Mild Acid-Catalyzed Atmospheric Glycerol Organosolv Pretreatment Effectively Improves Enzymatic Hydrolyzability of Lignocellulosic Biomass
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Conventional atmospheric glycerol organosolv pretreatment is energy-intensive with the requirement of long time and/or high temperature. Herein, acid-catalyzed atmospheric glycerol organosolv (ac-AGO) pretreatment was developed under a mild condition to modify the sugarcane bagasse structure for improving enzymatic hydrolyzability. Using single factor and central composite design experiments, ac-AGO pretreatment was optimized at 200 °C for 15 min with 0.06% H 2 SO 4 addition, wherein the hemicellulose and lignin removal rates were 82 and 52%, respectively, with extremely high cellulose retention of 98%. The ac-AGO-pretreated substrate exhibited good enzymatic hydrolyzability at a modest cellulase loading, affording a 70% glucose yield after 72 h. Multiple analysis tools were used to correlate the hydrolyzability of the substrate with its structural features. The results indicated that the mild ac-AGO pretreatment can modify the lignocellulosic biomass structure to achieve good hydrolyzability, mainly resulting in significant hemicellulose removal.
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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".