Abstract
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•Combined DFT-AIMD detailed temperature-induced Ea for lignin de/repolymerization.•Layered-kMC served as a modeling framework for the overall fractionation process.•We effectively tracked lignin content, molecular weight distribution, and S/G ratio.•The integrated model showcased its excellent real-time lignin monitoring performance.
Lignin, with its abundant and high energy density, offers potential for sustainable biofuels and chemicals. However, current research faces two primary challenges: limited experimental sampling frequency, impeding the real-time analysis and a lack of theoretical activation energies to elucidate thermodynamic and kinetic properties associated with structural changes under various processing conditions including reaction temperature (343 to 363 K), time (15 to 30 min), and chip size (1.0 to 5.0 mm). To address these challenges, we introduce the “real-time molecular-level sensing approach,” which considers delignification, redeposition, and de/repolymerization over time and various scales, leveraging sparse experimental data and theoretical activation energies. Our approach combines density functional theory, ab-initio molecular dynamics, and kinetic Monte Carlo simulations to deeply explore dynamic nature of lignin. Specifically, we gain insights into lignin content within biomass, molecular weight distribution, and monolignol ratio, improving lignin’s utilization during processing. This work establishes a new standard for comprehensive lignin analysis, fostering efficient lignin utilization by tracking their properties at the molecular level in the real-time manner.