Key Engineering Materials Vol. 1027

Abstract: The periodontitis leads to the formation of periodontal pockets, which provide an ideal site for localized antimicrobial drug delivery for therapy. In this study, metronidazole (Met)-loaded in-situ matrix (ISM) systems were developed to assess their physical and drug release characteristics. Bleached shellac (BS) and fatty compounds such as beeswax, carnauba wax, candelilla wax, shellac wax, and stearic acid (SA) were tested as matrix-forming agents. ISM systems containing BS and SA in BS ratios of 2:1, 1:1, and 1:2 at 30% w/w were chosen because they resulted in clear, liquid-like ISMs. Increasing the BS content raised both the density and viscosity of the ISM. Upon injection into simulated crevicular fluid these liquid ISMs quickly formed solid matrices. Drug release was notably prolonged, with only around 6% released within the first day and sustained over six days. Met-loaded BS-SA ISM shows promise as a delivery system for intra-periodontal pocket route. However, further studies should focus on modulating release profile to improve drug delivery.

Abstract: This study investigates the morphological, structural, and bioactive properties of strontium-doped bioactive glass (Sr-BG) and copper-strontium-doped bioactive glass (Cu-Sr-BG) scaffolds to enhance their potential for biomedical applications. Scanning electron microscopy (SEM) revealed that both Sr-BG and Cu-Sr-BG scaffolds feature smooth, highly porous surface morphologies with interconnected pores (120–150 µm) created using a foaming agent. This pore network facilitates cell attachment and proliferation. Fourier transform infrared (FTIR) analysis confirmed the preservation of the silica network, with characteristic Si–O–Si bending and stretching peaks remaining consistent after Cu doping. X-ray diffraction (XRD) analysis demonstrated that both scaffolds retained an amorphous structure, with Cu doping successfully incorporated without disrupting this feature. Both Sr-BG and Cu-Sr-BG scaffolds exhibited excellent bioactivity, forming an apatite layer on their surfaces after immersion in simulated body fluid (SBF), indicating strong potential for bone tissue engineering applications. These findings suggest that Sr-and Cu-doped bioactive glass scaffolds possess promising characteristics for promoting cell attachment and osteoconductivity, positioning them as viable candidates for future biomedical applications in bone regeneration

Abstract: This study investigates the impact resistance of 3D-printed polymers, including PLAWM, PLA, and ABS. ABS showed the highest impact resistance with an average damage area of 150 mm² and a standard deviation of 8 mm². PLA had moderate resistance, averaging 180 mm² with a 10 mm² standard deviation. PLA-WM exhibited the least resistance, with an average damage area of 220 mm² and a 15 mm² standard deviation—50% more than ABS and 22% more than PLA. These findings provide insights for optimizing 3D printing processes for high-durability applications.

Abstract: The demand for coronary artery stents has risen due to global healthcare advancements. However, conventional fabrication methods often fail to meet clinical standards for dimensional accuracy and efficiency. This study aims to determine optimal 3D printing parameters for high precision polylactic acid (PLA) stents using fused deposition modeling (FDM). Twenty-six parameter combinations were designed via response surface methodology (RSM), varying printing speed (4–8 mm/s), nozzle temperature (190–220°C), orientation (XY, ZY), and layer thickness (0.05–0.2 mm). Optical microscopy was used to assess dimensional accuracy against computer-aided design (CAD) models. Only six combinations successfully printed stents, and four were further optimized. The best-performing sample showed a length deviation of 1.21% and a width deviation of 33%. Results confirm that printing speed critically affects dimensional precision. The study concludes that optimal FDM settings are material-specific; for PLA, the best range was 3–4 mm/s speed, 190–220°C nozzle temperature, ZY orientation, and 0.1–0.2 mm layer thickness. These findings support the feasibility of using FDM to fabricate dimensionally accurate PLA stents for coronary applications.

