Nanocellulose in Thin Films, Coatings, and Plies for Packaging Applications: A Review
Bibliographic record
Abstract
This review article was prompted by a remarkable growth in the number of scientific publications dealing with the use of nanocellulose (especially nanofibrillated cellulose (NFC), cellulose nanocrystals (CNC), and bacterial cellulose (BC)) to enhance the barrier properties and other performance attributes of new generations of packaging products. Recent research has confirmed and extended what is known about oxygen barrier and water vapor transmission performance, strength properties, and the susceptibility of nanocellulose-based films and coatings to the presence of humidity or moisture. Recent research also points to various promising strategies to prepare ecologically-friendly packaging materials, taking advantage of nanocellulose-based layers, to compete in an arena that has long been dominated by synthetic plastics. Some promising approaches entail usage of multiple layers of different materials or additives such as waxes, high-aspect ratio nano-clays, and surface-active compounds in addition to the nanocellulose material. While various high-end applications may be achieved by chemical derivatization or grafting of the nanocellulose, the current trends in research suggest that high-volume implementation will likely incorporate water-based formulations, which may include water-based dispersions or emulsions, depending on the end-uses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".