Search Results (24,903)

This research was motivated by the urgent need to address resource shortages and high energy costs in concrete production by replacing an energy-intensive traditional curing method with a new, more sustainable solution. By exploring solar heat treatment with composite binders and THACs, the study aimed to develop sustainable, cost-effective alternatives that harness renewable energy sources and optimize natural cement hydration processes for accelerated hardening. This article explores the potential application of solar energy in the production of precast concrete products using a composite binder. The effectiveness of the composite binder in solar thermal treatment of concrete using translucent heat-accumulating coatings is tested. The results of laboratory studies are presented, and the feasibility of using concrete based on composite binder at the laboratory scale for the production of concrete and reinforced concrete products, both with steaming and with solar thermal treatment, is established. The study of the structural features and basic physical and mechanical properties of hardened concrete under various conditions indicates that, under the investigated laboratory conditions, solar-thermally treated concrete exhibits physical and mechanical properties comparable to those of normally cured concrete. Laboratory studies confirmed the effectiveness of both steaming and solar heat treatment methods under controlled experimental conditions. Within the scope of the performed laboratory tests, the structure and properties of these concretes were comparable to those of normally cured concretes and, in several aspects, superior to those obtained under conventional steam curing regimes, which indicates the effectiveness of the described method, not only from the point of view of significant savings in fuel and energy resources. When producing products based on composite binders using solar thermal treatment, the consumption of the clinker portion of the binder is reduced by 50% (composition of the composite binder itself) and the consumption of conventional fuel during heat and moisture treatment is reduced by 70–100 kg per 1 m³ of concrete (reflecting process-level comparisons), which is of significant value for external energy demand. These findings confirm the technical feasibility and environmental advantages of the proposed method at the laboratory scale and highlight its potential for broader industrial application in precast concrete production. Full article

Garlic (Allium sativum) has antimicrobial and antioxidant properties. However, only the cloves are used from the bulb; the peels or husks are waste material with limited utility that nevertheless retain properties that can be exploited in other materials such as edible films or coatings. Chitosan is a widely used biopolymer, due its interesting properties. The same is true for alginate and pectin, which are polysaccharides that have interesting application areas; among the most common are film or coating materials in the food industry. Therefore, in this research, comprising the elaboration of films based on Chitosan-Pectin-Alginate (Q-P-A) reinforced with garlic husk (GH) particles, the films were characterized by Brookfield viscosity (the biopolymers solutions), Fourier Transform infrared Spectroscopy (FTIR), Dynamic mechanical analysis (DMA), and thermogravimetry (TGA). According to the results, the addition of GH caused a significant decrease in viscosity without altering the pseudoplasticity behavior and also generating physical interactions with the matrices; no chemical reaction byproducts were identified by FTIR. An increase in the reinforcing effect was identified in Q-GH films, whereas the opposite effect was observed in Q-P-A-GH films. In addition, no significant changes in the thermal stability were observed. Full article

This study, based on the characteristic that a small number of hydrocarbon-rich sub-sags control the majority of oil and gas reservoirs discovered in the HZ sag, establishes a foothold on the HZ26 sub-sag—the most prolific hydrocarbon-generating sub-sag in the HZ sag, focuses on WC formation of its peripheral structures, and discusses the hydrocarbon accumulation mechanism of WC formation in the HZ sag. Hydrocarbon charging periods were determined through an integrated analysis of homogenization temperatures of brine inclusions coexisting with hydrocarbon inclusions, stratigraphic burial and thermal history, and hydrocarbon generation stages of source rocks. A multiphase fluid charging physical simulation experiment was conducted to establish a covariant relationship between reservoir permeability and hydrocarbon charging pressure difference. Based on the residual pressure history and reservoir property evolution, a coupling relationship was established between the hydrocarbon charging driving force and resistance during hydrocarbon charging periods. The covariant relationship between reservoir permeability and hydrocarbon charging pressure difference and the coupling relationship between hydrocarbon charging driving force and resistance during hydrocarbon charging periods were then integrated to reconstruct the hydrocarbon charging process. The results reveal a mechanism for large-scale hydrocarbon charging and differential accumulation under the influence of overpressure. Full article

