Materials Science Forum Vol. 1168

Abstract: The manufacture and repair of mechanical engineering products and other industries is associated with the qualitative preparation of their surfaces for the application of protective non-metallic coatings on them, which ensures an increase in their durability and reliability. Among the methods of cleaning metal surfaces, the environmentally friendly and energy-saving crushing process occupies a priority place. Therefore, it is important to substantiate aspects of the destruction process of the metal products’ surface layer of mechanical engineering during shot blasting, depending on the process parameters, which allows more rational planning and a balanced technology for high-quality surface preparation of products. The results of experimental and analytical studies of the mechanism of destruction of the surface layer of steel specimens attacked by a shot, as well as a shot torch, depending on the specified parameters: angle and speed of attack, shot diameter and mechanical properties of the material are presented. The destruction coefficient is related to the ratio of the trace volume left by the pellet on the attacked surface to the pellet volume, which integrates the result of dynamic contact. The results obtained indicate that the degree of destruction intensity does not depend on the diameter of the shot and reaches a maximum at angles of attack at a given speed. The analytical setting of the destruction coefficient allows, without unnecessary technological samples, to balance the process parameters to achieve the required quality of surface cleaning at the lowest possible cost.

Abstract: The paper investigates the influence of fibre orientation on the mechanical characteristics of PETG plastic products manufactured by FDM printing (Fused Deposition Modelling). Three groups of experimental samples were made with different fibre orientation: along the load axis, perpendicular to it, and at an angle of 45 °. Experimental tensile and compression tests, including the stages of elastic deformation, structure strengthening, and local fracture, were performed to study the mechanical characteristics of the material. It is established that the orientation of the fibres is a determining factor of strength and deformation behaviour of products: samples with fibres along the load axis are characterized by increased plasticity and strength, while the transverse orientation reduces mechanical resistance due to interlayer destruction. Samples with fibre orientation at an angle of 45° demonstrated an optimal combination of strength and deformation capacity. The results of the study confirm the need to consider the fibre orientation when designing PETG plastic products to ensure optimal performance.

Abstract: The tool stability and its wear are important factors that determine the patterns of change in systematic and random errors during processing. The higher the tool stability and, consequently, the lower the wear intensity over time, the longer the period of time the part error does not exceed the tolerance field and the less often it is necessary to re-adjust the equipment and re-grind the tool. Cold treatment of a blade tool to cryogenic temperatures after sharpening can make it possible to significantly increase its stability. The purpose of processing hardened steel at a temperature below room temperature is to remove residual austenite from the structure and subsequently affects the properties of the steel. In the martensitic transformation interval, between the temperatures and, ordinary room temperature is a stop that interrupts the course of transformations during cooling. Thus, cooling below zero is a natural part of the steel hardening process.

Abstract: When solar energy irradiates conductive metal surfaces, it is primarily converted into heat due to the generation of eddy currents on the metal surface. However, combining metals with inorganic ceramic long-persistent phosphors enables the storage and reuse of solar energy. In this study, a chemical precipitation method was employed to coat nickel precursors onto SrAl₂O₄:Eu²⁺,Dy³⁺ (SAO) ceramic phosphors, which emit a broad green spectrum at 520 nm under 440 nm excitation. A uniform nickel shell was successfully deposited on the surface of the phosphor particles, with only a slight decrease in photoluminescence intensity. The formation of a complete shell layer was confirmed through EDS elemental mapping analysis. Advanced oxidation heat treatment effectively produced a NiO shell and enhanced the structural integrity of SrAl₂O₄. Subsequent reduction heat treatment converted the NiO into a metallic nickel shell. This metallic layer improved the wettability and interfacial bonding between SAO and nickel backbone, providing increased resistance to mechanical stress. Due to the larger surface area of the foamed nickel structure, the resulting porous phosphor composite demonstrated superior luminescent performance compared to traditional phosphor-metal castings. This innovative phosphor-metal composite shows great potential for novel lighting applications in the metal and lighting industry.

