TEMPO Mediated Oxidation Optimization on Thermomechanical Pulp for Paper Reinforcement and Nanomaterial Film Production
Bibliographic record
Abstract
The 4-acetamido-TEMPO mediated oxidation system is well known on pure cellulose, but further investigation on more complex cellulose sources, such as thermomechanical pulp that is also composed of hemicelluloses and lignin, is needed. The reaction on wood pulp allows improvement in paper strength and nanofibril material production. However, the effects of the reaction parameters are not known enough to scale up the oxidation. With the help of two experimental designs, the chemical amounts, reaction temperature and time, sodium hypochlorite injection time, and nanofibril dispersion time were all studied. During the experiments, it was possible to observe pulp bleaching or delignification. Increasing the 4-acetamido-TEMPO amount promoted the pulp oxidation. In contrast, a large excess of sodium bromide and sodium hypochlorite was advantageous for pulp bleaching and delignification. High temperature favored the oxidation but delignification was induced by the oxidation. For the paper reinforcement, chemical amounts were optimized according to the end user needs. For nanomaterial production, both oxidation and delignification were needed. The reaction had to generate a significant delignification and increase pulp carboxyl content higher than 1600 mmol/kg to be able to produce nanofibril material. The results showed new leads on the various times required for future industrial implementation.
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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) | 0.001 | 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".