Deep eutectic solvent–assisted biorefineries: Mechanistic biomass deconstruction, hierarchical materials assembly, and AI-driven process feasibility

The growing demand for sustainable materials and energy has positioned lignocellulosic biomass as a renewable resource. However, structural recalcitrance, heterogeneous chemistry, and energy-intensive pretreatments constrain efficient deconstruction and valorization. This review examines the molecular-scale mechanisms governing biomass deconstruction and the transition from conventional mechanical, chemical, and enzymatic pretreatments toward emerging deep eutectic solvent (DES) platforms. DES have gained prominence as designer solvents due to their tunable supramolecular chemistry, low volatility, and potential environmental compatibility, enabling selective fractionation of lignocellulosic components under mild conditions. Advances in feedstock-specific and multicomponent DES systems and their integration with physical and biological treatments are examined. Molecular dynamics simulations, thermodynamic modeling, and spectroscopy elucidate DES–biomass interactions, including hydrogen-bond reorganization, lignin–carbohydrate complex disruption, and recovered biopolymers. The review introduces a unified structure–mechanism–performance–feasibility framework linking DES molecular design and biomass architecture to deconstruction, recovered-fraction quality, hierarchical material assembly, and process viability. Within this framework, molecular simulation, machine learning, techno-economic analysis, and life-cycle assessment are integrated as predictive and decision support tools for solvent selection, process optimization, and scale-up. This integrated perspective connects molecular-scale DES design with product-oriented material development and system-level feasibility, guiding DES-enabled smart biorefinery development while identifying key challenges and opportunities for scalable, circular bioeconomy solutions.