Materials最新文献

Various methods for classifying and evaluating the shape, size, and surface texture of sand particles are examined, highlighting their impact on concrete mixture properties. This study emphasizes the role of particle morphology in determining concrete workability and segregation, particularly in glass-fiber-reinforced (GRC) thin-layer concrete for building facade panels. The effects of different aggregate types on concrete workability and segregation are analyzed, showing that aggregates with spherical particles and a lower elongation index improve mixture consistency and reduce segregation. Three types of fine aggregates were used (instead of quartz sand in the mixtures, natural sand and granite screenings were chosen, which would be a sustainable alternative to quartz sand), and thin-layer glass-fiber-reinforced concrete using aggregates of different shapes was characterized by layering the mixture. The workability and segregation of fine-grained fiberglass-reinforced concrete mixtures depend on the aggregate particles' shape. Up to 50% of quartz sand can be replaced with granite siftings or natural sand, as measured by the segregation index, as calculated according to the method proposed in this paper. Increasing the amount of natural sand from 10% to 50% also increases the segregation index from 1.9 to 2.6, and when using granite sifting aggregates, it rises from 2.6 to 3.5. Aggregates with spherical particles are more suitable for this thin-layer GRC concrete, if we examine the consistency parameters of fresh concrete and the possibilities of working with it in real production conditions.

Fibres can markedly enhance the uniaxial compressive strength (UCS) of cemented paste backfill (CPB). However, previous studies have mainly verified the effectiveness of polypropylene and straw fibres in improving the UCS of CPB experimentally, while systematic multi-factor evaluation remains limited. In this study, laboratory experiments were conducted on polypropylene- and straw fibre-reinforced CPB to construct a reliable dataset. The factors influencing the intensity of uniaxial compressive strength were divided into four aspects (mixture proportions, physical properties of the cement-tailings mixture, chemical characteristics of tailings, and fibre properties), and four intelligent models were developed for effectiveness analysis and UCS prediction. SHapley Additive exPlanations (SHAP) were employed to quantify the contributions of individual features, and the findings were experimentally validated. The GWO-LGBM model outperformed the SVR, ANN, and LGBM models, achieving R² = 0.907, RMSE = 0.78, MAE = 0.515, and MAPE = 0.157 for the training set, and R² = 0.949, RMSE = 0.627, MAE = 0.38, and MAPE = 0.115 for the testing set, respectively. Feature analysis reveals that mixture proportions contribute the most to UCS, followed by the tailings' physical properties, the fibre properties, and the tailings' chemical characteristics. This study found that cement content and tailings gradation control CPB structural compactness and fibres enhance bonding between hydration products and tailings aggregates, while the chemical composition of the tailings plays an inert role, functioning mainly as an aggregate.

Salt permeation erosion is a key factor leading to the deterioration of service performance and shortening the lifespan of asphalt pavement in salt-rich areas. In this environment, the combined action of water and salt accelerates the decline in the asphalt-aggregate interface, leading to distress, such as raveling and loosening, which severely limit pavement durability. The authors systematically reviewed the research progress on asphalt-aggregate adhesion in a saline corrosion environment and discussed the complex mechanisms of adhesion degradation driven by intrinsic factors, including aggregate chemical properties, surface morphology, asphalt components, and polarity, as well as environmental factors, such as moisture, salt, and temperature. We also summarized multi-scale evaluation methods, including conventional macroscopic tests and molecular dynamics simulations, and revealed the damage evolution patterns caused by the coupled effects of water, salt, heat, and mechanical forces. Based on this, the effectiveness of technical approaches, such as asphalt modification and aggregate modification, is explored. Addressing the current insufficiency in research on asphalt adhesion under complex conditions in salt-rich areas, this study highlights the necessity for further research on mechanisms of multi-environment interactions, composite salt erosion simulation, development of novel anti-salt erosion materials, and intelligent monitoring and early warning, aiming to provide a theoretical basis and technical support for the weather-resistant design and long-term service of asphalt pavement in salt-rich regions.

