The effect of compatibilizer on the co‐continuity and nanoclay dispersion level of tpe nanocomposites based on PP/EPDM
Bibliographic record
Abstract
Abstract The effects of different polypropylene (PP)‐g‐maleic anhydride polymers, used as compatibilizers, on the degree of exfoliation and co‐continuity of PP/ethylene‐propylene‐diene terpolymer (EPDM) thermoplastic elastomer (TPE)/clay nanocomposites were investigated. X‐ray diffraction and transmission electron microscopic micrographs showed that nanocomposites ranged from intercalated structure to a coexistence of intercalated tactoids and exfoliated layers. The observed significant increase in crystallization temperature (∼20°C) could be beneficial for molding applications, because it means faster solidification and shorter cycle time. The rheological characteristic relaxation time of the compatibilizer correlated with the dispersion level in the nanocomposites. Solvent extraction and gravimetry measurements of continuity showed that compatibilizer affects the co‐continuity composition range through its effect on the dispersion level of nanoclays. At high EPDM concentration, the continuity of the thermoplastic phase for semi‐exfoliated TPE nanocomposites was higher than in the corresponding TPEs. Considering that TPE formation is the first step for thermoplastic vulcanizate production, where the thermoplastic phase should have a certain level of continuity, these results suggest that higher levels of EPDM could be incorporated into the semi‐exfoliated system before losing matrix continuity. It was also observed that there is a direct relation between the magnitude of the normalized stress growth viscosity overshoot and the continuity of TPE nanocomposites. POLYM. ENG. SCI., 2010. © 2010 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".