Search Results (237)

This paper discusses the potential of using steel fiber to produce self-compacting high-strength concrete. The effects of water–binder ratio and mortar and steel fiber content on the workability and mechanical properties of high-performance concrete were studied. The working performance of cementitious materials was evaluated by a slump expansion test, T₅₀₀, L-shaped instrument, U-shaped instrument, and V-shaped funnel. The mechanical properties were evaluated by compressive strength and flexural strength. The results show that when the compressive strength of self-compacting high-strength concrete with steel fiber is 90 MPa, the optimum mix ratio is a water–binder ratio of 0.22, sand ratio of 46%, and steel fiber content of 0.3%. When the steel fiber content is 0.3%, the compressive strength of the time can be increased by more than 4%, and the flexural strength can be increased by more than 5%. When the steel fiber content is 0.6% to 0.9%, the compressive strength of the specimen can be increased by more than 10%, and the flexural strength can be increased by more than 7%. However, with the increase in steel fiber content, self-compacting concrete becomes less and less dense, and the bond strength becomes lower and lower. When the water–binder ratio is 0.20, the fluidity of self-compacting concrete is poor, and the forming effect is not good. When the water–binder ratio is 0.24, the working performance of self-compacting concrete is better, but the cohesion is poor, and it can easily produce segregation. When the water–binder ratio is 0.22, the working performance of self-compacting concrete can be the best, and the strength of concrete is higher and more stable. The optimum sand ratio is 46%. At this time, the compressive strength and flexural strength of self-compacting concrete are the largest, and the working performance is also the best. When the sand ratio is lower than the optimum sand ratio, the self-compacting concrete will produce segregation. When the sand ratio is higher than the optimum sand ratio, the fluidity of self-compacting concrete is poor. This study provides insights into the potential for large-scale and high-value utilization of steel fibers and the development of cost-effective ways to reduce the carbon footprint of self-compacting concrete production. Full article

During electron beam cold hearth melting (EBCHM) of Ti-6wt%Al-4wt%V titanium alloy, aluminum volatilization causes compositional segregation in the ingot, significantly degrading material performance. Traditional methods (e.g., the Langmuir equation) struggle to accurately predict aluminum diffusion and compensation behaviors, while computational fluid dynamics (CFD), although capable of resolving multiphysics fields in the molten pool, suffer from high computational costs and insufficient research on segregation control. To address these issues, this study proposes a CFD-machine learning (backpropagation neural network, CFD-ML(BP)) approach to achieve precise prediction and optimization of aluminum segregation. First, CFD simulations are performed to obtain the molten pool’s temperature field, flow field, and aluminum concentration distribution, with model reliability validated experimentally. Subsequently, a BP neural network is trained using large-scale CFD datasets to establish an aluminum concentration prediction model, capturing the nonlinear relationships between process parameters (e.g., casting speed, temperature) and compositional segregation. Finally, optimization algorithms are applied to determine optimal process parameters, which are validated via CFD multiphysics coupling simulations. The results demonstrate that this method predicts the average aluminum concentration in the ingot with an error of ≤3%, significantly reducing computational costs. It also elucidates the kinetic mechanisms of aluminum volatilization and diffusion, revealing that non-monotonic segregation trends arise from the dynamic balance of volatilization, diffusion, convection, and solidification. Moreover, the most uniform aluminum distribution (average 6.8 wt.%, R² = 0.002) is achieved in a double-overflow mold at a casting speed of 18 mm/min and a temperature of 2168 K. Full article

Peanut (Arachis hypogaea L.) is a globally important oilseed cash crop, yet its limited genetic diversity and unique reproductive biology present persistent challenges for conventional crossbreeding. Traditional breeding approaches are often time-consuming and inadequate, mitigating the pace of cultivar development. Essential for double fertilization and programmed cell death (PCD), DUF679 membrane proteins (DMPs) represent a membrane protein family unique to plants. In the present study, a comprehensive analysis of the DMP gene family in peanuts was conducted, which included the identification of 21 family members. Based on phylogenetic analysis, these genes were segregated into five distinct clades (I–V), with AhDMP8A, AhDMP8B, AhDMP9A, and AhDMP9B in clade IV exhibiting high homology with known haploid induction genes. These four candidates also displayed significantly elevated expression in floral tissues compared to other organs, supporting their candidacy for haploid induction in peanuts. Subcellular localization prediction, confirmed through co-localization assays, demonstrated that AhDMPs primarily localize to the plasma membrane, consistent with their proposed roles in the reproductive signaling process. Furthermore, chromosomal mapping and synteny analyses revealed that the expansion of the AhDMP gene family is largely driven by whole-genome duplication (WGD) and segmental duplication events, reflecting the evolutionary dynamics of the tetraploid peanut genome. Collectively, these findings establish a foundational understanding of the AhDMP gene family and highlight promising targets for future applications in haploid induction-based breeding strategies in peanuts. Full article

