A solvent-selection strategy for the hydrogenation reaction inside a tubular-flow reactor through a statistical approach

Abstract

Solvent selection is crucial for optimizing reaction outcomes of various reactions. Herein, we conducted nitrobenzene hydrogenation in a tubular-flow reactor coated with a Pd/TiO₂ catalyst using 17 different solvents and exhaustively studied the solvent effect through statistical analysis. Results show that protic solvents provide higher conversion and aniline production than aprotic ones, but solvent study could not be interpreted using simple regression alone. Rigorous analyses revealed that the hydrogen donor and acceptor abilities of a solvent are the most important factors assisting nitrobenzene reduction. Importantly, solvent solubility in H₂O and dipole moment are key sub-factors influencing nitrobenzene conversion and aniline yields, which were validated using the statistical analysis of 57 solvent parameters. Our regression model predicts that 2,2,2-trifluoroethanol is a suitable solvent for hydrogenation reactions.