Enhancing Enzyme-Mediated Hydrolysis of Mechanical Pulps by Deacetylation and Delignification
Bibliographic record
Abstract
Alkaline induced deacetylation of the hemicellulose combined with subsequent mechanical refining enhanced the enzyme-mediated hydrolysis of pretreated corn stover. The addition of either NaOH (80 °C) or mild KOH (25 °C) to corn stover prior to mechanical refining led to greater than 80% deacetylation with the NaOH treatment also solubilizing low-molecular-weight lignin that was enriched in β-O-4 linkages with more than 25% and 13% of the total and surface lignin removed, respectively. The influence of deacetylation and delignification were further enhanced when NaOH was supplemented with 3% Na 2 SO 3, resulting in 100% deacetylation, 34% delignification, and a >20% increase in the hydrolysis yield of the substrate xylan. A milder KOH treatment resulted in the retention of more than 95% of the lignin within the cellulose rich, water-insoluble fraction with no apparent change in the surface lignin. However, both methods resulted in enhanced xylan hydrolysis when treated with xylanases, suggesting that deacetylation had enhanced accessibility to the xylan present in the pretreated of corn stover. It was apparent that cellulose accessibility was also enhanced by partial delignification, as NaOH treatment resulted in a 65% and 43% increase in the Water Retention Value and Directed Orange dye adsorption, respectively.
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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) | 0.001 | 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 | 0.001 |
| 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".