The microstructure evolution and hardness variations of as-fabricated and recrystallized 12Cr oxide dispersion strengthened (ODS) steel after dual-ion (6.4 MeV Fe³⁺ and energy-degraded 1 MeV He⁺) irradiation at 973 K up to 10.6 displacements per atom (dpa) at peak damage and 8900 appm He are investigated. Results show that the oxide particles slightly shrink in the as-fabricated specimen, while they are stable in the recrystallized specimen. Furthermore, larger helium bubbles are trapped at the grain boundaries in the as-fabricated specimen, and the size of helium bubbles in the grains is almost the same for both as-fabricated and recrystallized specimens, indicating that reduction of grain boundaries would reduce the potential nucleation sites and suppress the helium segregation. Moreover, no obvious hardening occurs in the as-fabricated specimen, whereas the hardness increases a little in the recrystallized specimen. Based on the barrier model, the barrier strength factor of helium bubbles is calculated. The value is 0.077, which is much smaller and suggests that helium bubbles seem not to significantly induce irradiation hardening. Full article

Cotton is one of the most valuable non-food agricultural products in the world. However, cotton production is often hampered by the invasion of disease. In most cases, these plant diseases are a result of insect or pest infestations, which can have a significant impact on production if not addressed promptly. It is, therefore, crucial to accurately identify leaf diseases in cotton plants to prevent any negative effects on yield. This paper presents a hybrid deep learning approach based on Bidirectional Encoder Representations from Transformers with Residual network and particle swarm optimization (BERT-ResNet-PSO) for detecting cotton plant diseases. This approach starts with image pre-processing, which they pass to a BERT-like encoder after linearly embedding the image patches. It results in segregating disease regions. Then, the output of the encoded feature is passed to ResNet-based architecture for feature extraction and further optimized by PSO to increase the classification accuracy. The approach is tested on a cotton dataset from the Plant Village dataset, where the experimental results show the effectiveness of this hybrid deep learning approach, achieving an accuracy of 98.5%, precision of 98.2% and recall of 98.7% compared to the existing deep learning approaches such as ResNet50, VGG19, InceptionV3, and ResNet152V2. This study shows that the hybrid deep learning approach is capable of dealing with the cotton plant disease detection problem effectively. This study suggests that the proposed approach is beneficial to help avoid crop losses on a large scale and support effective farming management practices. Full article

Bismuth ferrite (BiFeO₃, BFO) is a promising multiferroic material, but its optoelectronic potential is limited by a wide bandgap and charge recombination. Here, we report the sol–gel synthesis of Co-doped BiFeO₃/Bi₂₅FeO₄₀ heterostructured nanopowders (x = 0.07, 0.15) alongside pristine BFO to explore Co doping and phase engineering as strategies to enhance their functional properties. Using X-ray diffraction (XRD) with Rietveld refinement, Fourier-transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FE-SEM), UV-Vis spectroscopy, and dielectric analysis, we reveal a biphasic structure (rhombohedral R3c and cubic I23 phases) with tuned phase ratios (~73:27 for x = 0.07; ~76:24 for x = 0.15). Co doping induces lattice strain and oxygen vacancies, reducing the bandgap from 1.78 eV in BFO to 1.31 eV in BFO_0.15 and boosting visible light absorption. Dielectric measurements show reduced permittivity and altered conduction, driven by [Co²⁺-V₀^••] defect dipoles. These synergistic modifications, including phase segregation, defect chemistry, and nanoscale morphology, significantly enhance optoelectronic performance, making these heterostructures compelling for photocatalytic and photovoltaic applications. Full article

