Hydrogen is viewed as the future carbon–neutral fuel, yet hydrogen storage is a key issue for developing the hydrogen economy because current storage techniques are expensive and potentially unsafe due to pressures reaching up to 700 bar. As a consequence, research has recently designed advanced hydrogen sorbents, such as metal–organic frameworks, covalent organic frameworks, porous carbon-based adsorbents, zeolite, and advanced composites, for safer hydrogen storage. Here, we review hydrogen storage with a focus on hydrogen sources and production, advanced sorbents, and machine learning. Carbon-based sorbents include graphene, fullerene, carbon nanotubes and activated carbon. We observed that storage capacities reach up to 10 wt.% for metal–organic frameworks, 6 wt.% for covalent organic frameworks, and 3–5 wt.% for porous carbon-based adsorbents. High-entropy alloys and advanced composites exhibit improved stability and hydrogen uptake. Machine learning has allowed predicting efficient storage materials.

Selective laser sintering (SLS) is a bed fusion additive manufacturing technology that facilitates rapid, versatile, intricate, and cost-effective prototype production across various applications. It supports a wide array of thermoplastics, such as polyamides, ABS, polycarbonates, and nylons. However, manufacturing plastic components using SLS poses significant challenges due to issues like low strength, dimensional inaccuracies, and rough surface finishes. The operational principle of SLS involves utilizing a high-power-density laser to fuse polymer or metallic powder surfaces. This paper presents a comprehensive analysis of the SLS process, emphasizing the impact of different processing variables on material properties and the quality of fabricated parts. Additionally, the study explores the application of machine learning (ML) techniques—supervised, unsupervised, and reinforcement learning—in optimizing processes, detecting defects, and ensuring quality control within SLS. The review addresses key challenges associated with integrating ML in SLS, including data availability, model interpretability, and leveraging domain knowledge. It underscores the potential benefits of coupling ML with in situ monitoring systems and closed-loop control strategies to enable real-time adjustments and defect mitigation during manufacturing. Finally, the review outlines future research directions, advocating for collaborative efforts among researchers, industry professionals, and domain experts to unlock ML’s full potential in SLS. This review provides valuable insights and guidance for researchers in regard to 3D printing, highlighting advanced techniques and charting the course for future investigations.