Search Results (1,998)

Nowadays, there is an increasing interest in the development of novel bioresorbable membranes for Guided Bone Regeneration (GBR), and for this purpose, hydroxyapatite, from different sources, has been tested in combination with chitosan. This work details the production and the characterization of membranes of chitosan reinforced with hydroxyapatite derived from the bone of armoured catfish (Pterygoplichthys spp.), which is a widely available natural resource. The hydroxyapatite was characterized morphologically and chemically after the particles of hydroxyapatite were incorporated into a chitosan matrix. Then, the impact of adding hydroxyapatite particles into a matrix of chitosan on the roughness, mechanical properties, degradation, and cytotoxicity was evaluated. Subsequently, an in vivo test was carried out with the purpose of elucidating its guided bone regeneration activity, where the newly developed chitosan–hydroxyapatite membranes were implanted in rabbits with calvarial bone defects. The membranes of chitosan–hydroxyapatite presented a very rough surface morphology compared to the membranes of chitosan; moreover, the membranes of chitosan–hydroxyapatite showed superior mechanical tensile properties. The Masson’s trichrome staining analysis histologically demonstrated that the membranes of chitosan–hydroxyapatite enhanced the formation of a complete mineralized bone matrix in the calvarial bone defects. Finally, these findings confirm that the bone of armoured catfish (Pterygoplichthys spp.) is a viable, economic, and environmentally friendly source for isolating hydroxyapatite, which, combined with a matrix of chitosan, can be a suitable alternative to develop biocompatible GBR membranes. Full article

This study examined the impact of molybdenum disulfide (MoS₂) addition as a filler in epoxy composites reinforced with chopped basalt fibers (CBF), maintaining the basalt fiber content at a constant 40 wt. %. The investigation focused on physical, microstructural, mechanical, and sliding-wear properties. Testing revealed that tensile, impact, compressive, and flexural strengths improved with MoS₂ content from 0 to 8 wt. %. However, at 12 wt. % loading, these properties declined due to uneven dispersion and particle agglomeration. An increase in hardness was observed with rising MoS₂ content, with a maximum value of 98 HV at 16 wt. %. Wear testing was conducted using a Taguchi L16 orthogonal array, evaluating the effects of multiple parameters. The results indicated that MoS₂ content had the most significant influence on wear rate (WR), followed by applied load (P) and sliding distance (D), while sliding velocity (V) had minimal impact on specific wear rate (SWR) and coefficient of friction (COF). Scanning electron microscopy (SEM) was used to analyze wear mechanisms, and analysis of variance (ANOVA) confirmed the optimal conditions. Full article

Incorporating solid waste into polymeric matrices has proven effective in developing composites with enhanced mechanical and thermal properties. This study investigates a composite based on recycled high-density polyethylene (HDPE), reinforced with fine charcoal particles, assessing its thermal, microstructural, and density properties. Two processing methods (compression molding and extrusion) and four charcoal concentrations (0%, 5%, 10%, and 15 wt%) were evaluated. Thermal characterization was performed using thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR). The microstructure was analyzed through scanning electron microscopy (SEM) and X-ray diffraction (XRD), while the density was determined via X-ray densitometry. SEM revealed increased porosity with charcoal addition. The thermal properties and crystallinity of the composites were not significantly affected by variations in the manufacturing method or charcoal concentration. FTIR analysis identified characteristic peaks, while TGA indicated mass loss between 400 and 500 °C, with a maximum decomposition temperature of 487 °C. XRD confirmed the semicrystalline structure typical of HDPE. Thus, incorporating charcoal residues can reduce the use of fossil-based materials while providing a sustainable application for industrial waste. Full article

Biocomposites are defined as eco-friendly materials from an environmental point of view. Because of the importance of this class of materials, their study is important, especially in moist and heated conditions. In this sense, this work aims to evaluate the transient behavior of moisture absorption and mechanical performance of biocomposites composed of a matrix of high-density biopolyethylene (originated from ethanol produced from sugarcane) filled with curauá vegetable fiber and organophilic montmorillonite clay. For this purpose, dry biocomposites filled with organophilic montmorillonite clay and curauá fiber (1, 3, and 5 wt.%) were prepared using a hand lay-up technique and subjected to moisture absorption and mechanical (flexural and impact tests) characterizations at different times. The experiments were carried out at water bath temperatures of 30 °C and 70 °C. The results have proven the strong influence of chemical composition and temperature on the moisture absorption behavior of biocomposites across time. For a higher percentage of reinforcement on the polymeric matrix, a higher moisture migration rate was verified, reaching a higher hygroscopic equilibrium condition at 16.9% for 5 wt.% of curauá fiber and 10.25% for 5 wt.% of montmorillonite clay particles. In contrast, the mechanical properties of all of the biocomposites were strongly reduced with an increasing moisture content, especially at higher fiber content and water bath temperature conditions. The innovative aspects of this research are related to the study of a new material and its transient mechanical behavior in dry and wet conditions. Full article

