Enzymatically-Mediated Co-Production of Cellulose Nanocrystals and Fermentable Sugars
Bibliographic record
Abstract
Cellulose nanocrystals (CNCs) can be extracted from cellulosic materials through the degradation of non-crystalline cellulose domains in the feedstock via acid hydrolysis. However, the sugars released from the hydrolysis process cannot be easily recovered from the acid waste stream. In this study, cellulases were used to preferentially degrade non-crystalline domains with the objectives of recovering sugars and generating a feedstock with concentrated CNC precursors for a more efficient acid hydrolysis process. Filter paper and wood pulp substrates were enzyme-treated for 2–10 h to recover 20–40 wt % glucose. Substantial xylose yield (6–12 wt %) was generated from wood pulp. CNC yields from acid hydrolysis of cellulases-treated filter paper, and wood pulp improved by 8–18% and 58–86%, respectively, when compared with the original substrate. It was thought that CNC precursors accumulated in the cellulases-treated feedstock due to enzymatic digestion of the more accessible non-crystalline celluloses. Therefore, acid hydrolysis from enzyme-treated feedstock will require proportionally less water and reagents resulting in increased efficiency and productivity in downstream processes. This study demonstrates that an enzymatically-mediated process allows recovery of fermentable sugars and improves acid hydrolysis efficiency for CNC production.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".