Unbleached Nanofibrillated Cellulose as Additive and Coating for Kraft Paper
Bibliographic record
Abstract
Although paper packages are biodegradable, their applications in food packaging are limited due to high affinity for absorbing moisture and the high permeability of gases and liquids with surroundings. Therefore, exploring the use biodegradable coatings such as nanocellulose to improve barrier is a relevant strategy. This study assessed the efficacy of unbleached nanofibrillated cellulose (NFC) as an additive to paper and coatings. Using NFC derived from unbleached eucalyptus pulp, a 5% mass addition to the paper pulp and a 2 mm wet nanocellulose coating to kraft paper handsheets made from pine pulp were investigated. In addition, nanocellulose films of similar thicknesses were appraised. The physical, morphological, mechanical, and thermal characteristics of the resulting papers were assessed. Incorporating NFC notably enhanced the morphology area of the paper by padding its pores, thus increasing its density by up to 48% and improving its water barrier properties by up to 50%. The mechanical strength showed significant enhancements, particularly in bursting and tensile strength with increases of up to 134% and 50%, respectively. Anyhow, the films exhibited lower bursting indices and no improvement in the tearing index. Nonetheless, the thermal stability of the handsheets with NFC coating meets the minimum requirements for food packaging.
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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".