LigninGraphs: lignin structure determination with multiscale graph modeling.

Area of Science:

• Biomass Valorization and Renewable Energy
• Polymer Science and Engineering
• Computational Chemistry and Materials Science

Background:

• Lignin, an abundant aromatic biopolymer in biomass, presents complex structures.
• Understanding lignin's intricate molecular architecture is crucial for developing efficient biomass valorization processes.
• Existing experimental techniques like 2D-heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) provide global insights but lack atomistic detail.

Purpose of the Study:

• To introduce a novel graph-based multiscale modeling framework for generating and visualizing diverse lignin structures.
• To bridge the gap between experimental characterization and atomistic-level understanding of lignin.
• To provide a computational tool that aids in the design and optimization of lignin valorization strategies.

Main Methods:

• Development of a multiscale modeling framework utilizing graph-based representations.
• Implementation of accelerated rejection-free polymerization and hierarchical Metropolis Monte Carlo optimization algorithms.
• Generation of lignin structure libraries using existing literature data and new experimental NMR data from poplar wood, pinewood, and herbaceous lignin.

Main Results:

• Successful generation of diverse lignin structure libraries for various feedstocks.
• Demonstration of the framework's capability to represent lignin structures at molecular and atomistic scales.
• Creation of an open-source Python software implementation, LigninGraphs, available on GitHub.

Conclusions:

• The developed framework and software (LigninGraphs) offer a powerful tool for researchers studying lignin.
• This approach can guide the exploration of feasible lignin structures and facilitate future kinetics modeling.
• The multiscale modeling framework enhances the understanding of lignin, paving the way for improved biomass conversion technologies.