Potential To Produce Sugars and Lignin-Containing Cellulose Nanofibrils from Enzymatically Hydrolyzed Chemi-Thermomechanical Pulps
Bibliographic record
Abstract
Softwood mechanical pulps have proven to be quite recalcitrant to enzymatic hydrolysis. However, the unhydrolyzed, residual fibers might have potential as nanofibrillated cellulose feedstocks. In the work reported here, a bleached softwood chemi-thermomechanical pulp (CTMP) was neutrally sulfonated (S-BCTMP) in an attempt to enhance fiber accessibility and enzymatic hydrolysis. A 12 h hydrolysis at 10% solid loading with CTec3 cellulases provided optimum conditions with 22% of the pulp hydrolyzed to monosaccharides and about one-third of the original substrate remaining as lignin-containing cellulose nanofibrils (LCNFs). Prolonged hydrolysis (72 h) resulted in 42% hydrolysis of the original substrate with only 16% of the original S-BCTMP recovered as LCNFs. Although the LCNFs contained high levels of lignin (26.8%–38.5%), they were successfully used to prepare transparent films showing a high contact angle (82.8°) and strong UV-blocking properties. It was apparent that enzyme-mediated modification of CTMP has the potential to produce both fermentable sugars and higher-value LCNFs.
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".