Preparation of microcellular poly(ethylene‐<i>co</i>‐octene) rubber foam with supercritical carbon dioxide
Bibliographic record
Abstract
Abstract In the past 3 decades, there has been great advancement in the preparation of microcellular thermoplastic polymer foams. However, little attention has been paid to thermoplastic elastomers. In this study, microcellular poly(ethylene‐co‐octene) (PEOc) rubber foams with a cell density of 2.9 × 1010 cells/cm3 and a cell size of 1.9 μm were successfully prepared with carbon dioxide as the physical blowing agent with a batch foaming process. The microcellular PEOc foams exhibited a well‐defined, closed‐cell structure, a uniform cell size distribution, and the formation of unfoamed skin at low foaming temperatures. Their difference from thermoplastic foam was from obvious volume recovery in the atmosphere because of the elasticity of the polymer matrix. We investigated the effect of the molecular weight on the cell growth process by changing the foaming conditions, and two important effect factors on the cell growth, that is, the polymer matrix modulus/melt viscoelastic properties and gas diffusion coefficient, were assessed. With increasing molecular weight, the matrix modulus and melt viscosity tended to increase, whereas the gas solubility and diffusion coefficient decreased. The increase in the matrix modulus and melt viscosity tended to decrease the cell size and stabilize the cell structure at high foaming temperatures, whereas the increase in the gas diffusion coefficient facilitated cell growth at the beginning and limited cell growth because most of the gas diffused out of the polymer matrix during the long foaming times or at high foaming temperatures. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010
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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) | 0.000 | 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".