Lignocellulosic biomass presents a promising source of renewable carbon that can enable the sustainable generation of fuels, chemicals, and consumer goods that underlie our modern economy. Recent developments in reductive catalytic fractionation (RCF) processes have shown that easy separation of lignin monomers from the hemicellulose and cellulose fractions is achieved. Despite these advances, the design of RCF and other such lignin-first approaches to biomass conversion rely on a detailed understanding of the lignin polymer obtained from spectroscopic characterizations. While genetically modified lignin with structural variations that were hypothesized to improve monomer yields did so, wild type lignin variants with similar changes were substantially less effective, suggesting that other factors of plant control must be at play. Better understanding of such a complex polymer is necessary for improving lignin fractionation processes, and this understanding can be gained through improved insight into lignin growth within the plant cell wall.<br/>We utilize well known stochastic chemical kinetic methods, specifically kinetic Monte Carlo modeling, to computationally generate lignin fragments to bridge the gap between lignin understanding. While others have used such stochastic kinetic methods previously, we combine quantum chemical calculations of oxidized lignin fragment coupling with the Gillespie algorithm to generate lignin structural information from first principles. With this approach, we have enabled the prediction of macroscopic lignin-properties that can be compared to measured lignin properties. In addition, the effects of different cell control mechanisms on final lignin structure can be understood by evaluating the effects of parameter sensitivities on the modeled lignin. These efforts enable a better understanding of the lignin polymer that can help improve the design of catalysts used for lignin processing.

Understanding lignin polymerization using kinetic Monte Carlo methods

2019 Spring National Meeting

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