Molybdenum carbide co-catalyst and carbon nanofiber matrix are suggested to improve the nickel activity toward methanol electrooxidation process. The proposed electrocatalyst has been synthesized by calcination electrospun nanofiber mats composed of molybdenum chloride, nickel acetate, and poly (vinyl alcohol) under vacuum at elevated temperatures. The fabricated catalyst has been characterized using XRD, SEM, and TEM analysis. The electrochemical measurements demonstrated that the fabricated composite acquired specific activity for methanol electrooxidation when molybdenum content and calcination temperature were tuned. In terms of the current density, the highest performance is attributed to the nanofibers obtained from electrospun solution having 5% molybdenum precursor compared to nickel acetate as a current density of 107 mA/cm² was generated. The process operating parameters have been optimized and expressed mathematically using the Taguchi robust design method. Experimental design has been employed in investigating the key operating parameters of methanol electrooxidation reaction to obtain the highest oxidation current density peak. The main effective operating parameters of the methanol oxidation reaction are Mo content in the electrocatalyst, methanol concentration, and reaction temperature. Employing Taguchi’s robust design helped to capture the optimum conditions yielding the maximum current density. The calculations revealed that the optimum parameters are as follows: Mo content, 5 wt.%; methanol concentration, 2.65 M; and reaction temperature, 50 °C. A mathematical model has been statistically derived to describe the experimental data adequately with an R² value of 0. 979. The optimization process indicated that the maximum current density can be identified statistically at 5% Mo, 2.0 M methanol concentration, and 45 °C operating temperature.

End milling is one of the most used machining techniques for removing metal from objects and milled surfaces. It is widely used in tools, aerospace, automotive, machine design, and production for joining other parts. Surface roughness is a crucial indicator of a product's technological excellence and a component that significantly affects manufacturing costs. The effects of several factors of the milling process, such as rotational speed, cutting speed, depth of cut, number of cutting edges and feed rate, find their influence on the surface finish. Surface roughness, cutting forces, and material removal rate were chosen as process responses. Taguchi method and the RSM in the evaluation and optimization of the machining parameters of end milling for the machining of Al 6063/SiO2 by using Minitab 19. The vertical milling machine was used to perform experiments based on the Taguchi L27 orthogonal array. The rotational speed, cutting speed, weight percentages of Nano SiO2 (1, 3 and 5), number of cutting edges, feed rate, and depth of cut were considered machining parameters. According to ANOVA results, additions and cutting speed are the factors that have the greatest impact on the model metal removal rate.

This current work carries out an extensive analysis of the optimization of poly (methyl methacrylate) (PMMA) dental composites utilizing the Taguchi technique. The PMMA composites have been enriched using multiple graphene-based nanofillers. A mixed-level L16 orthogonal array was developed to examine the impact of three key parameters, kind of filler, loading level, and curing process, on tribological and mechanical attributes. The outcomes of the study responses comprised the coefficient of friction (COF), weight loss (WL), and hardness (H). It can indicate that the curing process exerted the biggest impact on hardness (33.75%), succeeded by filler type (22.09%) and loading level (10.25%). The outcomes indicate that the most suitable conditions for decreasing COF and WL and maximum hardness were identified as graphene filler, 0.5 N loading, and heat curing. The RSM models revealed a good prediction accuracy, with correlation values (R²) of 0.9468, 0.9725, and 0.9989 for COF, WL, and hardness, respectively. Multi-objective optimization applying desire function analysis proved these settings as the most successful. The merger of Taguchi and RSM techniques gives a solid approach for optimizing nanocomposite features in advanced dental applications.