Tailoring Heat-Seal Properties of Biodegradable Polymers through Melt Blending
Bibliographic record
Abstract
Abstract In this study, we address heat-seal properties of poly (lactic acid) (PLA), blended with Poly (butylene adipate-co-terephthalate) (PBAT). The objective is to correlate blends crystalline structure and morphology to corresponding heat-seal of blends films. The SEM micrographs show a two-phase elongated morphology where stretched ellipsoids developed through elongational flow during the cast film process. To distinguish the effect of crystallization, we also prepared amorphous and crystalline PBAT films and then compared them to blends with PLA. Heat-sealed areas were created by putting film surfaces in intimate contact for 1 s at the pressure of 0.5 N/mm2 or Pa and in the temperature range of 70 to 140 °C. Thermal analysis shows that the crystalline structure of PBAT has a significant effect on shifting its heat-seal initiation temperature (Tsi) up to 20 °C. Regarding the blends, incorporation of PBAT as a dispersed phase lowers Tsi of blend samples. Here, gradual decrease in PBAT crystallinity caused by the hindering effect of PLA rigid molecules correlates with the shift in heat-seal initiation temperature. As mentioned above, elongated disperse morphology with higher aspect ratio of the dispersed phase compared to spherical dispersed domain, is formed through film cast process. This enhances the adhesion process by providing higher contact area. The blends also show higher toughness and better puncture resistance, which is an asset for flexible packaging applications and would enhance the mechanical performance of the seal layer.
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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".