Effect of heat‐sealing parameters on the thermal profile and seal strength of multilayer films and non‐woven
Bibliographic record
Abstract
Abstract Heat sealing is one of the most common techniques for sealing food packages. In this study, the effects of sealer parameters such as dwell time (0.5–2 s), jaw temperature (225–250°C) and pressure (69–345 KPa) and the seal contaminants (coffee particles [0.70–0.85 mm]) were studied on polymer materials used in coffee capsules: (1) lidding, poly(ethylene terephthalate) (PET)/Al/linear low‐density poly(ethylene) (LLDPE); (2) wall, polystyrene (PS)/ethylene‐vinyl alcohol copolymer (EVOH)/PS; and non‐woven filter, NW1 (mono‐component fibre) or NW2 (bicomponent fibre [core/sheath]). The presence of NW1 and NW2 non‐woven in the seal structures decreased the interfacial temperature by 5°C and 11°C, respectively in comparison to seal structures without non‐woven. The degradation of the seal bonding caused a decline in the seal strength at elevated temperature (NW: >240°C without NW: >235°C) and longer dwell time (NW: >1 s, without NW: >1.5 s). The core and sheath structure of NW2 (969 N/mm) was responsible for higher seal strength in comparison to NW1 and without NW, as the core remained intact during the sealing, increasing the material strength. The minimum pressure of 207 KPa was required for creating enough contact between films to achieve proper sealing. The coffee particles buried under the opaque layer of lidding films created visible thermal artefacts when observed under thermal camera. The contaminated region differed by 30–38°C from the surrounding sound region after cooling for 3.5 s. The seal strength was stronger when a single coffee particle was in the middle compared to a sample with multiple particles.
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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".