Abstract: This study explores the potential of Lygodium circinatum (commonly known as Nito vine), an underutilized natural fiber in polymer composites, as reinforcement in epoxy-based polymer composites. With the growing shift toward sustainable alternatives to synthetic fibers, Nito fiber presents an eco-friendly and cost-effective option. Sodium hydroxide (NaOH) treatment was applied to modify the fiber surface, and its effects on the fiber–matrix interaction, thermal stability, and mechanical performance were evaluated. FTIR analysis confirmed the successful reduction of non-cellulosic components such as hemicellulose in the treated fibers. SEM micrographs revealed enhanced interfacial bonding between the NaOH-treated fibers and the epoxy matrix, with reduced signs of debonding. Thermogravimetric analysis indicated improved thermal stability in composites containing treated fibers, as reflected by a higher degradation temperature. Mechanical properties such as tensile and flexural strength and modulus, as well as impact resistance, however, did not exhibit significant improvements, which might also be affected by the variability in the natural fibers and the hand lay-up method. These findings emphasize both the promise of Nito fiber as a viable natural reinforcement and the importance of consistent processing methods in composite fabrication. Overall, this work supports the favorable transition toward natural fibers in composite applications, particularly where thermal performance is prioritized.

Abstract: Lateral flow immunoassay (LFIA) is a widely used immunoassay technology known for its simplicity, rapid detection, and low cost. However, conventional LFIA primarily relies on the passive diffusion of nanoparticle probes and immunocomplexes, resulting in low immunoreaction efficiency and less sensitive immune response. In this study, we developed an active propulsion-driven Au@mSiO₂@Pt Janus nanomotor to enhance the detection performance of LFIA. The Pt nanolayer in the nanomotor serves as a catalyst for H₂O₂ decomposition, generating a self-propulsion force that facilitates antigen-antibody interactions. These nanomotors were then employed as probes in LFIA to improve detection sensitivity. Additionally, we developed a portable image-processing biosensor to validate the effectiveness of this strategy. To demonstrate the analytical performance of nanomotor-LFIA, we selected C-reactive protein (CRP) and serum amyloid A (SAA) as target biomarkers. The results confirmed the multiplex quantitative detection capability of our system, achieving detection limits (LOD) of 0.9 ng/mL for CRP and 1 ng/mL for SAA. This work provides a novel strategy for improving immunoassay sensitivity. We believe that nanomotor-driven LFIA holds great potential for future high-sensitivity point-of-care testing (POCT) applications.

Abstract: The growing demand for sustainable alternatives to petroleum-based polymer materials has driven the development of bio-based materials. Among them, chitosan stands out as a promising biopolymer due to its biodegradability and biocompatibility. However, its hydrophilicity, causing high water absorption, limits its practical applications. In this study, tannic acid was employed as a cross-linking agent, and chitin nanofibers (ChNFs) were introduced as a reinforcing agent to enhance the properties of the chitosan-based films. The incorporation of ChNFs significantly improved the tensile stress of the films without compromising their transparency. Furthermore, the cross-linked chitosan films with ChNFs exhibited excellent UV-blocking capabilities. This highlights their potential as an alternative to conventional petroleum-based polymers.

Abstract: Aluminium based MMNCs have gained significant traction across various industries due to their superior stiffness, strength-to-weight ratio, and enhanced mechanical and tribological properties. Despite extensive research in this field, the application of ML techniques to predict the properties of these materials remains limited. Present work aims to predict the wear rate of A-MMNCs based on their chemical compositions. The nanocomposites were fabricated using ultrasonic assisted stir casting method and studied their wear results. Classification models achieved an accuracy of 0.92 with SVM, 0.95 with KNN, and 0.97 with ANN. Additionally, prediction models for wear rate yielded R² values of 0.8876 with linear regression and 0.9165 with ANN, with minimal MAE for the ANN model. Genetic algorithms were employed to optimize wear test parameters.

Abstract: Machine learning (ML) algorithms can improve and innovate the design of new, eco-friendly composite materials. Therefore, this study aims to forecast tensile strength for polyvinyl alcohol composite reinforced by crystalline nanocellulose (CNC) through ML regression algorithms. Moreover, 107 datapoints from the literature were used to train and test ML models. However, this dataset had missing values for the input variables considered, so an Iterative Imputation with an Extra Tree (ET) Regressor model as estimator was performed, which reached a determination coefficient of 0.88. This study implemented five ML algorithms to predict tensile strength: Adaptive Boosting, Extreme Gradient Boosting, Random Forest, ET, and Gradient Boosting (GB). Additionally, a hyperparameter optimization was carried out using the Random Search optimization technique, obtaining that the GB optimized model had the highest precision with a determination coefficient of 0.97. Moreover, it was determined that PVA hydrolysis degree, CNC percentage, and CNC diameter were the most important variables for the GB-optimized model through SHAP analysis.