Foaming behavior of microcellular thermoplastic olefin blends
Bibliographic record
Abstract
The influence of reactive compatibilization on the foaming behavior of thermoplastic olefin blends of polypropylene and a metallocene-catalyzed ethylene octene copolymer was investigated. A batch setup was used to foam the samples using carbon dioxide as blowing agent. Solubility measurements were performed to determine the relative amount of gas concentration in the pressurized polymers before foaming. A microscopic method based on the back-scattered electron imaging technique was used to determine the respective locations of the bubbles and the dispersed elastomeric domains in the polypropylene matrix. It was shown that the bubbles are preferentially formed in the dispersed elastomeric domains. A clear relationship was established between the microstructure of the blends prepared with different levels of compatibilizer and the final cellular morphology of the microcellular thermoplastic olefin foams. The initial morphology of the blends was also altered by quiescent coarsening as well as shear-induced phase coalescence, and the impact of the morphological transitions on the cellular structure of the resulting foams was investigated. Dynamic shear and transient measurements of elongation were performed to characterize the viscoelastic behavior of the thermoplastic olefins. It was shown that the addition of a compatibilizer resulted in enhanced viscoelastic properties at low frequencies as well as increased levels of strain hardening, especially at low strain rates. The reactive compatibilization could significantly improve the melt foamability through control of the blend microstructure as well as enhancement of the melt rheological properties.
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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".