Quasicrystalline coatings based on Al₆₅Cu₂₀Fe₁₅ are of increasing interest as potential alternatives to conventional wear-resistant materials due to their unique structural and tribological properties. This study explores the influence of air pressure during high-velocity oxy-fuel (HVOF) spraying on the phase composition, morphology, and wear behavior of Al₆₅Cu₂₀Fe₁₅ coatings deposited on U8G tool steel. Coatings were applied at a fixed spraying distance of 350 mm using three air pressures (1.9, 2.1, and 2.3 bar), with constant propane (2.0 bar) and oxygen (2.1 bar) supply. X-ray diffraction analysis identified the formation of Al₇₈Cu₄₈Fe₁₄ and Al_0.5Fe_1.5 phases, while scanning electron microscopy revealed a dense, uniform microstructure with low porosity and homogeneous element distribution across all samples. Tribological testing using the ball-on-disk method showed wear track widths ranging from 853.47 to 952.50 µm, depending on the air pressure applied. These findings demonstrate that fine-tuning the air pressure during HVOF spraying significantly influences the structural characteristics and wear resistance of the resulting quasicrystalline coatings, highlighting their promise for advanced surface engineering applications.

Recently, nanoparticles based on rare earth fluorides have been widely investigated due to their possible medical and spintronic applications, where it is desirable to know the magnetic moment volume distribution. However, this feature is hard to find out from the experiments, so we employ ab initio spin-polarized calculations for this purpose in order to find out the tendencies and common features in three different compounds. In the present work, the nanoparticles of dipole magnets with different sizes, namely TbF₃, DyF₃ and HoF₃, were simulated, and optimized structures were found. We present the optimized structures for the particles with various sizes, as well as for slabs constructed from same compounds. Lastly, for optimized geometries, the analysis of magnetic moments’ distribution over the nanoparticles and slabs volume was performed.

In this paper, the process of glass transition is studied, the theory of which is not fully developed. Computer modeling can be used to understand the theory of this phenomenon. The glass transition temperature is influenced by a large number of polymer parameters: cooling rate, pressure, presence of diluent, structural features, etc. We are considering a number of different polymers to test the ability of the pcff+ force field to determine the glass transition temperature. The effect of tactility, composition, pressure and the presence of a diluent on the glass transition temperature of polymers will be shown using molecular dynamics (MD) and pcff+ force-field modeling. The effect of tact was studied using the use of atactic, isotactic and syndiotactic poly(methyl methacrylate) and atactic, isotactic and syndiotactic polypropylene. The LAMMPS code integrated into the MedeA computing environment was used to simulate the molecular dynamics of polymers. The calculation of the glass transition temperature at different cooling rates is closely related to the balanced initial systems. Due to the inability of current atomistic simulations to achieve the required cooling rates, as in experiments, the gap was not sharp and quite obvious. The data obtained show that the pcf+ force field describes tactics quite effectively and gives differences in the glass transition temperature for different types of tact. For polymers diluted with CO2, the glass transition temperature decreases almost linearly, which is in good agreement with the experiment. As expected, the pressure increases the glass transition temperature. However, at high pressure, the slope fracture disappears, and the determination of Tg becomes extremely difficult. The simulated annealing process will be applied to a set of polymers to obtain graphs of the specific volume versus temperature and determine the glass transition temperature.

Mathematics learning (ML) is a fundamental aspect of education that lays the groundwork for various academic disciplines and practical applications. Understanding the factors that propel ML is crucial for optimizing educational outcomes. This quantitative empirical study investigates the impact of logical reasoning (LR), critical thinking (CT), information technology (IT), and distance learning (DL) on ML. The study employs structural equation modeling (SEM) using SmartPLS 4 for data analysis and hypothesis testing. The findings reveal that LR, CT, IT, and DL positively influence ML. The results highlight the importance of fostering LR, CT, and the integration of IT in mathematics education. This study contributes to the existing body of knowledge by providing insights into the factors that promote effective ML. These findings have implications for educators, policymakers, and curriculum developers, aiding in the design of instructional strategies and the integration of technology to enhance ML outcomes.