Decentralised manufacturing is expanding as digitally controlled fabrication tools become accessible to SMEs, independent operators, and community workshops outside traditional factory settings, but the resulting heterogeneous, autonomously operated network introduces systemic uncertainty that no central authority governs. This paper proposes a systems-theoretic framework in which Free and Open-Source Software (FOSS) governance acts as the structural interoperability layer of a distributed cyber–physical manufacturing system (CPS), and node-local digital twins—each hosting a machine learning (ML) disturbance estimator—provide local adaptive compensation without centralised data aggregation. A defining property of the architecture is automatic improvement propagation: learned corrections distribute via federated learning to structurally similar nodes without operator intervention, and the open, observable FOSS ecosystem enables advances in one fabrication modality to transfer to others through shared interface standards. The framework is applied analytically to three disturbance classes: regulatory restriction, technical process variability, and supply chain disruption. Across cases, the analysis shows how open modular interfaces and local adaptation preserve functional continuity under perturbations that would more strongly affect centralised architectures. The contribution is a unified mathematical basis for robustness analysis in decentralised manufacturing CPS and a foundation for future simulation and empirical validation. Full article

Large, shallow lakes lacking rooted aquatic vegetation are susceptible to wind-induced wave action that results in increased shear stress on the lake bottom, sediment resuspension and poor water clarity. The relationship between meteorological, hydrographical and sediment characteristics, and sediment dynamics has implications for internal phosphorus cycling and bioavailability, the frequency and duration of harmful cyanobacterial blooms, lake level management and restoration potential. In this study, a multi-parameter water quality sonde was deployed at various sites at the bottom of Utah Lake to measure water quality variables. Sediment cores were collected at each of the deployment sites and analyzed for common physical and chemical properties. Several machine learning regression techniques, including polynomial, decision tree, artificial neural network, and support vector machine, were applied to predict turbidity, a measure of water clarity and surrogate for sediment dynamics, using the observed explanatory variables wind speed and direction, fetch, water depth, sediment properties, algae, and cyanobacteria. The decision tree estimators, random forest and histogram-based gradient boosting had the best model performance, explaining 86–89% of the variability in turbidity when including all the explanatory variables. The artificial neural network estimator multi-layer perceptron and the polynomial regression models also performed well (81%), whereas the support vector machine estimator exhibited poor performance. Chlorophyll and phycocyanin, components of turbidity, were amongst the most important variables to the decision tree and artificial neural network models. Wind speed and water depth were also of high importance, which conforms with mechanistic explanations of sediment mobility caused by wave action and shear stress. Carbonate content was consistently a good predictor due to the calcareous nature of Utah Lake, whereas the importance of the other sediment properties was dependent on the machine learning technique applied. This case study demonstrated the potential for machine learning models to predict water clarity and has promise for more general applications to other shallow lakes and serves as a useful tool for lake management and restoration. Full article

One of the engineering applications inspired by nature is bio-inspired wings. The aerodynamic properties and autorotation characteristics of samara wing models have been studied extensively using both experimental and numerical methods. However, the three-dimensional flow behavior and angle of attack interaction around a natural samara wing are not yet fully understood. This study investigates the flow behavior around a samara wing model, with the aim of underlying physics and qualitatively analyzing the flow field, as well as the aerodynamic forces and stresses. Since the samara wing and the flow around it are three-dimensional, the difficulty of experimental investigation was taken into account, and the numerical analysis was performed using Computational Fluid Dynamics techniques. The results obtained from the numerical solution of the governing equations for three-dimensional turbulent flow were verified with experimental data. The calculations were performed by varying the angle of attack of the model wing between 0 and 50 degrees at 10-degree intervals. Depending on the angle of attack, the velocity field around the wing, surface pressure, and stress distributions, vortex structures formed on the wing and streamlines were analyzed, and the results were presented. This study and its results on this model may lead to the development and optimization of the model and its use in turbines or air vehicles. Full article