The phase composition and morphological characteristics of eutectic carbides are key factors affecting the wear resistance and fatigue life of high-speed steel. In this study, a combination of experimental characterization and thermodynamic calculations was used to systematically reveal the dynamic regulation mechanism of cooling rate on eutectic carbides in M2Al high-speed steel. The results indicate that within a cooling rate range of 5 to 225 °C/min, the steel always contains a small amount of face-centered cubic-structured MC-type eutectic carbides and a large number of hexagonal close-packed structured M₂C-type eutectic carbides. The three-dimensional morphology of MC-type eutectic carbides is smooth and rod-like, and is insensitive to the cooling rate, while the three-dimensional morphology of M₂C-type eutectic carbides evolves from lamellar to dendritic with an increasing cooling rate. The increase in cooling rate significantly reduces the average size of eutectic carbides, increases the total area fraction, and improves the distribution uniformity. Additionally, the increase in cooling rate also promotes the significant refinement of secondary dendrites in M2Al high-speed steel, and the relationship between secondary dendrite arm spacing and cooling rate is ${\lambda }_{SDAS}=149.42\cdot {C_R}^{-0.39}$. Finally, combining thermodynamic calculations with kinetic analysis, this study found that the formation of eutectic carbides is dominated by the segregation of elements such as V, Mo, and C during the final stage of solidification, while the chemical composition and three-dimensional morphological evolution of M₂C-type eutectic carbides are synergistically controlled by the diffusion and competitive growth of elements such as W, Mo, and C in austenite. This study provides a theoretical basis for the solidification process and eutectic carbide control of M2Al high-speed steel. Full article

First-principles calculations were employed to study the solution and diffusion properties of hydrogen (H) at the Si-rich and C-rich $\Sigma$3(111)[1$\overline{1}$0] ($\Sigma 3_{Si}$ and $\Sigma 3_C$) and $\Sigma$9(221)[1$\overline{1}$0] ($\Sigma$9) grain boundaries (GBs) in 3C-SiC. We constructed GBs of varying sizes and calculated their formation energies and excess volumes to identify the stability of GBs. The $\Sigma$9 GB is more stable and has a relatively open structure compared with the $\Sigma$3 GB. The solution energies of H at the $\Sigma 3_{Si}$, $\Sigma 3_C$ and $\Sigma$9 GBs are significantly reduced to 1.46, 2.30 and 1.47 eV, respectively. These values are much lower than that in the bulk. The negative segregation energies indicate that H is more likely to reside at the GBs rather than in the bulk. The diffusion energy barrier of H in the $\Sigma 3_C$ GB is as high as 1.27 eV, whereas in the $\Sigma 3_{Si}$ GB and $\Sigma$9 GB, the barriers are as low as 0.42 eV and 0.28 eV, respectively. These results suggest that H migration will be suppressed in the $\Sigma 3_C$ GB but promoted in the $\Sigma 3_{Si}$ and $\Sigma$9 GBs. The differences in H diffusion behavior among these three GBs may be attributed to the relatively more open structures of the $\Sigma 3_{Si}$ and $\Sigma$9 GBs compared with the $\Sigma 3_C$ GB. These results are essential for understanding the diffusion mechanism of H and its retention behavior in SiC. Full article

The disposal of orchard garbage (including pruning branches, fallen leaves, and non-biodegradable materials such as pesticide containers and plastic film) poses major difficulties for horticultural production and soil sustainability. Unlike general agricultural garbage, orchard garbage often contains both biodegradable organic matter and hazardous pollutants, which complicates efficient recycling. Traditional manual sorting methods are labour-intensive and inefficient in large-scale operations. To this end, we propose a lightweight YOLOv7-based detection model tailored for the orchard environment. By replacing the CSPDarknet53 backbone with MobileNetV3 and GhostNet, an average accuracy (mAP) of 84.4% is achieved, while the computational load of the original model is only 16%. Meanwhile, a supervised comparative learning strategy further strengthens feature discrimination between horticulturally relevant categories and can distinguish compost pruning residues from toxic materials. Experiments on a dataset containing 16 orchard-specific garbage types (e.g., pineapple shells, plastic mulch, and fertiliser bags) show that the model has high classification accuracy, especially for materials commonly found in tropical orchards. The lightweight nature of the algorithm allows for real-time deployment on edge devices such as drones or robotic platforms, and future integration with robotic arms for automated collection and sorting. By converting garbage into a compostable resource and separating contaminants, the technology is aligned with the country’s garbage segregation initiatives and global sustainability goals, providing a scalable pathway to reconcile ecological preservation and horticultural efficiency. Full article