This study investigates the thermal behavior and ignition dynamics of DC series arcs in a lab-scale photovoltaic (PV) system. The impacts of current magnitude, dynamic current variations, and electrode gap on electrode surface temperatures are analyzed, while ignition characteristics of common electrical materials (PC, PVC, XLPO, PPE, etc.) are investigated by analyzing critical time thresholds during the arc-induced combustion. Results show that electrode surface temperatures rise with increased current or larger electrode gaps, driven by the enhanced DC arc energy release. Dynamic current variations (increasing/decreasing) shift the balance between heat accumulation and dissipation, resulting in the nonlinear temperature evolution. Additionally, the peak temperature of the anode is about 50% higher than that of the cathode due to the electron flow-driven heat transfer and particle collisions. Notably, general electrical materials can be ignited successfully by stable DC arcs. The anode can ignite flame-retardant materials within 3 s, while the cathode takes a relatively long time to ignite, approximately 20 to 30 s. Besides, enlarged electrode gaps can induce a mutual reinforcement between arcs and flames, resulting in further stabilized arcs and intensified flames. This highlights potential elevated fire hazards as the connector gap increases due to the DC arc erosion. Full article

In order to solve the problem of damage identification of composite laminates during processing and service, a quantitative damage detection method based on swarm intelligence optimization was proposed for structural damage detection of curved composite laminates. Firstly, the structural damage element was defined by the method of reducing the elastic modulus of the element, and the modal parameters of the numerical model of the laminate under different damage conditions were obtained by analyzing the structural vibration characteristics. Secondly, the objective function was constructed from the vibration data, and the precise location and degree of damage were quantitatively calculated by the swarm intelligence optimization algorithm. In order to prevent the particles from falling into the local optimal, the boundary rebound strategy was used to process the boundary, and the MS operator was introduced to greatly accelerate the convergence speed of the algorithm. The numerical results indicate that without the influence of noise, the algorithm was not affected by the quantity, location or size of the damage and could effectively detect damage in curved fiber-reinforced composites, with the detection error rates being within 0.5%. After adding 1% and 5% noise to the frequency and vibration mode, respectively, the convergence speed of the algorithm slowed down, and the convergence times obviously increased. However, it could still accurately locate the damage, and the calculation error of the damage degree was less than 6%. Finally, the effectiveness of the proposed algorithm was verified through experimental tests. Full article

The uranium tailings mineral body is large and loose, and this could lead to radioactive contamination. Nuclides and heavy metals released from uranium tailings can be reduced through reinforcement treatment. The current study investigated the effect of CaCl₂ solutions with the same volume and different mass fractions on uranium tailing reinforcement under the premise of fixing the dosage of metakaolin, sodium hydroxide, sodium silicate, and the water reducer. It was found that, when 20.0% CaCl₂ was injected, the hydration reaction occurred more efficiently, and a more uniform gel polymer was produced. The degree of polymerization was higher, as well as the degree of aggregation near macropores. A large number of closed mesopores formed on the solidified surface. The pore structure of the solidified body was significantly improved; uranium ore particles had smaller gaps between them; the solidified body was better compacted; the leaching rates of uranium and its heavy metal ions were significantly reduced; and the compressive strength of the solidified body improved. In the triaxial test, the solidified body had a strength increase of 4.7 times. In addition to SEM, XPS, and XRD, the solidified samples were analyzed. In uranium slag solidified bodies, C-S-H and C-A-H gels and C-A-S-H and N-A-S-H polymers were formed. The gel polymers were wrapped around the uranium tailing particles, resulting in an 82.6% reduction in uranium leaching and a 57.2% reduction in radon exhalation. Full article

