Computational Generation of Lignin Libraries from Diverse Biomass Sources

Dellon, Lauren D.; Yanez, Abraham J.; Li, Wenjun; Mabon, Ross; Broadbelt, Linda J.

doi:10.1021/acs.energyfuels.7b01150

Cited by 44 publications

(86 citation statements)

References 45 publications

(124 reference statements)

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“…It has been reported in previous structure simulation work that these bond types make up the great majority of those occurring in lignins. [75] The relative composition used for HP lignin is as follows: b-O-4 = 72 %, b-1 = 6%, b-b = 15 %, and b-5 = 7%.D etermination of the S/G ratio from HSQC NMR spectroscopy also showedg ood agreement with Py-GC/MS results. The S/G ratio for HP lignin was estimatedt ob e0 .89 and0 .86 from Py-GC/MSa nd NMR, respectively.…”

Section: Resultsmentioning

confidence: 64%

“…This underscores the necessity of stochastics imulation to effectively sample among potential arrangements whiles till maintaining observed naturald iversity in lignin.T his type of simulation can produce as maller library of structures that probabilistically conformst om easured properties of ag iven sample. [75][76][77] Stochastically-generated lignin library As tochastic lignin simulation was carriedo ut to generate randomly 100 lignin structures. The inputs to this stochastic simulation require monomer distribution, bond-type distribution, and molecular weight.…”

Section: Hypothetical B-o-4 Lignin Oligomersmentioning

confidence: 99%

“…The inputs to this stochastic simulation require monomer distribution, bond-type distribution, and molecular weight. [75,77] In this case, polymeric branching was not considered. Monomer distribution wasd erived from py-GC/MSr esultsa nd molecular weightf rom the molecular weightd istribution of the FT-ICR MS data.…”

Section: Hypothetical B-o-4 Lignin Oligomersmentioning

confidence: 99%

“…This aposteriori understanding of lignin structure also allows for the computational creation of libraries of molecules. [75] Thisd raws an atural coupling between stochastic simulation and high-resolution MS foru nderstanding lignin structure. As shown in Table 9, calculable parameters of MS-observed lignin and simulated lignin correspond well.…”

Section: Comparison Of Ft-icr Ms Data With Stochastic Simulationmentioning

confidence: 99%

“…Therefore, the goal of this work is to exploref urther applicationso fF T-ICR MS for the characterization of lignin using matrix-assisted laser desorption/ionization( MALDI). We present the use of stochastic structure generation [75][76][77] as at ool to aid in the assignment of potential candidate structures to lignin-derived oligomers with given chemical formulaed etected in MALDI-FT-ICR MS.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Contributions to Lignomics: Stochastic Generation of Oligomeric Lignin Structures for Interpretation of MALDI–FT‐ICR‐MS Results

et al. 2020

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The lack of standards to identify oligomeric molecules is a challenge for the analysis of complex organic mixtures. High‐resolution mass spectrometry—specifically, Fourier‐transform ion cyclotron resonance mass spectrometry (FT‐ICR MS)—offers new opportunities for analysis of oligomers with the assignment of formulae (CxHyOz) to detected peaks. However, matching a specific structure to a given formula remains a challenge due to the inability of FT‐ICR MS to distinguish between isomers. Additional separation techniques and other analyses (e.g., NMR spectroscopy) coupled with comparison of results to those from pure compounds is one route for assignment of MS peaks. Unfortunately, this strategy may be impractical for complete analysis of complex, heterogeneous samples. In this study we use computational stochastic generation of lignin oligomers to generate a molecular library for supporting the assignment of potential candidate structures to compounds detected during FT‐ICR MS analysis. This approach may also be feasible for other macromolecules beyond lignin.

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Section: Resultsmentioning

confidence: 64%

Section: Hypothetical B-o-4 Lignin Oligomersmentioning

confidence: 99%

Section: Hypothetical B-o-4 Lignin Oligomersmentioning

confidence: 99%

Section: Comparison Of Ft-icr Ms Data With Stochastic Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Contributions to Lignomics: Stochastic Generation of Oligomeric Lignin Structures for Interpretation of MALDI–FT‐ICR‐MS Results

et al. 2020

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From Lignin to Chemicals: An Expedition from Classical to Modern Catalytic Valorization Technologies

Al‐Naji

Brandi

Drieß

et al. 2022

Chemie Ingenieur Technik

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For the transition towards greener biorefineries with reduced waste, valorization of lignin to drop-in chemicals instead of their combustion for energy purposes is a key issue for future processes. In this context, lignin should be extracted from lignocellulosic biomass (LCB) and fragmented into smaller units, followed by catalytic funneling to fine chemicals. In this review, we report on the classical approaches for lignin valorization from LCB: thermal treatments, solvolytic valorization, acid-catalyzed process, and base-catalyzed process. We also provide the reader with the modern approach of lignin valorization that led to an integrated LCB biorefinery. The performance of different solid catalysts in lignin-first approach via reductive or oxidative catalytic fractionation at different conditions is discussed in detail.

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Mesoscale Reaction–Diffusion Phenomena Governing Lignin‐First Biomass Fractionation

et al. 2020

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Lignin solvolysis from the plant cell wall is the critical first step in lignin depolymerization processes involving whole biomass feedstocks. However, little is known about the coupled reaction kinetics and transport phenomena that govern the effective rates of lignin extraction. Here, we report a validated simulation framework that determines intrinsic, transport‐independent kinetic parameters for the solvolysis of lignin, hemicellulose, and cellulose upon incorporation of feedstock characteristics for the methanol‐based extraction of poplar as an example fractionation process. Lignin fragment diffusion is predicted to compete on the same time and length scales as reactions of lignin within cell walls and longitudinal pores of typical milled particle sizes, and mass transfer resistances are predicted to dominate the solvolysis of poplar particles that exceed approximately 2 mm in length. Beyond the approximately 2 mm threshold, effectiveness factors are predicted to be below 0.25, which implies that pore diffusion resistances may attenuate observable kinetic rate measurements by at least 75 % in such cases. Thus, researchers are recommended to conduct kinetic evaluations of lignin‐first catalysts using biomass particles smaller than approximately 0.2 mm in length to avoid feedstock‐specific mass transfer limitations in lignin conversion studies. Overall, this work highlights opportunities to improve lignin solvolysis by genetic engineering and provides actionable kinetic information to guide the design and scale‐up of emerging biorefinery strategies.

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Computational Generation of Lignin Libraries from Diverse Biomass Sources

Cited by 44 publications

References 45 publications

Contributions to Lignomics: Stochastic Generation of Oligomeric Lignin Structures for Interpretation of MALDI–FT‐ICR‐MS Results

Contributions to Lignomics: Stochastic Generation of Oligomeric Lignin Structures for Interpretation of MALDI–FT‐ICR‐MS Results

From Lignin to Chemicals: An Expedition from Classical to Modern Catalytic Valorization Technologies

Mesoscale Reaction–Diffusion Phenomena Governing Lignin‐First Biomass Fractionation

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