Nanocellulose production by ultrasound-assisted TEMPO oxidation of Kraft pulp on laboratory and pilot scales
Bibliographic record
Abstract
Production of cellulose nanofibres from native cellulose has been the subject of intensive investigation during the past decade. In the pulp and paper industry, it is generally believed that this new product will open new market and increase profitability. Cellulose nanofibres can be successfully produced using a TEMPO-Sodium bromide-Sodium hypochlorite system followed by mechanical treatment. This system can be further optimized with the use of low frequency ultrasound. However, these laboratory trials are not suitable for mass production. For this reason, trials using a full scale flow-through sonoreactor which is compatible with such an oxidation system were carried out with limited sets of experiments. The objective of this study was to compare the laboratory oxidation results at various chemicals dosages to those obtained from the full scale flowthrough sonoreactor under an optimal ultrasound condition in order to further optimize the reaction conditions. The results clearly indicated that the ultrasonic efficiency of the sonoreactor was greater than that of the laboratory ultrasonic bath in terms of carboxylate content. This benefit was rather unclear basing on the rheological curves. However, the viscosity measurements suggested that it is possible to conduct the oxidation with reduced TEMPO and NaBr (3/5) using a sonoreactor and obtain similar end product. With the chemicals dosage and ultrasonic conditions now optimized in the sonoreactor, we can now produce up to 1 kg of nanocellulose per day.
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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) | 0.001 | 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".