Computational Generation of Lignin Libraries from Diverse Biomass Sources

The economic viability of the biofuel industry has been plagued in part by the incomplete valorization of lignin, which is currently being burned for process heat. One of the roadblocks to effectively converting lignin into usable fuels and chemicals is that the structure of lignin has yet to be entirely understood owing to its polydispersity, complexity, and hyper-branched topology. Libraries of structural representations of lignin accounting for these facets have recently been proposed for wheatstraw, an herbaceous biomass, based on a stochastic generation method that creates lignin molecules that collectively conform to properties measured experimentally. We have extended this stochastic method to accommodate more complexity and any type of biomass, i.e., softwood, hardwood, or herbaceous. The unique mechanistic details for several of the new lignin bond types are essential in deciding rules for bond formation in the algorithm. Further, we present two successful methods of decreasing the degrees of freedom during optimization of crucial parameters. Apart from generating libraries of lignin structures, the added complexity allows for the exploration of “lignin space”, which we coin here to represent all possible structures of lignin given the experimental characteristics of monomer distribution, bond distribution, molecular weight distribution, and branching coefficient. Using our overall approach, lignin libraries for any biomass source with reliable and consistent experimental data can be generated for future kinetic modeling studies or molecular simulations, and guidance can be provided to experimentalists to design and characterize lignin.