Systematic computational prediction and experimental confirmation of amino acid-based natural deep eutectic solvents for removal of sterically hindered trisulfur

Abstract

Amino acid-based deep eutectic solvents (DESs) provide a sustainable and efficient solution as extractant, addressing environmental challenges related to sulfur removal from fuels. In this study, 100 DESs structures were geometrically optimized by Turbomole and Conductor-like Screening Model for Real Solvents (COSMO-RS) was used to predict their desulfurization efficiencies of three sterically hindered sulfur. Experimental validation using 10 amino acid-based DES shows a strong correlation (R² > 0.8), confirming the reliability of the computational model. Sigma profile analysis revealed that all selected DESs exhibit dual functionality as hydrogen bond donors (HBDs) and acceptors (HBAs), enhancing their affinity for sulfur compounds. Notably, DESs with strong hydrogen bond donor capability prioritize the removal of thiophene (T) and benzothiophene (BT), while DESs containing both strong hydrogen bond donors and aromatic rings exhibit superior performance in removing dibenzothiophene (DBT). Additionally, COSMO-RS predictions for key physicochemical properties, including viscosity and density, were evaluated. DESs with lower viscosity and appropriate amount of density were found to perform better in removing sulfur, owing to enhanced mass transfer and easier handling. This comprehensive study demonstrates the potential of COSMO-RS as a reliable predictive tool for assessing desulfurization capabilities and for guiding the design of DESs with optimized properties. The findings provide importance into the formulation of DESs for industrial-scale desulfurization processes, contributing to cleaner and more sustainable fuel production.