Lignin is the most abundant natural source of aromatic chemicals. Approaches for lignin valorization typically rely on liquid-phase reactions, which means that the solvent, or the solvent mixture, must be carefully selected to promote lignin solubility. Deep Eutectic Solvents (DES) – solvents composed of a hydrogen bond donor (HBD) and acceptor (HBA) in varying ratios – and their interactions with lignin and lignin model compounds have received significant attention due to their promising application to lignin valorization, as reported by both experimental and computational studies. In particular, the experimental study by Soares et al. (2017) on 4 lignin model compounds (ferulic acid, syringaldehyde, syringic acid and vanillic acid) and 36 different DES showed that adding a small amount of water to the solvent can substantially enhance lignin solubility due to hydrotropy. However, the large number of possible DES systems (particularly when mixed with water or other cosolvents) makes solvent selection a challenge without laborious trial-and-error experimentation. In this work, we present computational methods to predict lignin solubility to facilitate the rapid screening of solvent systems. In our set of calculations, we replicated the aforementioned experimental mixtures by Molecular Dynamics (MD) simulations, and further investigated the hydrogen bonding mechanism in the mixed solvent environment by using graph network theory. By parameterizing a regression model to predict experimentally reported solubilities, our results reveal three important descriptors of solubility: (1) the system temperature, (2) the hydroxyl fraction of the solute, and (3) the ratio of water molecules that specifically form hydrogen bonds with the HBA component of the DES. This latter descriptor suggests that the HBA is the hydrotrope within the system. Motivated by these initial calculations, we next simulate actual lignin compounds with varying combinations of linkages and monolignols to demonstrate that relate solubility predictions for model compounds to lignin oligomers. These findings provide new molecular-scale insight into lignin solubilization mechanisms and a new method for screening potential solvent systems for lignin valorization.

Data-centric development of lignin structure-solubility relationships using molecular simulations

ACS Spring 2022

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