The distribution and spectral properties of colored dissolved organic matter (CDOM) in the northern Bay of Bengal were investigated in June 2016. Based on in situ data collected from 100 CDOM samples at 25 stations, the distribution characteristics of CDOM in the surface layer differed markedly from those at 30 m, 75 m, and 100 m. The CDOM spectral slope ($S_{350-500}$) exhibited a broad range, varying from 0.0026 to 0.0300 nm${}^{-1}$, and showed a significant negative correlation with the absorption coefficient $a_{\mathrm{CDOM}}\left(443\right)$. Analysis of salinity and temperature profiles revealed no obvious correlation between the distribution of $a_{\mathrm{CDOM}}\left(443\right)$ and these physical parameters. A comparative analysis with satellite-derived wind and current data indicated that elevated $a_{\mathrm{CDOM}}\left(443\right)$ values in the northeastern surface waters were primarily associated with the southwest monsoon. In contrast, $a_{\mathrm{CDOM}}\left(443\right)$ values in the lower mixed layer were mainly influenced by the combined effects of geostrophic and eddy currents. Full article

Electrohydrodynamic effects can significantly alter transport processes in reacting flows, even when the plasma is weakly ionized. However, predictive modeling of such plasma–flame interactions remains challenging due to the multiscale coupling among charge transport, fluid motion, and chemical kinetics. This study presents a charge-transport closure model to investigate electrohydrodynamic influences on laminar non-premixed flames. A two-dimensional computational framework in cylindrical coordinates is used to simulate plasma-assisted methane–air diffusion flames under weak electric-field conditions representative of practical combustion environments. To represent plasma–flow coupling in a computationally feasible yet physically consistent manner, a charge-transport formulation based on the drift–diffusion approximation is employed. The model solves transport equations for representative positive and negative charge carriers coupled with Poisson’s equation for the electric potential to obtain a self-consistent electric field. This formulation assumes a weakly ionized regime for low-temperature plasma-assisted combustion, in which neutral species dominate the mass and momentum transport, while ionization chemistry is simplified and charge transport primarily influences the flow through electrohydrodynamic body forces and Joule heating. Assuming a weak electric field, the steady flamelet model is applied, in which plasma effects primarily influence scalar transport and local thermal balance rather than inducing significant bulk ionization dynamics. The governing equations are discretized using a high-order compact finite-difference scheme that provides improved resolution of steep gradients in temperature, species concentration, and space-charge density near thin reaction zones. The canonical laminar flame model configuration was validated using the established laminar methane–air diffusion flame benchmark, and steady-state spatial profiles of key transport properties were evaluated. Two-dimensional analysis identified the discharge coupling location as an important factor. The application of discharge in the fuel-air mixing region leads to a clear restructuring of the flame. When the discharge is activated, electrohydrodynamic forcing and ion-driven momentum transfer produce a highly localized, columnar flame with sharp gradients and a confined reaction zone. Compared with the baseline case, the plasma-assisted flame localizes the OH-rich reaction zone, confines the high-temperature region into a narrow column, and enhances downstream H₂O formation. Full article

Polyester resin biocomposites containing biochar have attracted attention for improving mechanical strength and thermal stability while promoting sustainability. The pyrolysis temperature of biochar and its proportion in the polymer matrix are key factors affecting biocomposite performance. This study examined how biochar pyrolysis temperatures (400, 600, 800 °C) and incorporation levels (10, 20, 30 wt.%) influence the physical, chemical, mechanical, flammability, and morphological properties of polyester-based biocomposites. The samples were analyzed for density, water absorption, FTIR, XRD, flexural and tensile strength, ignition time, structural degradation, volumetric loss, and SEM microstructure. Biocomposites with 30 wt.% biochar produced at 800 °C showed the best mechanical properties, with a flexural strength of 95.3 MPa and an elastic modulus of 4417.4 MPa, representing increases of 14.5% and 45.7%, respectively, over the control. FTIR and XRD results revealed decreased aliphatic groups and increased aromaticity at higher pyrolysis temperatures, improving interactions between the matrix and biochar. These biocomposites also demonstrated enhanced thermal stability, with an ignition time of approximately 963 s, delayed structural degradation, and reduced volumetric loss (~19.3%). Overall, pyrolysis temperature and biochar content significantly influence the structural, mechanical, and thermal properties of polyester biocomposites, showing that biochar serves as a sustainable, performance-enhancing component in thermoset polymer matrices. Full article

