Characterization of Cellulase-Treated Fibers and Resulting Cellulose Nanocrystals Generated through Acid Hydrolysis
Bibliographic record
Abstract
Integrating enzymatic treatment and acid hydrolysis potentially improves the economics of cellulose nanocrystal (CNC) production and demonstrates a sustainable cellulosic ethanol co-generation strategy. In this study, the effect of enzymatic treatment on filter paper and wood pulp fibers, and CNCs generated via subsequent acid hydrolysis were assessed. Characterization was performed using a pulp quality monitoring system, scanning and transmission electron microscopies, dynamic light scattering, X-ray diffraction, and thermogravimetric analysis. Enzymatic treatment partially reduced fiber length, but caused swelling, indicating simultaneous fragmentation and layer erosion. Preferential hydrolysis of less ordered cellulose by cellulases slightly improved the crystallinity index of filter paper fiber from 86% to 88%, though no change was observed for wood pulp fibre. All CNC colloids were stable with zeta potential values below -39 mV and hydrodynamic diameters ranging from 205 to 294 nm. Furthermore, the temperature for the peak rate of CNC thermal degradation was generally not affected by enzymatic treatment. These findings demonstrate that CNCs of comparable quality can be produced from an enzymatically-mediated acid hydrolysis biorefining strategy that co-generates fermentable sugars for biofuel production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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, 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".