Evolution of rheological properties and morphology development during crosslinking of polyolefin elastomers and their TPV blends with polypropylene
Bibliographic record
Abstract
Abstract This work investigates the evolution of the rheological properties of elastomeric dynamically vulcanized ethylene‐α‐olefin copolymers (ECs) and their blends with polypropylene (PP), during peroxide initiated crosslinking. Rheological techniques are used in conjunction with gel content measurements to determine the onset of gelation during static crosslinking. The complex viscosity and moduli follow power‐law dependence with respect to frequency at the gel point. The relaxation exponent and corresponding values of tan δ at the gel point are determined from the complex viscosity versus frequency curves and used as criteria for the determination of the instance of gelation. The evolution of morphology of thermoplastic vulcanizate (TPV) blends consisting of EC and PP during dynamic crosslinking is discussed in the context of the evolving rheological properties of the matrix and the dispersed phase that take place upon peroxide modification. TPVs having the crosslinked EC as the matrix present a very fine morphology, whereas the blends containing crosslinked EC particles, present a coarser morphology. POLYM. ENG. SCI., 2009. © 2008 Society of Plastics Engineers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".