This study presents a new concept of refining and enhancing the properties of cast aluminum alloys by adding nanoparticles. In this work, the effect of adding alumina (Al₂O₃), titanium dioxide (TiO₂) and zirconia (ZrO₂) nano-particles (40 nm) to the aluminum cast alloy A356 as a base metal matrix was investigated. Alumina, titanium dioxide and zirconia nano-powders were stirred in the A356 matrix with different fraction ratios ranging from (0%–5%) by weight at variable stirring speeds ranging from (270, 800, 1500, 2150 rpm) in both the semisolid (600 °C) and liquid (700 °C) state using a constant stirring time of one minute. The cast microstructure exhibited change of grains from dendritic to spherical shape with increasing stirring speed. The fracture surface showed the presence of nanoparticles at the interdendritic spacing of the fracture surface and was confirmed with EDX analysis of these particles. The results of the study showed that the mechanical properties (strength, elongation and hardness) for the nanoreinforced castings using Al₂O₃, TiO₂ and ZrO₂ were enhanced for the castings made in the semi-solid state (600 °C) with 2 weight% Al₂O₃ and 3 weight% TiO₂ or ZrO₂ at 1500 rpm stirring speed.

Green hydrogen production is emerging as a crucial component in global decarbonization efforts. This review focuses on the role of computational approaches and artificial intelligence (AI) in optimizing green hydrogen technologies. Key approaches to improving electrolyzer efficiency and scalability include computational fluid dynamics (CFD), thermodynamic modeling, and machine learning (ML). As an instance, CFD has achieved over 95% accuracy in estimating flow distribution and polarization curves, but AI-driven optimization can lower operational expenses by up to 24%. Proton exchange membrane electrolyzers achieve efficiencies of 65–82% at 70–90 °C, but solid oxide electrolyzers reach up to 90% efficiency at temperatures ranging from 650 to 1000 °C. According to studies, combining renewable energy with hydrogen production reduces emissions and improves grid reliability, with curtailment rates of less than 1% for biomass-driven systems. This integration of computational approaches and renewable energy ensures a long-term transition to green hydrogen while also addressing energy security and environmental concerns.

The utilization of steel slag for industrial and construction purposes has gained significant attention in recent years due to its abundant availability and potential environmental benefits. This study explores the feasibility of utilizing steel slag as a coarse aggregate in concrete, aiming to determine the optimal replacement percentages for desired mechanical and structural properties. Experimental investigations were conducted, substituting steel slag for conventional coarse aggregate in varying proportions. The concrete mixtures were tested for compressive strength, flexural strength, workability, and other parameters. The results demonstrate the promising potential of incorporating steel slag as a partial replacement for coarse aggregate. The mixture with 30% steel slag replacement exhibited favorable strength properties and workability. Both compressive and flexural strengths showed significant improvement, highlighting the viability of steel slag as an alternative to conventional coarse aggregate. The findings provide valuable guidance for engineers, researchers, and decision-makers in the construction industry regarding the practical benefits and considerations of using steel slag in concrete mixtures. This utilization promotes resource efficiency, reduces environmental impact, and maintains or enhances the mechanical and workability properties of concrete.

H13 die steel is characterized by its high hardness and need for special surface features that are obtained by nontraditional machining processes. Electrical discharge machining (EDM) is used to machine hard materials and to produce complicated shapes. In this work, different EDM process parameters are investigated on H13 die steel. Several experiments are conducted to study the effect of three process parameters: peak current (Ip), pulse on-time (Ton) and electrode material on the machining process of H13 die steel. The machining process is evaluated by material removal rate (MRR), electrode wear ratio (EWR%) and surface roughness (SR) as indicators of the process efficiency in terms of quality and cost. Taguchi method was used to investigate the significant effect of process parameters on the performance measurements and the optimal parameters of the EDM process. For analysis and explanations Minitab version 17 software was used. Different process parameters were experimentally investigated and statistically analyzed and the results showed that the copper electrode leads to the highest MRR and lowest EWR%; whereas the brass electrode leads to the lowest SR.