(722c) Advancing Lignin Research: Importance of Kinetic Study through an Integrated DFT-Aimd-Kmc Methods

Lignin, the second most abundant biomaterial on Earth, offers significant potential for sustainable biofuel and chemicals due to its abundant availability and high energy density.^[1-2] However, its complex structure, characterized by resistance to degradation, presents considerable challenges for valorization. This complexity necessitates a deep understanding of de/repolymerization behavior of lignin to facilitate sustainable valorization methods, potentially reducing reliance on fossil fuels. Traditionally, to investigate the reaction mechanism of lignin process, density functional theory (DFT) method has been utilized.^[3] While this affords us detailed insights into the structural transformations, thermodynamics, and reaction pathways at the atomic level, they fall short in capturing the dynamic kinetic aspects, such as predicting monomer yields and chain length distributions, due to its inability to account for the temporal evolution of complex systems.

To address this challenge, we employed a combination of DFT, ab-initio molecular dynamics (AIMD) and kinetic Monte Carlo (kMC) simulations, constituting a comprehensive framework for assessing various lignin properties. Specifically, DFT method was used to delve into the mechanistic insights of the de/repolymerization processes by examining thermodynamic aspects such as the structural conformation of lignin and the activation energy barriers. Further, an important factor considered was the impact of temperature on lignin structure, particularly how temperature altering binding energies and activation energy barriers. To capture the nuanced effects of temperature on lignin structure, AIMD simulations were conducted, informing the calculation of activation energy barriers under varying temperatures via DFT.^[3]

Following this, kMC simulations were utilized to investigate lignin properties across different scales^[4], operating on data derived from DFT-AIMD findings. These simulations allowed for real-time tracking into account the status and configuration of system. This study embraced a multiscale simulation framework to address the processes of delignification and de/repolymerization, classifying reactions into macroscopic and microscopic levels for simultaneous execution. Through this simulation, we unveiled insights into the residual lignin content in biomass, lignin molecular weight distribution, and the S/G ratio. We believe that our comprehensive, multifaceted approach provides valuable insights into the thermodynamic and kinetic properties of lignin systems.

References

[1] C. C. Azubuike, M. N. Allemann and J. K. Michener, Microbial assimilation of lignin-derived aromatic compounds and conversion to value-added products, Current Opinion in Microbiology, 65 (2022), 64-72, 10.1016/j.mib.2021.10.014.

[2] Z. W. Cao, M. Dierks, M. T. Clough, I. B. D. de Castro and R. Rinaldi, A Convergent Approach for a Deep Converting Lignin-First Biorefinery Rendering High-Energy-Density Drop-in Fuels, Joule, 2 (2018), 6, 1118-1133, 10.1016/j.joule.2018.03.012.

[3] L. Petridis, R. Schulz and J. C. Smith, Simulation Analysis of the Temperature Dependence of Lignin Structure and Dynamics, Journal of the American Chemical Society, 133 (2011), 50, 10.1021/ja206839u.

[4] 28. A. J. Yanez, P. Natarajan, W. J. Li, R. Mabon and L. J. Broadbelt, Coupled Structural and Kinetic Model of Lignin Fast Pyrolysis, Energy & Fuels, 32 (2018), 2, 1822-1830, 10.1021/acs.energyfuels.7b03311.