Abstract: As a new type of energy dissipation damping mechanism widely used in recent years, anti-buckling bracing still has some shortcomings such as self-importance. In this paper, the composition of anti-buckling support, its working principle and weight reduction ideas and methods are explained first, and then the test process and methods of light aggregate are described, and then the possible problems of light anti-buckling support are explained, and finally the possible problems are put forward for practical application. Read More

Abstract: In the application of laser cladding technology, cobalt-based alloys are widely used due to their excellent wear resistance and corrosion resistance. However, they are prone to cracking under high-temperature conditions, which limits their application range. To address this issue, this study introduced the rare earth element Sm into cobalt-based alloy powders and systematically investigated its effect on crack suppression in cobalt-based alloys. The results indicate that with the increase in Sm content, the elastic modulus of the cobalt-based alloy first increases and then decreases. When the Sm content is 10%, the alloy achieves its maximum nano-hardness and elastic modulus, but the crack suppression effect is the weakest at this point. As the Sm content further increases to 30%, the cracks are significantly reduced, and the overall performance of the material is markedly improved. In conclusion, this study successfully developed a cobalt-based alloy coating with excellent performance and fewer cracks, providing an important reference for the application of laser cladding technology. Read More

Abstract: In this study, Ni-Zr alloy glass is chosen as the research object, and we use the molecular dynamics method to simulate and study the microstructure evolution of Ni-Zr alloy at different temperatures. The computational analysis carried out using LAMMPS, and investigated the effect of five different cold speeds on the amorphization potential of Ni-Zr. Additionally, the structural properties of Ni-Zr amorphous clusters at different cold speeds were elucidated. The results shown that cold speed is a key factor influencing the ability of Ni-Zr to form amorphous. Furthermore, amorphous formation is more likely to occur at higher cold speeds. The formation of amorphous is facilitated under the condition of higher cold speeds. By investigating the microevolutionary process and the relationship between structural entropy and temperature, it can be seen that the entropy value of the structure shows an increasing trend with the increase of temperature and cold speed. This indicates that the structure's instability increases with increasing cold speed, which is favorable to the glass-forming ability of Ni-Zr. The kinetic process and structural analysis facilitate an in-depth comprehension of the microstructural evolution law and evolution mechanism of the Ni-Zr alloy, thereby providing a more reliable theoretical basis for the application and optimization of this material. Read More

Abstract: The thermoelectric properties of NdZnSbO have been systematically studied using density functional theory calculations. The bulk NdZnSbO is a natural super lattice structure with low thermal conductivity and low electrical conductivity. The band structure indicates that it is a direct gap semiconductor with a band gap of 0.68eV. Its conductivity can be improved by doping and thus increase its ZT value. At a temperature of 800K, near the carrier concentration 2.1E19/cm3, the p-type doped system presents a Seeback coefficient of 382μVκ-1, a conductivity of 1.48×104Sm-1 and a power factor of 0.66×10-3Wm-1κ-2. As a result, a thermoelectric figure of merit (ZT) reaches a maximum value of 1.12. Read More

Abstract: This paper focuses on the innovative business model and its technology application of the Yuan-Universe financial trading platform. Firstly, the global trend and competitive status of cosmic finance are discussed through market analysis. Later, the paper analyzes in depth the role of Web3.0, blockchain and AI in improving the performance of the trading platform. In the business model section, this paper discusses the profit models such as transaction withdrawal and information consulting, and evaluates their financial sustainability. In addition, the paper analyzes the potential technical and market risks of the platform and discusses the management strategies. Finally, the paper summarizes the business and technological innovation of the yuan universe financial trading platform, and prospects the future development. This study provides insights for understanding new models of financial services and provides reference for the development of fintech. Read More

Abstract: Laser-induced metal surface coloring technology combines the advanced manufacturing technology of modern laser technology and material science. It no longer relies on dyes or chemicals to achieve material surface coloring, but makes use of the characteristics of the interaction between laser and metal materials. Through the precise control of laser parameters (such as wavelength, pulse width, energy density, etc.), complex micro-structures can be formed on the metal surface, thus changing its optical properties. To achieve the coloring effect, compared with the traditional coloring method, it has significant advantages such as no pollution, high precision, strong flexibility and so on. This paper deeply discusses the core principle of laser-induced metal surface coloring, systematically analyzes the evaluation criteria of coloring quality, and focuses on the key factors affecting the induced coloring process. Despite its strong application potential, laser-induced metal surface coloring technology still lacks sufficient key data support for constructing an accurate coupling model between laser parameters and coloring effect. This hinders the achievement of high controllability, reproducibility, durability, and stability of surface shading colors. Therefore, it is necessary to further focus on these core issues and accumulate more experimental data through fine experimental design and advanced characterization technology, so as to promote the continuous optimization and wide application of laser-induced metal surface coloring. Read More

Abstract: The purpose of this study is to investigate the innovative design and efficiency improvement strategies of biomass catalysts from the perspective of green chemistry. In view of the increasing global awareness of environmental protection and the keen demand for sustainable development models, green chemistry has become an indispensable evolution direction in the field of chemical engineering. Biomass catalysts, with their excellent efficiency, excellent selectivity, and low impact on the environment, have shown great potential applications in the territory of green chemistry. This paper describes how to select specific microbial populations, use gene manipulation technology to breed high-performance recombinant enzymes, and use immobilization technology to enhance their durability and reuse. Experimental experience confirms that the improved biomass catalyst exhibits outstanding catalytic activity and stability, significantly reducing energy consumption while minimizing by-product output. Through the characterization of the catalyst, the catalytic mechanism of the catalyst is discussed and evaluated from the standpoint of green chemistry. These findings not only lay the theoretical foundation for the construction of innovative biomass catalysts, but also light the green light for technical support to further promote more eco-friendly chemical processes. Future work will examine more closely the potential of such biocatalysts in complex industrial ecosystems. Read More

Abstract: The increasing concentration of carbon dioxide (CO2) in the atmosphere presents a significant challenge in the context of climate change, necessitating innovative strategies for greenhouse gas mitigation. CO2 hydrogenation, which converts CO2 into hydrocarbons and other valuable chemicals using hydrogen, has emerged as a promising method for addressing both carbon emissions and renewable energy production. Catalysts are crucial in enhancing the efficiency and selectivity of these reactions; however, traditional catalyst design methods often rely on laborious trial-and-error approaches, which can be inefficient and resource-intensive. This paper explores the integration of active learning and reinforcement learning as advanced methodologies for automating catalyst design specifically for CO2 hydrogenation. Active learning focuses on selecting the most informative data points to improve model predictions while minimizing experimental costs, whereas reinforcement learning optimizes decision-making processes through iterative feedback. A case study demonstrates the application of these techniques, leading to the successful identification of novel catalyst compositions that exhibit superior performance metrics. The findings highlight the potential of machine learning to revolutionize catalyst discovery, ultimately contributing to more sustainable CO2 conversion strategies. Read More