Biofuels and nanocatalysts: A Data Mining study

Abstract

A myriad of scientific documents is produced annually on the most diverse topics. Thus, understanding the paths taken during scientific advances in a given area is often challenging to map, and scientific fortunes are hidden in these documents. Therefore, developing strategies for understanding advances in topics of interest is crucial for good scientific work. Among the most relevant themes of modernity, the use of renewable resources for the production of biofuels attracts the attention of several countries, constituting a vital part of the global geopolitical chessboard since humanity's energy needs will grow faster and faster. Fortunately, advances in personal computing associated with free and open-source software production greatly facilitate this work of prospecting and understanding complex scenarios. Thus, for the development of this work, the keywords biofuel and nanocatalyst were delivered to the Scopus database, which returned 1071 scientific articles. The titles and abstracts of these papers were saved in RIS format and submitted to automatic analysis via the Visualization of Similarities Method implemented in VOSviewer 1.6.18 software. Then, the data extracted from the VOSviewer were processed by software written in Python, which allowed using the network data generated by the Visualization of Similarities Method. Thus, it was possible to establish the relationships for the pair between the nodes of all clusters classified by Link Strength Between Items or Terms (LSBI) or by year. This approach allowed us to infer that the most recent pairs of terms associate the need to produce biofuels from oils produced by microorganisms and the use of cerium oxide nanoparticles to improve the performance of fuel mixtures by reducing the emission of hydrocarbons and NOx.

A 30-Year Review on Nanocomposites: Comprehensive Bibliometric Insights into Microstructural, Electrical, and Mechanical Properties Assisted by Artificial Intelligence

“…The research landscape is refined using network graph methodologies to identify key research nodes and their interconnections [31,120,140,215,217,218,301,302,[441][442][443][444][445][446][447][448][449][450][451], spotlighting pivotal works and active research clusters, especially in emerging research hubs like the United States, China, and India.…”