Co-Production of Cellulose Nanocrystals and Fermentable Sugars Assisted by Endoglucanase Treatment of Wood Pulp
Bibliographic record
Abstract
In this study, fermentable sugars and cellulose nanocrystals (CNCs) were co-produced from endoglucanase treatment of wood pulp, followed by acid hydrolysis. Enzymatic hydrolysis was performed using two endoglucanases differentiated by the presence or absence of a cellulose-binding domain (CBD). The enzyme with an intact CBD gave the higher glucan conversion (up to 14.1 ± 1.2 wt %) and improved the degree of crystallinity of the recovered wood pulp fiber (up to 83.0 ± 1.0%). Thus, this endoglucanase-assisted treatment successfully removed amorphous content from the original cellulosic feedstock. CNC recovery (16.9 ± 0.7 wt %) from the feedstock going into the acid hydrolysis was improved relative to untreated pulp (13.2 ± 0.6 wt %). The mass loss from enzymatic treatment did not cause a decrease in the CNC yield from the starting material. The characteristics of CNCs obtained through acid hydrolysis (with or without enzyme treatment of pulp) were analyzed using X-ray diffraction, transmission electron microscopy, dynamic light scattering, Fourier transform infrared spectroscopy, and differential scanning calorimetry as characterization techniques. The CNCs generated through acid hydrolysis of endoglucanase-treated wood pulp displayed comparable properties relative to those generated using untreated pulp. Thus, endoglucanase treatment can enable co-production of CNCs and sugars for biofuel fermentation.
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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) | 0.000 | 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".