Microstructure‐properties correlations in dynamically vulcanized nanocomposite thermoplastic elastomers based on PP/EPDM
Bibliographic record
Abstract
Abstract Thermoplastic vulcanized (TPV) nanocomposites were prepared in a laboratory mixer using EPDM, polypropylene of different viscosities, maleic anhydride modified polypropylene, an organo‐clay, and a sulfur‐based curing system. Based on the obtained results from X‐ray diffraction, transmission electron microscopy (TEM), scanning electron microscopy (SEM), differential scanning calorimeter, and mechanical properties, the microstructure of the prepared nanocomposites was found to be sensitive to the viscosity difference between the two phases and the clay content. X‐ray diffraction and TEM images of the TPV nanocomposites showed that clay was nearly exfoliated and randomly distributed into the polypropylene phase. The SEM photomicrographs of the dynamically vulcanized thermoplastic elastomer samples showed that the rubber particles were dispersed through the polypropylene in form of aggregates and their size increased with the introduction of clay. The nanoscale dimensions of the dispersed clay resulted in a significant improvement of the tensile modulus of the TPV nanocomposite samples, from 20 to 90% depending on clay content and the viscosity ratio of PP/EPDM. In the PP nanocomposites, the clay layers act as nucleating agents, resulting in higher crystallization temperature and reduced degree of crystallinity. Moreover, the oxygen permeability in the TPV nanocomposites was found to be lower than in unfilled but otherwise similar materials. POLYM. ENG. SCI., 47:207–217, 2007. © 2007 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".