The application of ceramic particle-reinforced metal matrix composites (CPRMMCs) in the nuclear power sector is primarily dependent on their mechanical and thermal properties. A comprehensive understanding of the structure–property (SP) linkages between microstructures and macroscopic properties is critical for optimizing material properties. However, traditional studies on SP linkages generally rely on experimental methods, theoretical analysis, and numerical simulations, which are often associated with high time and economic costs. To address this challenge, this study proposes a novel method based on Materials Informatics (MI), combining the finite element method (FEM), graph Fourier transform, principal component analysis (PCA), and machine learning models to establish the SP linkages between the microstructure and thermodynamic properties of CPRMMCs. Specifically, FEM is used to model the microstructures of CPRMMCs with varying particle volume fractions and sizes, and their elastic modulus, thermal conductivity, and coefficient of thermal expansion are computed. Next, the statistical features of the microstructure are captured using graph Fourier transform based on two-point spatial correlations, and PCA is applied to reduce dimensionality and extract key features. Finally, a polynomial kernel support vector regression (Poly-SVR) model optimized by Bayesian methods is employed to establish the nonlinear relationship between the microstructure and thermodynamic properties. The results show that this method can effectively predict FEM results using only 5–6 microstructure features, with the R² values exceeding 0.91 for the prediction of thermodynamic properties. This study provides a promising approach for accelerating the innovation and design optimization of CPRMMCs. Full article

The influence of machining parameters on the generation of dust particles during the machining of carbon fiber-reinforced polymer (CFRP) composites remains insufficiently understood. These particles, which stay suspended in the air, pose a serious health risk to operators. This study examined the effects of cutting conditions—specifically cutting speed, feed per tooth, and depth of cut—and the impact of delaminations formed during CFRP drilling on the size, shape, and quantity of hazardous dust particles. Experiments were conducted using a commercially available uncoated cemented carbide cutting tool. The analysis of dust particle size and morphology, as well as the evaluation of delamination, was performed using microscopic and tomographic methods. The results demonstrate that reducing the cutting speed led to a decrease in particle size for the investigated CFRP material. Furthermore, it was observed that tool wear results in the generation of smaller particles. Simultaneous delamination during drilling was found to significantly affect the structural integrity of the composite material. Full article

The wear loss and frictional characteristics of magnesium-based hybrid composites reinforced with boron carbide (B₄C) particles and graphite filler were the main subjects of the investigation. Key parameters, including reinforcement content (0–10 wt%), applied load (5–30 N), sliding speed (0.5–3 m/s), and sliding distance (500–3000 m), were varied. Data-driven machine learning (ML) algorithms were utilized to identify complex patterns and predict relationships between input variables and output responses. Five distinct machine learning algorithms, Artificial Neural Network (ANN), Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting Machine (GBM), and Support Vector Machine (SVM), were employed to analyze experimental tribological data for predicting wear loss and coefficients of friction (COFs). The performance evaluation showed that ML models effectively predicted friction behavior and wear behavior of magnesium-based hybrid composites using tribological test data. A comparison of model performances revealed that the Gradient Boosting Machine (GBM) provided superior accuracy compared to other machine learning models in predicting both wear loss and the coefficient of friction. Additionally, feature importance analysis indicated that the graphite weight percentage was the most significant influence in predicting the coefficient of friction and wear loss characteristics. Full article

The use of recycled rubber particles, in the form of crumb rubber (CR), in concrete is gaining momentum due to its environmental benefits and potential for enhancing ductility. However, the strength degradation associated with CR incorporation remains a concern. This study investigates the compressive and axial behavior of reinforced concrete columns incorporating CR and hybrid steel fibers, comprising recycled steel fibers (RSFs) and copper-coated micro steel fibers (MSFs). Sixteen circular columns with varying CR contents (0–20%) and a constant fiber dosage (0.7% RSF and 0.3% MSF by volume) were cast and tested under axial compression. The results showed that CR reduced compressive strength, while the addition of hybrid fibers significantly improved strength, ductility, and energy absorption. Columns with up to 8% CR and fibers demonstrated comparable or superior load-bearing capacity to conventional concrete. Finite element modeling using ABAQUS software (Version 6.9) validated the experimental results, with numerical predictions closely matching load–displacement behavior and failure modes. This study highlights the potential of using CR and hybrid steel fibers in structural concrete to promote sustainability without compromising performance. Full article

CoCrFeNiMn high-entropy alloy (HEA) composite coatings with 0, 10, and 20 wt% TiC are synthesized through laser cladding technology, and their corrosion and wear resistance are systematically investigated. The X-ray diffraction (XRD) results show that with the addition of TiC, the phases of TiC and M₂₃C₆ are introduced, and lattice distortion occurs simultaneously (accompanied by the broadening and leftward shift of the main Face-Centered Cubic (FCC) peak). Scanning electron microscopy (SEM) reveals that the incompletely melted TiC particles in the coating (S2) are uniformly distributed in the matrix with 20 wt% TiC, while in the coating (S1) with 10 wt% TiC, due to gravitational sedimentation and decomposition during laser processing, the distribution of the reinforcing phase is insufficient. When rubbed against Si₃N₄, with the addition of TiC, S2 exhibits the lowest friction coefficient of 0.699 and wear volume of 0.0398 mm³. The corrosion resistance of S2 is more prominent in the simulated seawater (3.5 wt% NaCl). S2 shows the best corrosion resistance: it has the largest self-corrosion voltage (−0.425 V vs. SCE), the lowest self-corrosion current density (1.119 × 10⁻⁷ A/cm²), and exhibits stable passivation behavior with a wide passivation region. Electrochemical impedance spectroscopy (EIS) confirms that its passivation film is denser. This study shows that the addition of 20 wt% TiC optimizes the microstructural homogeneity and synergistically enhances the mechanical strengthening and electrochemical stability of the coating, providing a new strategy for the making of HEA-based layers in harsh wear-corrosion coupling environments. Full article

