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.

Thermal and mechanical properties of blends of nucleated heterophasic polypropylene copolymer (HECO) (80% homopolypropylene matrix and 20% ethylene-propylene rubber) with Vistamaxx™ (VM) statistical copolymer (85 wt% propylene and 15 wt% ethylene) have been investigated. The nucleating agent polyvinylcyclohexane (PVCH) has shown an improved crystallization behavior of the blends, maintaining high crystallization temperatures between 118.5 °C and 129.0 °C (± 0.2 °C). The small shift in the crystallization temperature indicates a very fast crystallization and excellent compatibility in the blend. The morphology and crystallization of different blend compositions have been investigated with small-angle and wide-angle X-ray scattering (SAXS–WAXS) and polarized light microscopy (PLM). The formation of small crystallites, uniformly distributed within the blends, leads to not only the formation of toughened materials but also a novel class of rubbery blends with high ultimate tensile strength, elastic modulus, and strain recovery. The results suggest that the optimized blend composition, in conjunction with nucleation, is a highly effective approach for superior physical cross-linking in the investigated elastomeric blends, offering both improved mechanical performance and environmental benefits by introducing a new class of advanced elastomers.