This study investigates the application of machine learning (ML) techniques for predicting vibration frequencies of thin rectangular plates with variable thickness. Traditional optimization methods, such as genetic algorithms, require repeated solutions of the plate vibration eigenproblem using finite element (FE) analysis, which is computationally expensive. To reduce this cost, a surrogate model based on artificial neural networks (ANNs) is proposed as an efficient alternative. The dataset includes variations in plate geometry, boundary conditions, and thickness distribution, encoded numerically for model training. ANN architecture and hyperparameters-such as the number of hidden layers, neurons per layer, and activation functions-were systematically tuned to achieve high prediction accuracy while avoiding overfitting. Data preprocessing steps, including standardization and scaling, were applied to improve model stability. Performance was evaluated using metrics such as RMSE and R². The results demonstrate that ANNs can accurately predict eigenvalues with significantly reduced computational effort compared to FE analysis. This approach offers a practical solution for integrating machine learning into structural optimization workflows.

Sapphire (α-Al₂O₃) has been widely used in high-power lasers, optical windows, semiconductor substrates, radomes, and other applications due to its exceptional optical properties, high hardness, excellent chemical stability, and thermal resistance. However, machining sapphire poses significant challenges because of the material's high hardness and brittleness. Traditional mechanical and chemical-mechanical machine methods often fail to meet the processing requirements for micro and nanoscale structures. Recently, the use of femtosecond lasers-with ultra-short pulses and extremely high peak power-has allowed for the precise machining of sapphire with minimal thermal damage, a method akin to cold processing. Femtosecond laser processing offers significant advantages in fabricating three-dimensional micro- and nanoscale structures, surface and internal modification, optical waveguide writing, grating fabrication and dissimilar materials welding. Thus, this paper systematically reviewed the research progress in femtosecond laser processing of sapphire, covering technical approaches such as ablation, hybrid processing and direct writing micro- and nanoscale fabrication. The capability of femtosecond laser processing to modulate sapphire's optical properties, wettability and mechanical and chemical characteristics were discussed in detail. The current challenges related to efficiency, cost, process standardization and outlines future development directions, including high-power lasers, parallel processing, AI optimization and multifunctional integration were also analyzed.

Nickel-Aluminum-Bronze (NAB) has gained significant attention in marine applications due to its excellent corrosion resistance and has shown growing potential for laser powder bed fusion (L-PBF) additive manufacturing. However, research on the fabrication of NAB alloys using L-PBF remains relatively limited. In this study, fully dense NAB samples were successfully fabricated through L-PBF process parameter optimization. The microstructural evolution and mechanical properties of both as-built and annealed L-PBF samples were systematically investigated and compared with those of traditionally cast NAB. The results reveal that the as-built L-PBF specimens primarily consist of columnar β' grains, with the α phase distributed along the grain boundaries and a small amount of κ phase precipitated within the β' matrix, distinctly different from the cast microstructure characterized by a columnar α-phase matrix with precipitated β' and κ phases. After annealing at 675 °C for 6 h, the β' phase in both methods decomposed into α + κ phases, and the original columnar structure in the L-PBF specimens transformed into a dendritic morphology. Compared to the cast samples, the L-PBF-produced NAB alloy exhibited significantly enhanced yield strength, tensile strength, and microhardness, attributable to rapid solidification during the L-PBF process. Following annealing, the yield strength and elongation increased by 12.8% and 184.4%, respectively, compared to the as-built condition, resulting from the decomposition of the martensitic phase into α + κ phases and further grain refinement.

Electric field-assisted local functionalization of materials is a resist-free technique generally applied at the nanoscale, which has been understood within the paradigm of the water meniscus. Using a home-made prototype the authors applied this technique at scales compatible with the biosensor industry (tens of microns). However, interpreting these results requires a different paradigm. The expansion of the oxidized region over time in two-dimensional materials under a localized electric field is modeled from first physical principles. Boltzmann statistics is applied to the oxyanion incorporation at the perimeter of the oxidized zone, and a new general relation between oxide radius and time is formulated. It includes the reduction in the energy barrier due to the field effect and its dependence on the oxide radius. To gain insight into this dependence whatever the layers structure, 2D material involved, or electrical operating conditions, simple structures based on multilayer stacks representing the main constituents are proposed, where the Poisson equation is solved using finite element calculations. This enables to derive energy barriers for oxyanion incorporation at varying spot radii which are consistent with those resulting from fitting experimental data. The reasonable agreement obtained provides researchers with a new tool to predict the evolution of local functionalization of 2D layers as a function of the following fabrication parameters: time, applied voltage, and relative humidity, solely based on materials properties.

