Effect of Back‐layer on seal performance of multilayer polyethylene‐based sealant films
Bibliographic record
Abstract
Abstract This work studies effects of back‐layer materials, thickness of sealant layer, and sealing condition on seal performance of multilayer polyethylene‐based films. Multilayer films with back‐layers of high‐density polyethylene (HDPE), or low‐density polyethylene (LDPE), or linear low‐density polyethylene (LLDPE) were produced with different thicknesses of the metallocene layer. It was found that increasing the thickness of the metallocene layer improved hot tack properties. In addition, films with back‐layers of LLDPE or LDPE showed higher hot tack strength compared to those with HDPE back‐layer. Increasing sealing temperature reduced significantly the hot tack strength and its dependency on metallocene layer thickness. It was found that increasing delay time after sealing, before peeling test, increased hot tack strength, but the rate of hot tack evolution and the type of peeling behavior were considerably affected by the type of back‐layer material. The effect of dwell time was also examined, and it was observed that increasing dwell time in the studied range did not affect the hot tack evolution. The mechanisms involved in the development of hot tack evolution were discussed, and it was shown that the back‐layer effects can be explained by bulk viscoelastic energy dissipation theory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".