Multi-scale computational screening and mechanistic insights of cyclic amines as solvents for improved lignocellulosic biomass processing

Lignocellulosic biomass is a promising feedstock for production of affordable fuels and chemicals from renewable resources. Effective solubilization and subsequent deconstruction of its cellulose, hemicellulose, and lignin fractions is essential for the viability of future biorefineries. This study used quantum chemistry-based equilibrium thermodynamics methods to evaluate the potential of 650 cyclic amines to solubilize cellulose, hemicellulose, and lignin. The activity coefficients of solvent - biopolymer interactions were predicted using the COSMO-RS (COnductor-like Screening MOdel for Real Solvents) method and used to identify cyclic amines that can efficiently dissolve and extract selective fractions of biopolymers during biomass pretreatment. Among the 650 cyclic amines, 1-piperazineethanmaine was predicted to be an effective solvent for extracting all three polymers and was experimentally shown to achieve the highest lignin removal (97.1%). Non-covalent interaction, reduced density gradient and quantum chemical calculations were performed to elucidate the dissolution mechanism of lignin, cellulose and hemicellulose and gain further molecular level insights into the interactions between the cyclic amines and biomass polymers that promote efficient solubilization and extraction. These analyses indicated that 1-piperazineethanmaine and 1-methylimidazole make noncovalent van der Waals, electrostatic interactions and hydrogen bonding with lignin, leading to enhanced lignin removal, while the strong intramolecular hydrogen bonding interactions in cellulose and hemicellulose result in weaker solvent-biopolymer interactions. Overall, the computational approach provided an efficient method for identifying cyclic amines tailored for optimal biomass pretreatment and resulted in the identification of a potential new class of solvents for effective biomass pretreatment.