Bibliometric insights into metal-organic frameworks modified with metal-based materials for hydrogen storage: Prospects, opportunities and challenges

• Main Authors: Abdulkadir, B. A., Herma Dina, Setiabudi
• Format: Article
• Language: English; English
• Published: Elsevier Ltd 2024
• Subjects: TP Chemical technology
• Online Access: http://umpir.ump.edu.my/id/eprint/43161/1/Bibliometric%20insights%20into%20metal-organic%20frameworks_ABST.pdf; http://umpir.ump.edu.my/id/eprint/43161/2/Bibliometric%20insights%20into%20metal-organic%20frameworks.pdf; http://umpir.ump.edu.my/id/eprint/43161/; https://doi.org/10.1016/j.jtice.2024.105893; https://doi.org/10.1016/j.jtice.2024.105893

Backgrounds: Solid-state hydrogen storage technology is known for its reliability and cost-effectiveness. Porous materials such as metal-organic frameworks (MOFs) are a major area of interest because of their high surface area and porosity. Recent advancements in modifying MOFs with metal-based materials offer new avenues for enhancing their storage capacity and stability. However, the research landscape in this area remains complex, necessitating a comprehensive overview. Methods: This review critically examines recent studies on MOFs using metal-based materials to enhance hydrogen storage capacities. A bibliometric analysis was conducted to identify and analyse the key research related to MOFs, such as publication trends, author collaborations, and research clusters. The paper also explores MOFs' synthesis, characterisation, and hydrogen adsorption processes. Visual analytical tools were employed to map research trajectories and identify gaps in the literature. Significant findings: The analysis reveals a considerable increase in publications related to this field, with a rise in interdisciplinary collaborations between materials science and engineering. Key findings highlight that metal modifications enhance hydrogen storage kinetics. Despite the promising advancements, challenges persist, including material stability under high operational conditions. The work further identifies the current state of research and outlines critical opportunities for future exploration, such as integrating machine learning techniques. Key research trends and collaborative networks were identified, shedding light on the advancements in this area.