Python Scripts for Advancing Aerogel Research for Energy Storage: Bibliometric Trends and Predictive Insights from Machine Learning and Deep Learning

Aerogel, a unique class of nanoporous materials, has gained significant attention from researchers in energy storage owing to its unique properties, e.g., high surface area and porosity, lightweight and low density, thermal insulation, and electrical conductivity. Aerogels are used as electrode materials in supercapacitors, lithium ion batteries (LIB), and also employed in thermal energy storage. In this study, a bibliometric assessment based on the Scopus database was carried out on the topic: “aerogel in energy storage”. The progress of aerogel applications in energy storage is systematically analyzed based on articles, authors, journals, countries, institutions and cited references. Burst keywords have been analyzed to get insights about the research trend. Machine learning and deep learning analyses have been carried out to identify homogeneity in the database and to predict future research trends. This work presents an overview for the beginners to understand the research trend on the application of aerogel in energy storage systems all over the world and can be helpful in predicting the future development of aerogel in energy storage systems.