GeSn alloys are among the most promising materials for the fabrication of high-efficiency silicon-based light sources. However, due to the tendency of Sn to segregate to the surface during growth, it is challenging to achieve a high Sn concentration while maintaining high-quality GeSn alloys. Both theoretical and experimental studies have confirmed that non-substitutional Sn defects (VSnV) are the primary driving factors in Sn surface segregation. However, there is a discrepancy between existing theoretical and experimental findings regarding the variation in VSnV concentration with total Sn concentration. To clarify this issue, we first prepared GeSn materials with varying Sn concentrations using molecular beam epitaxy (MBE) and subjected them to annealing at different temperatures. Subsequently, we characterized the VSnV concentration and Sn surface segregation. The results indicate that a higher total Sn concentration and temperature lead to an increased VSnV concentration, and the proportion of VSnV relative to the total Sn concentration also increases, which is consistent with existing theoretical research. To explain these phenomena, we employed first-principles calculations based on density functional theory (DFT) to investigate the effect of varying the total Sn concentration on the formation of substitutional Sn (Sn_s) and VSnV in GeSn alloys, while simultaneously studying the migration kinetics of Sn atoms. The results demonstrate that as the total Sn concentration increases, the formation of Sn_s becomes more difficult, while the formation of VSnV becomes easier, and Sn atoms exhibit enhanced migration tendencies. The analysis of binding energies and charge density distribution maps reveals that this is due to the weakening of Ge-Sn bond strength with increasing Sn concentration, whereas the binding strength of VSnV exhibits the opposite trend. These findings demonstrate excellent agreement with experimental observations. This study provides both theoretical and experimental references for GeSn material growth and VSnV defect control through a combined theoretical–experimental approach, offering significant guidance for enhancing device performance. Full article

Composite grids serve as reinforcement in concrete structures, offering alternatives to conventional steel reinforcement. These grids can be fabricated from various materials, including synthetic polymers, metals, and natural fibers. This study explores the use of composite grids as lateral confinement of self-compacting concrete (SCC) cylinders and examines their impact on the failure mode under axial compression. In the experiment, the types of grids and mesh shapes used were plastic grids of diamond mesh (PGD) and regular mesh (PGT), metallic grids of diamond mesh (MGD) and square mesh (MGS), vegetable grids of Alfa fiber mesh, 10 × 10 mm (VGAF-1) and 20 × 20 mm (VGAF-2), and vegetable grids of date palm fibers (VGDF). The binder of SCC mixtures incorporated 10% marble powder as a partial replacement for ordinary Portland cement (OPC). SCC mixtures were tested in the fresh state by measuring the slump flow diameter, V-funnel flow time, L-box blocking ratio, and segregation index. Cylinders with a diameter of 160 mm and a height of 320 mm were made to assess the mechanical properties of hardened SCC mixtures under axial compression. The results indicate that most of the confined cylinders exhibited an increase in ductility compared to unconfined cylinders. Grid types MGD and PGD provided the best performance, with ductility increases of 100.33% and 96.45%, respectively. VGAF-2 cylinders had greater compressive strength than cylinders with other grid types. The findings revealed that the type and mesh shape of the grids affects the failure mode of confined cylinders, but has minimal influence on their modulus of elasticity. This study highlights the potential of lateral grid confinement as a technique for rehabilitating, strengthening, and reinforcing weaker structural concrete elements, thereby improving their mechanical properties and extending the service life of building structures. Full article

The high-temperature performance of 10CrNi2Mo3Cu2V steel is critically governed by the distribution of Cu-rich phases. This study systematically investigated the evolution of solute redistribution, Cu-rich phase precipitation, microstructural transformations, and mechanical properties in 10CrNi2Mo3Cu2V alloy under varying aging temperatures. Advanced characterization techniques, including atom probe tomography (APT) and transmission electron microscopy (TEM), were employed to analyze microstructural features and phase formation in both as-built and heat-treated specimens. The key findings reveal that copper atom segregation initiates at a tempering temperature of 350 °C. Upon increasing the temperature to 450 °C, extensive precipitation of nanoscale copper clusters is observed. Temperatures exceeding 450 °C trigger the formation of ε-Cu phases, which undergo subsequent coarsening. Notably, these copper clusters and Cu-rich precipitates act as dislocation pinning sites, promoting crack nucleation and propagation, thereby markedly degrading the alloy’s impact energy absorption capacity. The critical diameter for Orowan mechanism-governed strengthening by Cu-rich phases is determined to be ~6 nm, while the average diameter of matrix-penetrating Cu-rich particles is approximately 1.46 nm. Quantitative analysis demonstrated that the combined contributions of the Orowan bypass mechanism and particle-cutting mechanism yield a strength enhancement of ~219 MPa, which exhibits excellent agreement with experimentally measured strength increments. These results provide critical insights into the interplay between microstructural evolution and mechanical degradation in precipitation-strengthened steels under thermal exposure. Full article