Abstract: For fused deposition modeling (FDM) 3D printing, maintaining consistent local ambient temperature is critical to prevent warping, poor layer adhesion, and dimensional errors. This study investigated the efficacy of a metal sheet enclosure in preserving thermal uniformity during FDM printing, addressing the limited information on metal enclosures' performance. Experiments were conducted using ABS filament on a Creality Ender 3 Pro, comparing an open-frame setup with a custom 500x600x620mm metal sheet enclosure. Results show the metal enclosure significantly enhanced thermal stability. The open-frame setup exhibited unstable temperatures, fluctuating from a peak of 58.0°C to 36°C, averaging 49.5°C. Conversely, the metal enclosure maintained a stable plateau between 65-67°C for most of the print, reaching a peak of 72.0°C and averaging 63.0°C. Qualitatively, open-frame prints displayed surface cracking and suboptimal layer adhesion, while enclosed prints showed markedly improved surface finish with no visible cracks and smoother layers. The metal enclosure acts as a thermal buffer, mitigating environmental changes and convective heat loss, thus improving print quality and reliability by ensuring thermal consistency in FDM 3D printing.

Abstract: Metallic Glasses (MGs) have unique mechanical and physical properties making them highly desirable for applications across various fields. However, some MGs have poor Glass-Forming Ability (GFA) and conventional methods to improve it are time-consuming, resource-demanding, and costly. In this study, advanced machine learning (ML) techniques are leveraged to develop robust and data-driven models capable of predicting the critical diameter (Dmax) of MGs from the concentrations of their constituent elements. Dmax is an essential indicator for GFA, whereby higher Dmax values indicate better GFA. A comprehensive dataset encompassing 8,734 MG alloys and their associated Dmax was compiled, cleaned, and analyzed from various sources. The Gradient Boosting model was the best-performing predictive model achieving a R² of 0.86 and a RMSE of 1.66 mm for estimating Dmax, outperforming other models such as Random Forest and XGBoost. Furthermore, the SHAP (SHapley Additive exPlanations) analysis was utilized to rank the importance of individual elements of the alloys, identifying Zirconium (Zr) as the most influential feature in predicting Dmax. Additionally, pseudo-ternary diagrams were generated based on the Gradient Boosting model to identify potential novel BMGs with enhanced GFA. The model's robustness and utility were validated by comparing the Dmax values predicted by the ML model to experimentally obtained values for the Ni_76-xFe_xP₁₄B₆Ta₄ alloy across varying Fe concentrations (x). The results of the study enhance the accuracy of GFA predictions and establish a robust data-driven framework for expediting and automating the discovery of novel BMGs.

Abstract: Accurate prediction of Hansen Solubility Parameters (HSPs) is important for understanding chemical compatibility in fields like pharmaceuticals, cosmetics and chemical engineering. This study aims to enhance HSP prediction by employing machine learning techniques and using a large, extended dataset from the Hansen Solubility Parameter in Practice (HSPiP) software. Models like XGBoost, CatBoost, LightGBM, as well as ensemble methods, were used for regression, optimized through hyperparameter tuning, feature selection and evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-squared (R²) metrics. The results indicated that using a wide variety of molecular components improves prediction accuracy and enhances the model’s applicability across different compounds. The findings additionally show that advanced machine learning methods can significantly improve HSP prediction accuracy, facilitating more precise solubility estimates and advancing applications in chemical and materials science.

Abstract: At present intensification of ion exchange processes in water treatment systems is widespread. Mainly technological and constructive methods are used. It is proposed to apply a magnetic field to modify the processes of ion exchange when adjusting the mineral composition of water. The study evaluates the method of magnetic modification of ion exchange processes on changes in the working dynamic capacity of the ionite, the amount of sorbed salts, the duration of the filter cycle and the flow rate of the regeneration solution. Results of method application were obtained. For the qualitative and quantitative analysis of the investigated water, cationite KU-2x8 and anionite AN-22 were used. Proposed method of modification of the ion exchange process due to simultaneous influence of magnetic field on the ion exchanger and purified water.

Abstract: This study investigates the impact of chemical and thermal activation on the sorption capacity of Transcarpathian clinoptilolite from the Sokyrnytsky deposit. The chemical activation of natural clinoptilolite was performed using aqueous solutions of 5% HCl and 25% H₂SO₄ at a solid-to-liquid phase ratio of 1:10. Thermal activation was conducted at a temperature of 300°C. The sorption capacity of both natural and activated clinoptilolite was evaluated for water vapor in this thermal analysis. The ability of activated clinoptilolite to absorb direct blue dye was determined through spectrophotometric analysis. The partial degradation of clinoptilolite due to acid modification was confirmed by X-ray phase analysis and electron microscopy. The sorption isotherms of direct blue dye were modeled using the Langmuir equation, and the corresponding constants were determined. Clinoptilolite activated with a 25% aqueous H₂SO₄ solution is recommended for use in sorption technologies for wastewater treatment from direct dyes.