Nanocellulose/natural latex composite film with high barrier and preservation properties
Bibliographic record
Abstract
Abstract Nanocellulose films have been extensively studied for their excellent oxygen barrier properties. However, in the presence of moisture and higher humidity, the oxygen barrier performance decreases rapidly. In this work, natural rubber latex (NRL) was used as a compounding material to improve the hydrophobic properties of 2,2,6,6-tetramethylpiperidin-1-oxyl (TEMPO) oxidized nanocellulose fibers (TOCNF) due to the ability of its naturally occurring phospholipid-protein surface to avoid the interfacial compatibility problems that exist in most hydrophobic polymers when mixed in aqueous solutions. The exposure of the internal hydrophobic isoprene molecular chains of NRL during the drying process allows the composite film to have greatly improved water resistance and excellent water vapor and oxygen barrier properties. The water vapor permeability (WVP) and oxygen permeability (OP) of the films were as low as 6.07×10− 10g·mm/m2·s·pa and 3.11×10− 15 cm3·cm/cm2·s·Pa, respectively. And the good water resistance of the composite film makes the wet tensile strength of the film up to 15.87 MPa, which reaches 71.69% of the dry tensile strength. In addition, the high ductility of NRL makes the laminate film good toughness, and its elongation at break can reach about three times that of most nanocellulose-based films. Experiments on strawberry preservation with composite films have shown that it can effectively slow down the deterioration of strawberries and extend their shelf life from two days to seven days. This study highlights the exceptional promise of these innovative films for use in food packaging applications.
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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".