Improvement of Cell Opening by Maintaining a High Temperature Difference in the Surface and Core of a Foam Extrudate
Bibliographic record
Abstract
This article presents an extrusion-based, open-cell foaming process using thermoplastic polymers such as polystyrene (PS) and polycarbonate (PC) with supercritical CO 2 . Our previous studies have indicated that a cell opening can be promoted by inducing: (i) a nonhomogeneous melt structure by cross-linking, polymer blending, or filler compounding, (ii) cell-wall thinning by a high volume expansion ratio while maintaining soft cell walls, (iii) cell-wall thinning by a high cell-population density, and (iv) plasticization of the soft region of the cell walls with a secondary blowing agent. Until now, the foam extrudate temperature across the cross-section was maintained uniformly for the simplicity of the experiments. In this study, the significant temperature difference between the core and surface of the foam extrudate was induced by surface cooling method. This method increased the chance of cell opening by: (i) increasing the core temperature of the foam extrudate and thereby softening the cell walls, and (ii) decreasing the foam surface temperature to prevent gas loss and thereby increasing the internal gas pressure within the cells. The effects of CO 2 content, surface quenching, die geometry, and temperature on foam morphologies were investigated. Low-density, microcellular, open-cell foams were successfully produced. The large intercellular pores were observed from micrographs for both PS and PC foams at optimum processing conditions.
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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".