(253c) Predicting and Improving Lignin Depolymerization Processes with Whole Plant Cell Wall HSQC0 NMR and Kinetic Monte Carlo Simulation | AIChE

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(253c) Predicting and Improving Lignin Depolymerization Processes with Whole Plant Cell Wall HSQC0 NMR and Kinetic Monte Carlo Simulation

Authors 

Bourmaud, C. - Presenter, Ecole Polytechnique Federale De Lausanne
Li, Q., University of Delaware
Wang, Y., University of Delaware
Sabanza Gil, V., EPFL
Sun, S.
Bertella, S., Ecole Polytechnique Fédérale De Lausanne (EPFL)
Facas, G. G., University of Minnesota Twin Cities
Beckham, G., National Renewable Energy Laboratory
Schwaller, P., EPFL
Vlachos, D., University of Delaware - Catalysis Center For Ener
Luterbacher, J., Ecole Polytechnique Federale De Lausanne
Lignin depolymerization via hydrogenolysis is a promising pathway to produce renewable phenolic monomers.1 However, our understanding of this process is limited by lignin heterogeneous structure, following complex deconstruction mechanism and yielding broad product distribution after β-O-4 ether linkages.2 We developed a rapid extrapolated time zero 1H–13C heteronuclear single quantum coherence nuclear magnetic resonance (HSQC0 NMR) sequence that enables to quantify native lignin structural features with whole plant cell wall (WPCW) NMR spectroscopy, overcoming fast spin relaxation in gel phase.3,4 This analytical method is a promising tool to gain new insights towards native lignin structure.

Kinetic and mechanistic studies of β-O-4 model compounds depolymerization were then performed and confirmed higher reactivity of lignin end-units when adsorbed on a Ru/C catalyst. The obtained reaction network and NMR structural insights were applied to multiscale LigninGraph libraries via a kinetic Monte Carlo framework to model lignin hydrogenolysis.5,6 Reductive Catalytic Fractionation was successfully modelled and first-ever predicted by our algorithm using dimer kinetic data. This model can notably establish structure-reactivity relationships, that expands our knowledge of lignin structure and understanding of extraction processes. Reaching unexpected insights for lignin engineering, we could ultimately guide the design of highly reactive lignin moieties towards hydrogenolysis.

References

1. Mahdi M. Abu-Omar, Katalin Barta, Gregg T. Beckham, Jeremy S. Luterbacher, John Ralph, Roberto Rinaldi, Yuriy Román-Leshkov, Joseph S. M. Samec, Bert F. Sels, and Feng Wang. “Guidelines for Performing Lignin-First Biorefining.” Energy & Environmental Science, 2021, 14, 1, 262–92.

2. John Ralph, Catherine Lapierre, and Wout Boerjan. “Lignin Structure and Its Engineering.” Current Opinion in Biotechnology, 2019, 56, 240–49.

3. Masoud Talebi Amiri, Stefania Bertella, Ydna M. Questell-Santiago, and Jeremy S. Luterbacher. “Establishing Lignin Structure-Upgradeability Relationships Using Quantitative 1 H– 13 C Heteronuclear Single Quantum Coherence Nuclear Magnetic Resonance (HSQC-NMR) Spectroscopy.” Chemical Science, 2019, 10, 35, 8135–42.

4. Shawn D. Mansfield, Hoon Kim, Fachuang Lu, and John Ralph. “Whole Plant Cell Wall Characterization Using Solution-State 2D NMR.” Nature Protocols 2012, 7, 9, 1579–89.

5. Yifan Wang, Jake Kalscheur, Elvis Ebikade, Qiang Li, and Dionisios G. Vlachos. “LigninGraphs: Lignin Structure Determination with Multiscale Graph Modeling.” Journal of Cheminformatics 2022, 14, 1, 43.

6. R. Vinu, Seth E. Levine, Lin Wang, and Linda J. Broadbelt. “Detailed Mechanistic Modeling of Poly(Styrene Peroxide) Pyrolysis Using Kinetic Monte Carlo Simulation.” Chemical Engineering Science, 2012, 69, 1, 456–71.

Topics 

Forest & Plant Bioproducts
Sustainable Engineering
Catalysis

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