Comparison Of Mechanical And Chemical Nanocellulose As Additives To Reinforce Recycled Cardboard
Bibliographic record
Abstract
Recycling cycles cause a decrease in mechanical paper properties due to cellulose fiber degradation. The use of cellulose micro/nanofibers (CMF/CNF) to reinforce paper strength has been well studied, although it has been found to have negative effects on drainage. However, the application of CMF/CNF as paper reinforcement is affected by the nanocellulose type. Thus in this study mechanical and chemical treatments in CNF production were compared. Old corrugated container (OCC) pulp used to produce recycled cartonboard was reinforced with 1) CMF from never-dried northern bleached softwood kraft pulp (NBSK) highly refined in a 16-inch low consistency refiner at 1200 rpm and 25 kW of net power; and 2) CNF from NBSK pulp treated by TEMPO-mediated oxidation and homogenization at 600 bars. CMF/CNF and OCC were pulped at the same time and handsheets formed with cationic starch (CS) as retention system. Mechanical, drainage and flocculation properties were evaluated and compared. Data were also compared with other sources of TEMPO CNF. Results show an improvement in mechanical properties, drainage and flocculation when OCC is reinforced with CMF obtained with LCR. Therefore, high fibrillation was not necessary to improve mechanical paper or cardboard properties.
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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) | 0.001 | 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".