In this study, the results of transmission electron microscopy (TEM) examinations of neutron-irradiated (2.5 dpa at 150 °C, 350 °C, and 450 °C) CuCrZrV and ODS(Y₂O₃)-Cu alloys are presented. These materials were developed for application as heat sink materials in fusion technology. This study includes TEM imaging and quantitative analysis of neutron radiation-induced defects such as dislocation loops and voids as well as the determination of the conditions for their formation. It was found that dislocation loops of a₀½⟨110⟩ type form in both alloys at all irradiation temperatures. The formation of voids in CuCrZrV alloy is effectively suppressed. The neutron irradiation causes a redistribution of Cr, Zr, and V in the CuCrZrV alloy. A particular focus was on the investigation of the distribution of the transmutation products Ni and Zn. Ni tends to segregate at the Cr-rich clusters and forms a shell around them, while Zn is evenly distributed. Full article

Stainless steel wires exhibit excellent mechanical properties and are widely used in engineering applications. This study fabricates 15 μm stainless steel wires for potential integration into wire bonding technology for electronic packaging. The research explores the microstructural characteristics, electrical conduction mechanisms, and ball formation behavior of ultra-fine stainless-steel wires to assess their feasibility for wire bonding applications. Results indicate that both 15 μm and 30 μm stainless steel wires exhibit elongated grains with outstanding tensile strength and hardness. Compared to the 30 μm wires, the 15 μm wires undergo more pronounced work hardening, leading to higher tensile strength and resistance. This study investigates the differences between vacuum and electrified annealing processes to address the work hardening and ductility issues in stainless steel wires. Results confirm that the hardness of the original wire significantly decreases after vacuum annealing at 780 °C for 15 min. Furthermore, using the derived equation, $T={\displaystyle \frac{IV}{2.3085\times {10}^{-3}}}+25$, the annealing temperature of 780 °C is converted into an equivalent current, and electrify annealing is conducted under a condition of 0.08 A for 15 min. The annealed wires exhibit a softening effect and enhance ductility. Furthermore, due to stored deformation energy and recrystallization effects, the electrical fatigue life of 15 μm stainless steel wires is approximately 300 cycles. After electrifying annealing, the base microstructure becomes more homogeneous due to thermal effects, reducing fatigue life to around 150 cycles. However, due to the softening effect, the annealed wires make the EFO process easier and minimize solidification segregation in the free air ball (FAB) microstructure, demonstrating their potential for electronic packaging applications. Full article

In this study, we investigated the influence of an epoxy end-capped polypropylene oxide crosslinker (epoxy-PPO) on the formation of the crosslinked network structure, the stress–strain response, and the electro-mechanical actuation performance of a maleic anhydride functionalized liquid isoprene rubber (LIR). The crosslinker amount varied from 10 (C-LIR-10) to 50 (C-LIR-50) weight parts per hundred parts (phr) of LIR. The swelling test of the cured rubbers revealed that C-LIR-20 formed the densest crosslinked network with the lowest chloroform uptake value within this series. The crosslinked rubber became stiffer in tensile response upon increasing the epoxy-PPO amount from C-LIR-10 to C-LIR-20 and then softened at higher amounts. The SEM measurements were used to relate this composition-induced softening of the rubbers to the phase morphology evolution from nanoscale homogeneity in C-LIR-10 to microscale segregations of excess crosslinkers in C-LIR-50. The use of epoxy-PPO improved the dielectric constant value of LIR; however, the leakage current through the films also increased from 25 µA DC to 320 µA DC for LIR-30 and LIR-50, respectively, during DEA operation. The electro-mechanical actuation tests with circular actuators showed that the C-LIR-10 elastomer film demonstrated a radial strain of 1.7% on activation at an electric field strength of 17.5 V/µm. At higher crosslinker amounts, the close proximity of excess epoxy-PPO molecules caused leakage current across elastomer films thus diminishing the actuation strain of otherwise relatively softer elastomers with higher dielectric constant values. Full article