Lignin is an abundant aromatic heteropolymer found in secondary plant cell walls and is a potential feedstock for conversion into bioderived fuels and chemicals. Lignin chemical diversity complicates traditional structural studies, and so, relatively little experimental evidence exists for how lignin structure exists in aqueous solution or how lignin polymers respond to changes in their chemical environment. Molecular modeling can address these concerns; however, prior computational structural lignin models typically did not capture lignin heterogeneity, as only a few polymers were considered. LigninBuilder creates a framework for building structural libraries for lignin from existing topological libraries, permitting significantly greater diversity of lignin structures to be sampled at atomic detail. As a demonstration of its capabilities, LigninBuilder was applied to three libraries of lignin from hardwood, softwood, and grass, and the resulting polymer structures were simulated in an aqueous environment. The lignins adopted compact globular structures, as would be expected for polymers in poor solvents. The differences between the libraries were largest when quantifying the packing of nonadjacent aromatic residues, with greater branching within the polymer resulting in poorer aromatic packing. Individual lignin polymers were also found to undergo rapid conformational changes, with the dwell time within a state growing as the square of the molecular weight. This first application of LigninBuilder demonstrates the potential for atomic-level insight into lignin interactions. LigninBuilder is distributed as a plugin to the visualization software VMD, lowering the barrier for modeling lignin structure in diverse environments.

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.

Lignin liquefaction microkinetics is a move toward a more first-principles (i.e., ab initio)-based understanding at the molecular level in reaction engineering. While the microkinetic modeling of reactions to obtain kinetic rate parameters of chemical reactions have been widely used in the field of gas phase combustion and heterogeneous catalysis, this approach has not been as thoroughly developed in the area of biomass thermochemical reactions (e.g., lignin pyrolysis, hydrothermal liquefaction). The difficulties in establishing the structure of complex heterogeneous materials, like lignin, is perhaps the main challenge in developing rational microkinetic descriptions of biomass thermochemical reactions. In this manuscript, we review the current state of the art and the challenges to develop microkinetic models for lignin liquefaction technologies (e.g., pyrolysis, hydrothermal liquefaction, solvolysis). A general strategy for the development of microkinetic models for lignin liquefaction technologies is discussed. The first hurdle is to obtain sufficiently rich experimental data of lignin underlying polymeric structure and methodologies to use this data to build realistic lignin structural representations. Some analytical techniques for lignin structural characterization and their associated data, as well as a correlation for calculating the degree of macromolecular lignin branching, are discussed. The presence of small lignin oligomeric structures and the role of these structures in lignin pyrolysis is also addressed. The ways in which elementary deconstruction and repolymerization reactions occur within this structure to form a liquid intermediate and how these deconstruction products continue to interact with each other until they are removed from the liquid intermediate is thoroughly discussed. Further, experimental work with model compounds and the effect of reaction parameters (e.g., temperature, pressure, vapor residence time) are reviewed. Another major challenge to develop microkinetic models of lignin liquefaction is to describe product removal mechanisms (e.g., evaporation, solubilization, thermal ejection) from the liquid intermediate. Group contribution methods are presented for estimation of thermophysical parameters, like normal boiling point and heat of vaporization for model structures. Once the products have been removed from the liquid intermediate, they continue reacting in the aerosol droplets, in vapor phase, or in the solvent depending on the liquefaction technology studied. These “secondary reactions” need to be included in realistic microkinetic models. Based on this review, we can state that with careful implementation, high-quality microkinetic models can be developed to simulate thermochemical lignin liquefaction.

Bio-oil produced from fast pyrolysis of biomass is a complex mixture of more than 200 compounds, including oxygenates and acids. As these species are highly undesirable in fuels, catalytic upgrading of biomass pyrolysis product vapors, also known as catalytic fast pyrolysis, is performed to upgrade the vapors to valuable fuels and chemicals. This work presents a detailed microkinetic model, composed of elementary steps, of the catalytic upgrading of acetic acid and acetone, two common oxygenates present in bio-oil. An automated network generator was utilized to construct a reaction network composed of 580 unique species and 2160 unique reactions. The kinetic parameters for each reaction in the network were estimated using transition state theory, the Evans–Polanyi relationship, and thermodynamic data. The resulting mechanistic model is able to describe experimental data presented in the literature for the transformation of acetic acid and acetone on HZSM-5 in a fixed-bed reactor, which is modeled as a plug-flow reactor. Additionally, the model solutions reveal vital information regarding the mechanism by which acetic acid and acetone are upgraded to valuable fuels and chemicals. In the first phase of the mechanism, acetic acid is converted to acetone via acylium ion addition to acetic acid; this is followed by decarboxylation of acetoacetic acid. The second phase is dominated by the self-aldol condensation of acetone, which is shown to occur predominantly through the keto form of acetone rather than the enol form, and subsequent deoxygenation reactions leading to olefins and aromatics. Finally, net rate analysis shows that aromatics are primarily formed via a pathway including aldol condensation of mesityl oxide, whereas olefins are produced from the addition of isobutene and subsequent cracking.

Bio-oil produced from biomass fast pyrolysis often requires catalytic upgrading to remove oxygen and acidic species over zeolite catalysts. The elementary reactions in the mechanism for this process involve carbenium and oxonium ions. In order to develop a detailed kinetic model for the catalytic upgrading of biomass, rate constants are required for these elementary reactions. The parameters in the Arrhenius equation can be related to thermodynamic properties through structure–reactivity relationships, such as the Evans–Polanyi relationship. For this relationship, enthalpies of formation of each species are required, which can be reasonably estimated using group additivity. However, the literature previously lacked group additivity values for oxygenates, oxonium ions, and oxygen-containing carbenium ions. In this work, 71 group additivity values for these types of groups were regressed, 65 of which had not been reported previously and six of which were newly estimated based on regression in the context of the 65 new groups. Heats of formation based on atomization enthalpy calculations for a set of reference molecules and isodesmic reactions for a small set of larger species for which experimental data was available were used to demonstrate the accuracy of the Gaussian-4 quantum mechanical method in estimating enthalpies of formation for species involving the moieties of interest. Isodesmic reactions for a total of 195 species were constructed from the reference molecules to calculate enthalpies of formation that were used to regress the group additivity values. The results showed an average deviation of 1.95 kcal/mol between the values calculated from Gaussian-4 and isodesmic reactions versus those calculated from the group additivity values that were newly regressed. Importantly, the new groups enhance the database for group additivity values, especially those involving oxonium ions.