Enzymatic digestibility of lignocellulosic wood biomass: Effect of enzyme treatment in supercritical carbon dioxide and biomass pretreatment
Bibliographic record
Abstract
Energy and resource intensive mechanical and chemical pretreatment along with the use of hazardous chemicals are major bottlenecks in widespread lignocellulosic biomass utilization. Herein, the study investigated different pretreatment methods on spruce wood namely supercritical CO 2 (scCO 2 ) pretreatment, ultrasound-assisted alkaline pretreatment, and acetosolv pulping-alkaline hydrogen peroxide bleaching, to enhance the enzymatic digestibility of wood using optimized enzyme cocktail. Also, the effect of scCO 2 pretreatment on enzyme cocktail was investigated after optimizing the concentration and temperature of cellulolytic enzymes. The impact of scCO 2 and ultrasound-assisted alkaline pretreatments of wood were insignificant for the enzymatic digestibility, and acetosolv pulping-alkaline hydrogen peroxide bleaching was the most effective pretreatment that showed the release of total reducing sugar yield (TRS) of ∼95.0 wt% of total hydrolyzable sugars (THS) in enzymatic hydrolysis. The optimized enzyme cocktail showed higher yield than individual enzymes with degree of synergism 1.34 among the enzymes, and scCO 2 pretreatment of cocktail for 0.5–1.0 h at 10.0–22.0 MPa and 38.0–54.0 °C had insignificant effect on the enzyme's primary and global secondary structure of cocktail and its activity.
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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 | 0.000 |
| 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".