Optimized delignification of wood‐derived lignocellulosics for improved enzymatic hydrolysis
Bibliographic record
Abstract
One of the major bottlenecks in the bioconversion of lignocelluosic feedstocks to liquid ethanol is the recalcitrance of residue following pretreatment, specifically softwood derived residues. Peroxide delignification has previously been shown to effectively aid in the removal of condensed lignaceous moieties from substrates following pretreatment, and thereby improve the hydrolyzability of the polymeric carbohydrates to their monomeric constituents. Despite the effectiveness of peroxide, drawbacks in this system still remain, as the concentration of peroxide required for adequate hydrolysis performance is currently uneconomical. In an attempt to improve the efficacy of the delignification process, we evaluated other post-treatment operations and concurrently attempted to limit the decomposition of peroxide loading; with the over arching aim to improve the efficiency of the bioconversion process. By employing several peroxide stabilizers and pre-chelating the steam exploded recalcitrant substrates, we were able to substantially improve the delignification treatment of Douglas-fir wood chips, and to reduce peroxide loading by more than 50% without negative effects on the hydrolysis rates and yield.
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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".