Effect of Talc Content on the Volume Expansion Ratio of Extruded PP Foams
Bibliographic record
Abstract
This paper elucidates the effects of cell density on the volume expansion behavior of polypropylene (PP) foams blown with butane in extrusion. The cell density was controlled by varying the talc content, and foam expansion was observed at a fixed blowing agent content while varying the melt and die temperatures. As observed in our previous studies, the curve of the final expansion ratio of PP foam versus temperature showed a typical mountain shape for each talc content, confirming that the two governing expansion mechanisms were gas loss and stiffening of melt. As the talc content increased, the expansion curve skewed towards the lower temperature, which showed that the expanded foams with a high talc content were more susceptible to gas loss at elevated temperatures. This indicates that the processing temperature should be decreased to have a large expansion ratio from the extruded PP foams at a high talc content. In other words, the optimum temperature to maximize expansion decreased at a higher talc content. It is believed that the shift of the expansion curves was caused by the promoted expansion rate of extruded foams with a greater talc content because of the reduced diffusion distance of gas molecules to the nearest stabilized nucleus. On the other hand, the increased cell density at a high talc content increased the number of cell layers in the cross section of the extruded foam, and thereby the gas loss was localized to the cells on the surface which acted favorable for the final expansion ratio to a certain degree.
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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) | 0.000 | 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 | 0.001 |
| 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".