Nikhil Kumar

The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin’s complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.

The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin’s complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.

Deep eutectic solvents (DESs) are developing as an alternate medium for aromatic extraction, especially benzene and thiophene from aliphatic hydrocarbon mixtures. In this work, molecular dynamics (MD) simulations were first used to investigate the solvation structure of benzene, thiophene, and n-hexane in monoethanolamine-based DESs. It reveals the liquid structures in the adjacent neighbor shells, which is a function of electron-withdrawing sulfur attached to thiophene and the π-electron cloud of benzene. The intermolecular forces between aromatic, aliphatic, and DES components are analyzed in van der Waals and hydrogen bond interactions. The chloride ions serve as a charge carrier bridge between choline and monoethanolamine precursors. The solvation of benzene, thiophene, and n-hexane in the DESs depends on volume expansion and minor solvent structural changes. Density functional theory results provided information on the mechanism of short-range interactions between organic solutes and studied DES. It aids in understanding the structural orientations of a DES with the addition of solutes, essential to the formation of DES. The solvation shell structure and characteristics were investigated in tandem with the possibility of benzene and thiophene clustering. The ¹H NMR and 2D ¹H-¹H-NOESY were used to investigate the intermolecular interactions between benzene, thiophene, and n-hexane with monoethanolamine-based solvents. It concludes that high-ordered DES1 is more inclined to higher solubility than lower-ordered ones with a higher molar ratio of monoethanolamine. The solvation was reduced because the entropy gain was not maximized in the lower ordered DESs.