Fibroin-based films represent a promising platform for sustainable and bio-derived materials. Existing literature has mainly focused on isolated molecules, plasticizers, or chemical cross-linkers, and the function of complex, multi-component natural extracts as structure-modulating agents in fibroin films remains poorly understood. In this study, edible films containing mulberry leaf extract (MLE; 2–8 wt%) and fibroin (8 wt%) were prepared by solution casting, and their structures were investigated using spectroscopic, morphological, thermal, mechanical, and barrier property analyses. The results reveal that MLE induces concentration-dependent changes in film performance through multicomponent, non-covalent interactions with the fibroin. An approximately 187% increase in tensile strength was achieved at high MLE concentration, confirming effective physical reinforcement. The water vapor transmission rate decreased markedly from 0.888 to 0.170 g·h⁻¹·m⁻², indicating an enhanced moisture barrier, whereas oxygen permeability increased at higher extract loadings, suggesting localized chain rearrangements. High optical transparency in the visible region was maintained (79.95–83.77%), while UV response was selectively altered with extract concentration. Overall, the 8MLE formulation exhibited the most balanced performance. This study demonstrates that plant-derived extracts can serve as effective natural modifiers for tailoring fibroin film properties without inducing crystallization, offering a sustainable strategy for designing bio-based and edible protein film systems. Full article

Resin 3D-printed molds are being increasingly favored for PDMS microfluidics across many disciplines. However, resin diversity, as well as secret manufacturer formulations, leads to a lack of standardization when using 3D printing for microscale applications. The impact of physical resin properties, both in its monomeric concoction and polymerized lattices at 100 µm or lower scales, needs quantification. We tested the performance of locally available resin formulations, isolating the impact of resin pigments and how it impacted the resin’s properties and performance. Lower resin viscosity improved feature fidelity (edge filleting < 25 µm) and improved resolution limit for recessed features, while cured polymer mechanical strength impacted the limit for positive mold features. We combined our findings to fabricate quality negative and positive mold structures in the mold and determined the best protocols associated with limitations during the fabrication of such structures. The methodologies in this study are expected to be widely applicable across various resin types and simplify the adoption of 3D printing protocols for specific feature fabrication in microscale molds for PDMS devices. Full article

This study investigated the impact of incorporating calcined eggshell and carbon nanotube (CNT) on the properties of polyvinyl alcohol (PVOH) blends. Prior to solution casting, eggshell waste underwent a calcination process and then the samples were prepared via solution cast method. Mechanical properties study revealed a significant enhancement in tensile strength and elongation at break with increasing loads of calcined eggshell and CNT. Higher tensile strength was observed with increasing CNT loading for PVOH blends added with 1 phr and 3 phr calcined eggshell, owing to the reinforcing role of CNT in the composite matrix. In contrast, the tensile strength at 0.3 phr CNT is lower than at 0.2 phr CNT due to CNT agglomeration, which weakens the interfacial adhesion with the PVOH matrix and hinders effective stress transfer during deformation. SEM images depicted well-dispersion and interaction effect of calcined eggshell particles and CNT particles at low loading levels. The good interaction effect between calcined eggshell and PVOH matrix (which both exhibit hydrophilic behaviour) is mainly attributed to the presence of hydrogen bonding in the polymer matrix, as proven in FTIR analysis. XRD analysis revealed significant peaks in the 2θ range of 19° to 21°, suggesting that increased amounts of calcined eggshells influenced the crystallite size of the original PVOH matrix. In summary, the addition of calcined eggshell and CNT at low loading levels markedly enhanced the mechanical, physical, and thermal properties of the composite material. Full article

