Materials最新文献

This review considers the use of fractal concepts to improve the development, fabrication, and characterisation of catalytic materials and supports. First, the theory of fractals is discussed, as well as how it can be used to better describe often highly complex catalytic materials and enhance structural characterisation via a variety of different methods, including gas sorption, mercury porosimetry, NMR, and several imaging modalities. The review then surveys various synthesis and fabrication methods that can be used to create catalytic materials that are fractals or possess fractal character. It then goes on to consider how the fractal properties of catalysts affect their performance, especially their overall activity, selectivity for desired products, and resistance to deactivation. Finally, this review describes how the optimum fractal catalyst material for a given reaction system can be designed on a computer.

Nanochitin was developed to effectively utilize crab shells, a food waste product, and there is ongoing research into its applications. Short nanowhiskers were produced by sonicating partially deacetylated nanochitin in water, resulting in a significant decrease in viscosity due to reduced entanglement of the nanowhiskers. These nanowhiskers self-assembled into a multilayered film through an evaporation technique. The macro- and nanoscale structures within the film manipulate light, producing vibrant and durable structural colors. The dried cast film exhibited green and purple stripes extending from the center to the edge formed by interference effects from the multilayer structure and thickness variations. Preserving structural colors requires maintaining a low ionic strength in the dispersion, as a higher ionic strength reduces electrostatic repulsion between nanofibers, increasing viscosity and potentially leading to the fading of color. This material's sensitivity to environmental changes, combined with chitin's biocompatibility, makes it well-suited for food sensors, wherein it can visually indicate freshness or spoilage. Furthermore, chitin's stable and non-toxic properties offer a sustainable alternative to traditional dyes in cosmetics, delivering vivid and long-lasting color.

Basalt fiber (BF) has been proven to be an effective additive for improving the properties of asphalt mixtures. However, the influence of basalt fiber morphology on the properties of asphalt binders and mixtures remains inadequately explored. In this study, chopped basalt fiber (CBF) and flocculent basalt fiber (FBF) were selected to make samples for testing the influence of the two types of basalt fibers on asphalt materials. Fluorescence microscopy was used to obtain the dispersion of fiber in asphalt binders. Then, a temperature sweep test and a multiple stress creep recovery (MSCR) test were carried out to appraise the rheological characteristics of the binder. Moreover, the performance of the fiber-reinforced asphalt mixture was evaluated by a wheel tracking test, a uniaxial penetration test, an indirect tensile asphalt cracking test (IDEAL-CT), a low-temperature bending test, a water-immersion stability test, and a freeze-thaw splitting test. The results indicate that the rheological behavior of asphalt binders could be enhanced by both types of fibers. Notably, FBFs exhibit a larger contact area with asphalt mortar compared to CBFs, resulting in improved resistance to deformation under identical shear conditions. Meanwhile, the performance of the asphalt mixture underwent different levels of enhancement with the incorporation of two morphologies of basalt fiber. Specifically, as for the road property indices with FBFs, the enhancement extent of DS in the wheel tracking test, that of R_T in the uniaxial penetration test, that of the CT_index in the IDEAL-CT test, and that of ε_B in the low-temperature trabecular bending test was 3.1%, 6.8%, 15.1%, and 6.5%, respectively, when compared to the CBF-reinforced mixtures. Compared with CBFs, FBFs significantly enhanced the elasticity and deformation recovery ability of asphalt mixtures, demonstrating greater resistance to high-temperature deformation and a more pronounced effect in delaying the onset of middle- and low-temperature cracking. Additionally, the volume of the air void for asphalt mixtures containing FBFs was lower than that containing CBFs, thereby reducing the likelihood of water damage due to excessive voids. Consequently, the moisture susceptibility enhancement of CBFs to asphalt mixture was not obvious, while FBFs could improve moisture susceptibility by more than 20%. Overall, the impact of basalt fibers with different morphologies on the properties of asphalt pavement materials varies significantly, and the research results may provide reference values for the choice of engineering fibers.

Accurate temperature measurement is critical across various scientific and industrial applications, necessitating advancements in thermometry techniques. This study explores luminescence thermometry, specifically utilizing machine learning methodologies to enhance temperature sensitivity and accuracy. We investigate the performance of principal component analysis (PCA) on the Eu³⁺-doped Y₂Mo₃O₁₂ luminescent probe, contrasting it with the traditional luminescence intensity ratio (LIR) method. By employing PCA to analyze the full emission spectra collected at varying temperatures, we achieve an average accuracy (ΔT) of 0.9 K and a resolution (δT) of 1.0 K, significantly outperforming the LIR method, which yielded an average accuracy of 2.3 K and a resolution of 2.9 K. Our findings demonstrate that while the LIR method offers a maximum sensitivity (Sr) of 5‱ K⁻¹ at 472 K, PCA's systematic approach enhances the reliability of temperature measurements, marking a crucial advancement in luminescence thermometry. This innovative approach not only enriches the dataset analysis but also sets a new standard for temperature measurement precision.

Heating steel charges is essential for proper charge formation. At the same time, it is a highly energy-intensive process. Limiting the scale formed is critical for reducing heat consumption in this process. This paper applies fuzzy logic to model heating and scale formation in industrial re-heating furnaces. Scale formation depends on the temperature of the initial charge, heating time, excess air coefficient value, and initial scale thickness. These parameters were determined based on experimental tests, which are also the inputs in the model of the analyzed process. The research was carried out in walking beam furnaces operating in hot rolling mill departments. To minimize the excess energy consumption for heating a steel charge in an industrial furnace before forming, a heating and scale formation (HSF) model was developed using the fuzzy logic-based approach. The developed model allows for the prediction of the outputs, i.e., the charge's final surface temperature and the scale layer's final thickness. The comparison between the measured and calculated results shows that the model's accuracy is acceptable.

