Rheology/morphology relationship of plasticized and nonplasticized thermoplastic elastomers based on ethylene–propylene–diene–terpolymer and polypropylene
Bibliographic record
Abstract
Abstract The rheology/morphology relationship of plasticized and nonplasticized ethylene–propylene–diene–terpolymer/polypropylene (EPDM/PP) TPOs was studied. The aim was to investigate the effect of a plasticizer on the morphology, specially on the co‐continuity interval of these blends. The addition of a plasticizer increased the interconnectivity of the elastomeric phase, resulting in a rapid percolation of the EPDM at a relatively low composition range as compared to the nonplasticized counterparts. However, the addition of the plasticizer did not change the onset of the co‐continuity interval in the low EPDM content side of the composition diagram. Moreover, due to plasticization, the percolation of the PP phase was delayed on the other side of the composition diagram. Large differences between the viscous and elastic properties of the constituent polymers were observed. Hence, a combination of low frequency measurements and a gel approach were crucial to characterize the co‐continuity interval using rheology. The phase inversion compositions were fairly well described by existing semi‐empirical viscosity ratio‐based models. Furthermore, a satisfactory prediction was obtained for the viscoelastic properties of the nonplasticized TPOs using a micro‐mechanical model. However, this model failed in the case of the plasticized TPOs, due to the probable presence of a plasticized interphase. POLYM. ENG. SCI., 2011. ©2011 Society of Plastics Engineers
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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".