In this study, Zn-reinforced Mg matrix composite materials were produced via powder metallurgy by exposing them to ball milling at varying mechanical milling times. Following ball milling, the powders were cold-pressed under 600 MPa to obtain green compacts. The sintering process was carried out in a tube furnace under an argon atmosphere at 500 °C for 120 min. The effects of different milling times (2 h, 4 h, and 8 h) on particle and grain size, as well as the influence of sintering temperature and time on the microstructure, were investigated through SEM analysis. Phase evolution and changes in crystal planes occurring after ball milling were revealed by XRD analysis. SEM images show that Zn particles were homogeneously distributed within the matrix after 8 h of milling. Furthermore, it can be clearly stated that the highest hardness values were obtained from the samples produced after 8 h of milling. The sample group with the highest density, least mass loss, and lowest degradation rate was obtained from materials produced from 4 h ball milled powders. The intermetallic phase formed in the powder structure after 8 h of milling tends to reduce density and corrosion properties. The findings reveal that the addition of these alloys to pure Mg clearly enhances its hardness and density, while also imparting superior corrosion resistance. These combined improvements suggest that the developed materials hold strong potential for application in biomedical and clinical environments, where both mechanical strength and corrosion resistance are critical. Full article

Microplastic (MP) occurrence is a growing concern in environmental research, with significant attention focused on its presence in various ecosystems worldwide. While much research has centered on large lakes and water bodies, remote alpine lakes remain relatively unexplored in terms of microplastic occurrence. Studying microplastic occurrence in remote alpine lakes is important to understand the global spread of pollution, assess its impact on pristine ecosystems, and inform conservation efforts in these vulnerable environments. This study investigates microplastic presence in the sediment of Lake Cadagno, a remote alpine lake situated in the Piora Valley of southern central Switzerland. The lake has no effluents, and its meromictic nature means that the water on the bottom is not mixed with the water above, which can potentially lead to an enhanced accumulation of microplastics in the sediments that perpetuate in the lake system. Through sediment core sampling and analysis, we aim to identify the sources and deposition trends of microplastics in this pristine alpine environment. Our findings reveal the presence of microplastic within Lake Cadagno: in total, 186 MP particles were extracted from 756 cm³ of processed sediment (0.24 MP/cm³) with an average of 19.5 MP/sample (SD ± 11.8 MP/sample). Our results suggest that microplastics are predominantly attributable to localized sources associated with nearby human activities. The absence of synthetic fibers and the limited polymer types detected suggest a minimal contribution from atmospheric deposition, reinforcing the significance of local anthropogenic influences. Spatial clustering of microplastic particles near potential sources underscores the impact of surrounding land use activities on microplastic distribution. Overall, this study highlights the importance of addressing microplastic contamination even in remote and relatively unmodified ecosystems like Lake Cadagno, to elucidate the need for strict adherence to waste management and correct disposal actions to reduce the impacts of microplastic contamination. Full article

The growing use of unmanned aerial vehicles (UAVs) in diverse fields such as disaster recovery, rural regions, and smart cities necessitates effective dynamic drone network establishment techniques. Conventional optimization techniques like genetic algorithms (GAs) and particle swarm optimization (PSO) are weak when it comes to real-time adjustment to the environment and multi-objective constraints. This paper proposes a hybrid optimization framework combining genetic algorithms and reinforcement learning (RL) to improve the deployment of drone networks. We integrate Q-learning into the GA mutation process to allow drones to adaptively adjust locations in real time under coverage, connectivity, and energy constraints. In the scenario of large-scale simulations for wildfire tracking, disaster response, and urban monitoring tasks, the hybrid approach performs better than GA and PSO. The greatest enhancements are 6.7% greater coverage, 7.5% less average link distance, and faster convergence to optimal deployment. The proposed framework allows drones to establish strong and stable networks that are dynamic in nature and adapt to dynamic mission demands with efficient real-time coordination. This research has important applications in autonomous UAV systems for mission-critical applications where adaptability and robustness are essential. Full article