This study aims to examine the variation in corrosion characteristics and tensile properties of WE43 magnesium alloy in an actual marine atmospheric environment by means of outdoor exposure tests. The macroscopic corrosion morphology, microstructure, and tensile properties were analyzed. The results indicated that WE43 alloy will corrode rapidly during exposure under marine atmospheric environmental conditions, resulting in a loose and porous Mg(OH)₂ layer on the surface. The Mg matrix was mainly consumed as an anode, leading to the occurrence of corrosion pits. With the increase in exposure time, both the tensile strength and plasticity of WE43 alloy gradually deteriorated. After exposure for six months, the elongation and area reduction were significantly reduced, with a reduction ratio of more than 50%. After 18 months of exposure, the ultimate strength of the alloy decreased from 359 MPa to 300 MPa. According to an analysis of fractures in the alloy, the corrosion pits on the sample surface were the main reason for the decrease in tensile properties.

As nanophysics constitutes the scientific core of nanotechnology, it has a decisive potential for advancing clean renewable energy applications. Starting with a brief foray into the realms of nanophysics' potential, this review manuscript is expected to contribute to understanding why and how this science's eruption is leading to nanotechnological innovations impacting the clean renewable energy economy. Many environmentally friendly energy sources are considered clean since they produce minimal pollution and greenhouse gas emissions; however, not all are renewable. This manuscript focuses on experimental achievements where nanophysics helps reduce the operating costs of clean renewable energy by improving efficiency indicators, thereby ensuring energy sustainability. Improving material properties at the nanoscale, increasing the active surface areas of reactants, achieving precise control of the physical properties of nano-objects, and using advanced nanoscale characterization techniques are the subject of this in-depth analysis. This will allow the reader to understand how nanomaterials can be engineered with specific applications in clean energy technologies. A special emphasis is placed on the role of such signs of progress in hydrogen production and clean storage methods, as green hydrogen technologies are unavoidable in the current panorama of energy sustainability.

In order to impart the properties of cementitious material to the Tibetan Agar soil, two high-temperature activation mechanisms (HTMA, HTMB) were designed in this study, and the products and hydration-hardening properties of Tibetan Agar soil high-temperature activation mechanism were analyzed by means of SEM, XRD, and XRF. The results show that the main components of Tibetan Aga soil are calcite and quartz; Aga soil is activated by HTMA high-temperature activation, forming the main products of CaO, C₂S, CaSiO₃, and CaAl₂Si₂O₈, and its products have both air-hardening and water-hardening characteristics; Aga soil is activated by HTMB high-temperature activation, and when the temperature reaches 1250 °C when the clinker is not found in the CaO, the generation of C₂S, C₃S, C₃A, C₄AF, and Mg₂SiO₄ minerals with good water-hardening cementitious properties occurs when the temperature rises to 1350 °C, although the formation of some inert minerals that do not have the cementitious properties, but this temperature activation products of the thermodynamic properties of the best; Enhancing the value of lime saturation degree (KH) and silicon rate (SM) can promote the formation of the products of the C₂S and C₃S, increase the reactivity of the Aga soil activation products, and increase the hydration heat as well as compressive and flexural strength, combined with the results of the hydration heat and mechanical test, KH is recommended to be 0.9~0.94, SM is recommended to be 1.8~2.4, and alumina ratio (IM) is recommended to be 1.8~2.4 when Aga soil is used with raw materials.

This study employs machine learning models to predict the adsorption characteristics of biochar-activated carbon derived from waste wood. Activated carbon is a high-performance adsorbent utilized in various fields such as air purification, water treatment, energy production, and storage. However, its characteristics vary depending on the activation conditions or raw materials, making explaining or predicting them challenging using physicochemical or mathematical methods. Therefore, using machine learning techniques to determine the adsorption characteristics of activated carbon in advance will provide economic and time benefits for activated carbon production. Datasets, consisting of 108 points, were used to predict the adsorption characteristics of biochar-activated carbon derived from waste wood. The input variables were the activation conditions, and the iodine number of activated carbon was used as the output variable. The datasets were randomly split into 75% for training and 25% for model validation and normalized by the min-max function. Four models, including artificial neural networks, random forests, extreme gradient boosting, and support vector machines, were used to predict the adsorption properties of biochar-activated carbon. After optimization, the artificial neural network model was identified as the best model, with the highest coefficient determination (0.96) and the lowest mean squared error (0.004017). As a result of the SHAP analysis, activation time was the most crucial variable influencing the adsorption properties. The machine learning model precisely predicts the adsorption characteristics of biochar-activated carbon and can optimize the activated carbon production process.

This paper presents the experimental and numerical results of a study on producing axisymmetric parts made of the C60-grade steel by skew rolling. The experimental part of this study involved conducting the skew rolling process with varying parameters, including the forming angle α, tool angle θ, chuck velocity V_u, and reduction ratio δ. Their effect on the quality of produced parts was examined and described by the roughness parameter Ra. Numerical calculations involved the use of machine learning models to predict the quality class of produced parts. The highest prediction accuracy of the results was obtained with the random forest and logistic regression models. Metrics such as precision, recall and accuracy were used to evaluate the performance of individual models. Confusion matrices and ROC curves were also employed to illustrate the performance of the classification models. The results of this study will make it possible to prevent the formation of spiral grooves on the circumference of steel parts during the rolling process.