Production of nanocellulose from native cellulose – Various options utilizing ultrasound
Bibliographic record
Abstract
In this study three different ways of applying ultrasound for the production of nanocellulose from native cellulose were explored. In the first option bleached hardwood kraft pulp was oxidized with the ultrasound (US) assisted TEMPO/NaBr/NaOCl-system (US-TEMPO-system) followed by mechanical separation of nanocellulose. The pulp oxidized by the US-TEMPO-system had higher carboxyls content and ca. 10% increase in nanocellulose yield when compared to the TEMPO-system without sono catalysis. In the second option ultrasound pretreated pulp was oxidized using the TEMPO-system. Although there was no gain in carboxyls content in this process versus the oxidation with TEMPO-system without ultrasound treatment, a higher degree of fibrillation was obtained after ultrasound treatment. In the third case the TEMPO oxidized pulp was subjected to mechanical and ultrasound treatments for nanocellulose production. Under similar treatment time the subsequent ultrasound treatment achieved higher nanocellulose yield than the subsequent mechanical treatment. However, in comparison, the ultrasound treated nanocellulose had lower Rheometer Stresstech viscosity. Furthermore, it was observed that cellulose nanofibrils produced by ultrasound treatment were slightly thinner compared to those produced using the mechanical method.
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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) | 0.000 | 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, 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".