Deep Eutectic Solvents in Lignocellulosic Biorefineries: A Comprehensive Review of Mechanistic Insights, Molecular Modeling, and Artificial Intelligence

Abstract

Deep eutectic solvents (DESs) have gained significant attention as solvents and catalysts in a wide range of research applications owing to their attractive properties, ease of preparation, low cost, and environmental friendliness. In recent years, the application of DESs in lignocellulosic biomass pretreatment has been growing rapidly, driven by the ability to tailor DES compositions to meet the specific processing needs of different biomass components. Despite the recent progress, biomass pretreatment using DESs still lacks a comprehensive understanding of the relevant interactions and designing principles. Here we review emerging strategies that combine experimental approaches, computational chemistry, and machine learning (ML) techniques to develop DESs with improved pretreatment efficiency. We summarize studies that investigate the effects of DES pretreatment on lignocellulosic biomass and what is known about the underlying mechanisms. The role of quantum chemical calculations and molecular dynamics simulations in revealing molecular-level interactions and plausible depolymerization mechanism is discussed. The implementation, challenges and limitations of ML are discussed in DES-based biomass pretreatment together with DES recovery methods aimed at reducing pretreatment costs on the economic feasibility and life-cycle environmental impacts of DES-based processes. Finally, we outline future research directions for advancing DES applications in lignocellulosic biomass pretreatment.