Supported gold nanoparticles have proven to be highly effective catalysts for the base-free oxidation of furfural, a compound derived from biomass. Their small size enables a high surface-area-to-volume ratio, providing abundant active sites for the reaction to take place. These gold nanoparticles serve as catalysts by providing surfaces for furfural molecules to adsorb onto and facilitating electron transfer between the substrate and the oxidizing agent. The role of the support in this reaction has been widely studied, and gold-support interactions have been found to be beneficial. However, the exact mechanism of furfural oxidation under base-free conditions remains an active area of research and is not yet fully understood. In this review, we delve into the essential factors that influence the selectivity of furfural oxidation. We present an optimization process that highlights the significant role of machine learning in identifying the best catalyst for this reaction. The principal objective of this study is to provide a comprehensive review of research conducted over the past five years concerning the catalytic oxidation of furfural under base-free conditions. By conducting tree decision making on experimental data from recent articles, a total of 93 gold-based catalysts are compared. The relative variable importance chart analysis reveals that the support preparation method and the pH of the solution are the most crucial factors determining the yield of furoic acid in this oxidation process.
Keywords: base-free; decision tree; furfural; gold; machine learning; oxidation.