Design and engineering of novel cast films from plasticized cellulose acetate filled mineral fillers for flexible packaging applications
Bibliographic record
Abstract
Plasticized cellulose acetate (pCA)-based composite films were fabricated via hot-melt cast film extrusion as a sustainable alternative to both solvent cast processing and conventional non-biodegradable plastics, for flexible packaging applications. Low-acetyl content cellulose acetate (CA) was plasticized with bio-based triacetin (pCTA) or petrochemical triethyl citrate (pCTEC). Then, composite films were developed from pCA reinforced with 10 or 15 wt% talc or recycled CaCO₃ (rCaCO₃), within a small amount of Luperox as a compatibilizer. Talc-filled pCTEC composites exhibited the best performance, with elastic modulus and tensile strength improvements of up to 136% and 40%, respectively, over neat pCTEC films. These enhancements were attributed to improved filler-matrix adhesion, strain-induced crystallization, and increased crystallinity. In terms of thermal stability, TmaxTEC (315.37 °C) greatly surpassed TmaxTA (226.25 °C) and this trend maintained across all pCTEC-based composites. Moreover, talc-filled pCTEC composites provided superior barrier properties than pCTA-based composites, reducing oxygen and water vapor permeability by up to 38% and 72%, respectively, compared to neat pCTEC film. These results underscore the potential of pCA-based composites for sustainable flexible 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.012 |
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".