The manuscript details the influence of high-temperature and high-shear processing, as well as radiation sterilization, on properties of bioresorbable and osteoconductive, patient-tailored alloplastic scaffolds for guided bone regeneration. Functionalized poly(l-lactide-co-d,l-lactide) copolymer filled with hydroxyapatite was used to produce two personalized implants for upper and lower jaw reconstruction via 3D printing. Morphology analysis (SEM, µCT), gel permeation chromatography, and thermal analysis quantified the effects of melt processing and sterilization on chain structure. Physical properties of sterilized parts, such as hardness and density, proved suitable for bone implants. Removal of the upper jaw implant after 4 months and of the lower jaw substitute after 18 months enabled monitoring of bioresorption and tissue regrowth over time. Gradual overgrowth of the implants with human tissue, initiated by the osteoconductive filler, was observed, along with time-dependent polylactide degradation, showing up to 92% molar mass reduction. The medical procedures confirmed safety, nontoxicity, non-allergenicity, and, most importantly, the tissue-forming properties of the polylactide-based formulation. Full article

The development of high-performance copper alloys requires balancing mechanical strength and electrical conductivity, properties that are often inversely correlated due to competing strengthening mechanisms. This study presents a comparative machine learning analysis of Cu-Ni-Si and Cu low alloys using a curated dataset of 1690 entries derived from the Gorsse et al. database, comprising 1507 samples with hardness measurements and 1685 samples with electrical conductivity data. Three ensemble-based regression algorithms, Random Forest, XGBoost, and Gradient Boosting, were trained to predict Vickers hardness (HV) and electrical conductivity (%IACS) from an augmented feature set encompassing alloy composition, thermomechanical processing parameters, missingness indicators, and physics-informed descriptors (valence electron concentration, atomic size mismatch, electronegativity difference, and Ni:Si atomic ratio). XGBoost achieved optimal performance for hardness prediction (R² = 0.8554, RMSE = 29.90 HV), while Gradient Boosting performed best for electrical conductivity (R² = 0.8400, RMSE = 5.96%IACS). Averaged tree-based feature-importance analysis identified valence electron concentration as the most influential predictor for hardness (39.9%), followed by aging temperature (11.2%), while Cu content dominated conductivity prediction (37.7%), followed by aging time (8.9%). Complementary SHAP analysis confirmed these trends while revealing directional relationships and nonlinear feature interaction effects. Composition-grouped cross-validation by unique alloy formula (K = 10) yielded substantially lower performance, with grouped CV R² = 0.438 for hardness and 0.293 for conductivity, indicating that generalization to unseen alloy formulations remains limited. The models were further applied for practical tasks, including property prediction for new alloy compositions, processing parameter optimization via differential evolution with metallurgical constraints (achieving hardness up to 293.9 HV or conductivity up to 45.7%IACS for the same base composition, with prediction intervals reported), and inverse design to identify alloy formulations meeting specified target properties. This work demonstrates the potential of interpretable machine learning to support copper alloy development by enabling rapid computational screening of the compositional and processing parameter space, subject to the generalization limitations identified herein. Full article

The limited availability of high-quality timber and the increasing demand for wood-based panels have encouraged the exploration of alternative and sustainable lignocellulosic resources. Rattan waste is abundant in Indonesia; however, its low mechanical strength and limited durability restrict its direct application in composite materials. This study investigated the effect of furfuryl alcohol (FA) modification and different adhesive systems on the performance of rattan-based particleboard. Rattan particles were immersed in FA for 24 h and used to produce particleboards (300 × 300 × 10 mm) bonded with phenol formaldehyde (PF), melamine formaldehyde (MF), and urea formaldehyde (UF) adhesives at a resin content of 12%. The boards were manufactured under controlled hot pressing conditions and conditioned for 14 days prior to testing. Furfurylation significantly improved dimensional stability by reducing moisture content, water absorption, thickness swelling, and leaching, with anti-swelling efficiency values ranging from 43.25% to 71.06%. Some selected mechanical properties, including internal bonding strength, hardness, and screw holding power, were also enhanced. However, the modification showed limited influence on the modulus of elasticity and, in some cases, reduced the modulus of rupture. Among the adhesive systems, MF-bonded boards exhibited the most balanced mechanical performance. Furfurylation also produced darker and more uniform board surfaces. These findings indicate that furfurylated rattan particleboards are suitable for non-structural and decorative applications. Full article