Porosity formation due to solidification shrinkage and inadequate liquid metal feeding during the casting of Sn-0.3Ag-0.7Cu (SAC0307) is a critical issue that impairs quality and subsequent processing. However, the opacity of the casting process often obscures the quantitative relationships between process parameters and defect formation, creating a significant barrier to science-based optimization. To address this, the present study utilizes finite element method (FEM) analysis to systematically investigate the influence of pouring temperature (PCT, 290-390 °C) and interfacial heat transfer coefficient (HTC, 900-5000 W/(m²·K)) on this phenomenon. The results reveal that PCT exerts a non-monotonic effect on porosity by modulating the solidification mode, which governs the accumulation of dispersed microporosity. In contrast, HTC plays a critical role in determining porosity morphology by controlling both the solidification rate and mode. Consequently, an optimal processing window was identified at 350 °C PCT and 3000 W/(m²·K) HTC, which significantly enhances interdendritic feeding and improves the ingot's internal soundness. The efficacy of these optimized parameters was experimentally validated through macro- and microstructural characterization. This work not only elucidates the governing mechanisms of solidification quality but also demonstrates the value of numerical simulation for process optimization, offering a reliable scientific basis for the industrial production of high-quality SAC0307 alloys.

Ultra-High-Performance Concrete (UHPC) is being increasingly utilized in major engineering projects due to its excellent mechanical properties, strong durability, and superior overall performance. Nevertheless, the widespread use of premium cementitious materials leads to high expenses and a substantial environmental impact. In this work, crushed recycled paste was calcined at 600 °C for two hours to produce calcined recycled fine powder (RFP) with varying hydration reactivity. UHPC was produced using the RFP in place of some of the cement. Chemical activation was accomplished by adding a composite activator system made up of Ca(OH)₂, Na₂SO₄, Na₂SiO₃·9H₂O, and K₂SO₄ in order to further improve the performance of UHPC. Particle size, viscosity, fluidity, mechanical properties, and hydration products were analyzed to establish the best activator type and dosage, as well as the ideal activation procedure for recycled fine powder. By mass replacement of cementitious materials, when 15.0% of the calcined recycled fine powder was added, the compressive strength of UHPC reached 149.1 MPa, a 23.2% increase over reference UHPC without calcined recycled fine powder. The results show that the calcined recycled fine powder ground for 60 min exhibits the highest activity. More hydrated products were formed in UHPC as a result of the addition of Ca(OH)₂. The compressive strength peaked at 162.2 MPa at an incorporation rate of 1.5%, which is 8.8% higher than UHPC without an activator.

High-entropy alloys (HEAs) have attracted significant attention due to their exceptional physical, chemical, and mechanical properties. The current development of HEAs primarily depends on time-consuming and costly trial-and-error approaches, which not only hinder the efficient exploration of new compositions but also result in unnecessary resource and energy consumption, thereby negatively affecting sustainable development and production. To address this challenge, this study introduces a machine learning-based methodology for predicting the yield strengths of various HEA compositions. The model was trained using 181 data points and achieved an R² performance score of 0.85. To further assess its reliability and generalization capability, the model was validated using external data not included in the collected dataset. The validation was performed across four categories: modified Cantor alloys, refractory HEAs, eutectic HEAs, and other HEAs. The predicted yield strength trends were found to align with the actual experimental trends, demonstrating the model's robust performance across various categories of HEAs. The proposed machine learning approach is expected to facilitate the combinatorial design of HEAs, thereby enabling efficient optimization of compositions and accelerating the development of novel alloys. Moreover, it has the potential to serve as a guideline for sustainable alloy design and environmentally conscious production in future HEA development.