The fate of lignin during hydrothermal pretreatment
Bibliographic record
Abstract
BACKGROUND: Effective enzymatic hydrolysis of lignocellulosic biomass benefits from lignin removal, relocation, and/or modification during hydrothermal pretreatment. Phase transition, depolymerization/repolymerization, and solubility effects may all influence these lignin changes. To better understand how lignin is altered, Populus trichocarpa x P. deltoides wood samples and cellulolytic enzyme lignin (CEL) isolated from P. trichocarpa x P. deltoides were subjected to batch and flowthrough pretreatments. The residual solids and liquid hydrolysate were characterized by gel permeation chromatography, heteronuclear single quantum coherence NMR, compositional analysis, and gas chromatography-mass spectrometry. RESULTS: Changes in the structure of the solids recovered after the pretreatment of CEL and the production of aromatic monomers point strongly to depolymerization and condensation being primary mechanisms for lignin extraction and redeposition. The differences in lignin removal and phenolic compound production from native P. trichocarpa x P. deltoides and CEL suggested that lignin-carbohydrate interactions increased lignin extraction and the extractability of syringyl groups relative to guaiacyl groups. CONCLUSIONS: These insights into delignification during hydrothermal pretreatment point to desirable pretreatment strategies and plant modifications. Because depolymerization followed by repolymerization appears to be the dominant mode of lignin modification, limiting the residence time of depolymerized lignin moieties in the bulk liquid phase should reduce lignin content in pretreated biomass. In addition, the increase in lignin removal in the presence of polysaccharides suggests that increasing lignin-carbohydrate cross-links in biomass would increase delignification during pretreatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".