applied. Subsequent aging at 500 °C, a common temperature for such alloys, highlighted the solution-treated sample at 908 °C as possessing high hardening potential, attributed to its high β_t fraction (Vβ_t = 70%) and sufficient retained β phase that promoted fine α_s precipitation. Gradient aging (502–590 °C) of this optimized microstructure further showed that peak hardness (>350 HV1, measured under a 1 kg load) was achieved at 502 °C and 551 °C, where the Vα_p/Vβ_t ratio remained near the optimal 3:7, and the precipitated refined α_s exhibited minimal width. The hardness of the bimodal microstructure is governed by two principal factors: the Vα_p/Vβ_t ratio (optimum near 3:7) and the precipitation efficiency of refined α_s from retained β phase. The gradient approach proves to be an effective high-throughput method for rapidly correlating heat treatment parameters with microstructure and properties, accelerating the design of heat treatments for titanium alloys. Full article

The aim of this study is to develop a digital twin hierarchy that fully examines the design and manufacturing processes of an automotive component for metal additive manufacturing. Initially, a lighter model was obtained that was more resistant to static, dynamic, and fatigue loads under various operating conditions. This step improved product strength and resulted in a 28.5% mass reduction. After the product was validated, the orientation of the part direction and the generation of support structures were performed for the manufacturing process. These processes were implemented with the criterion of minimizing production time. Finally, the manufacturing process was digitally implemented using the selective laser melting method and Ti6Al4V material. The design of the experiment was created using the three most frequently preferred values for each of the three important process parameters. After performing process simulations with thermomechanical analyses, Taguchi and ANOVA were applied to the process parameters. The optimum process parameters for layer thickness, hatch spacing, and scanning speed were found to be 50 µm, 120 µm, and 1200 mm/s, respectively. Full article

Tailings sand primarily consists of fine sand, silt, and other non-cohesive soil particles. Due to its persistent saturation, it exhibits a high susceptibility to liquefaction under dynamic loading or fluctuating groundwater conditions, potentially leading to engineering failures such as foundation instability and slope failure. This study focuses on a representative tailings pond located in the northwest region of China. A series of geotechnical laboratory tests were conducted to investigate the fundamental physical and mechanical properties of tailings sand. The test results indicate that moisture content increases gradually with depth and stabilizes beyond a certain depth, while dry density decreases approximately linearly with increasing depth. Owing to the presence of certain metallic minerals, the specific gravity of tailings sand materials is slightly higher than that of conventional standard sand. Particle-size analysis reveals that the non-uniformity coefficient ranges from 2.04 to 3.1, and the coefficient of curvature varies between 0.72 and 0.97, indicating poor gradation. Compaction testing determined an optimum moisture content of 13.59%, corresponding to a maximum dry density of 1.868 g/cm³. Soil-water characteristic curve analysis shows that larger particle sizes are associated with enhanced drainage capacity and lower suction requirements. An increase in dry density significantly reduces the drainage rate but has a limited effect on the matric suction at the residual stage. This research provides valuable insights into the engineering behavior of tailings sand, supports the assessment of its performance in foundation applications, and offers practical guidance for the stabilization of and improvement in tailings sand foundations. Full article

Thermal control represents one of the most important parameters influencing the safety and reliability of lithium-ion batteries, especially at high rates required for modern electric vehicles. The present paper investigates the thermal and electrothermal performance of a lithium iron phosphate (LiFePO₄) battery pack using a combination of experimental, statistical, and numerical methods. The 8S5P module was assembled and examined under load tests of 200, 400, and 600 W with and without active air-based cooling. The findings indicate that cooling reduced cell surface temperature by up to 10 °C and extended discharge time by 7–16% under various load conditions, emphasizing the effect of thermal management on battery performance and safety. In order to more systematically investigate the impact of ambient temperature and load, a RSM study with a central composite design (CCD; 13 runs) was performed, resulting in two very significant quadratic models (R² > 0.98) for peak temperature and discharge duration prediction. The optimum conditions are estimated at a 200 W load and an ambient temperature of 20 °C. Based on experimentally determined parameters, a finite-element simulation model was established, and its predictions agreed well with the measured results, which verified the analysis. Integrating measurements, statistical modeling, and simulation provides a tri-phase methodology to date for determining and optimizing battery performance under the electrothermal dynamics of varied environments. Full article

The identification of non-circular critical slip surfaces in slopes using metaheuristic algorithms remains a frontier challenge in geotechnical engineering. Such approaches are particularly effective for assessing the stability of heterogeneous slopes, including earth dams. This study introduces ODACO, a comprehensive program developed to determine the optimum cross-section of earth dams with berms. The program employs the Max–Min Ant System (MMAS), one of the most robust variants of the ant colony optimization algorithm. For each candidate cross-section, the critical slip surface is first identified using MMAS. Among the stability-compliant alternatives, the configuration with the most efficient shell geometry is then selected. The optimization process is conducted automatically across all loading conditions, incorporating slope stability criteria and operational constraints. To ensure that the optimized cross-section satisfies seismic performance requirements, an artificial neural network (ANN) model is applied to rapidly and reliably predict seismic responses. These ANN-based predictions provide an efficient alternative to computationally intensive dynamic analyses. The proposed framework highlights the potential of optimization-driven approaches to replace conventional trial-and-error design methods, enabling more economical, reliable, and practical earth dam configurations. Full article

The utilization of thermoelectric systems within internal combustion engines has emerged as a promising approach to recuperate a portion of the energy dissipated through exhaust gases. The objective of this study is twofold: firstly, to assess the heat recovery potential of a thermoelectric generator integrated into a diesel engine, and secondly, to elucidate the impact of varying operating conditions on the efficiency of heat recovery. For this purpose, the thermoelectric generator was mounted onto the exhaust pipe of a single-cylinder diesel engine featuring a common-rail fuel injection system with pilot injection and a displacement volume of 1.12 L. The calculations were conducted under 100% engine load at 1500 RPM engine speed and three different injection timing settings (−2, STD, and +2 °CA). The optimum heat recovery efficiency was determined to be 5.02%, which was achieved under the following conditions: B50 fuel, −2 °CA injection timing, 1500 RPM engine speed, and 100% engine load. Full article

Ethyl butyrate is a typical flavor ester with pineapple-banana scents, but the poor yield from natural fruits limits its feasibility in food and fragrance industries. In this study, dendritic fibrous nano-silica (DFNS) was hydrophobically modified with octyl groups (DFNS-C₈) to immobilize Candida antarctica lipase B (CALB) for solvent-free esterification of ethyl butyrate. The immobilized lipase CALB@DFNS-C₈, with the enzyme loading of 354.6 mg/g and the enzyme activity of 0.064 U/mg protein, achieved 96.0% ethyl butyrate conversion under the optimum reaction conditions where the molar ratio of butyric acid to ethanol was 1:3, with a reaction temperature and time of 40 °C and 4 h. Under the solvent-free catalytic reactions, CALB@DFNS-C₈ presented the maximum catalytic efficiency of 35.1 mmol/g/h and retained 89% initial activity after ten reuse cycles. In addition, the immobilized lipase can efficiently catalyze the synthesis of various flavor esters (such as butyl acetate, hexyl acetate, butyl butyrate, etc.) and exhibits excellent thermostability and solvent tolerance. A molecular docking simulation reveals that the hydrophobic cavity around the catalytic triad stabilizes the acyl intermediate and ensures the precise orientation of both acid and alcohol substrates. This work provides new insights into the sustainable production of flavor esters using highly active and recyclable immobilized lipases through rational carrier hydrophobization and structural confinement design. Full article