The effects of nanoclay dispersion levels and processing parameters on the dynamic vulcanization of TPV nanocomposites based on PP/EPDM prepared by reactive extrusion
Bibliographic record
Abstract
Abstract The study considers the effects of different dispersion levels of nanoclay on the crosslinking reaction of thermoplastic vulcanizate (TPV) nanocomposites based on polypropylene/ethylene propylene diene M‐class rubber (PP/EPDM). PP nanocomposites with dispersion level ranging from intercalated structures to a mixture of intercalated tactoids and exfoliated layers were used as the thermoplastic phase. Dimethylol phenolic resin or octylphenol‐formaldehyde resin was used as curing agents, along with stannous chloride dihydrate as the catalyst, to vulcanize the rubber phase during the reactive extrusion process. Initially, temperature effects were investigated in internal batch mixer. Subsequently, the effects of screw speed (i.e., shear rate and residence time) were evaluated along the screw length. Different criteria such as nuclear magnetic resonance (NMR) signal line width, bound curative content, and residual diene concentration were used to evaluate the extent of crosslinking, along with normalized storage modulus and gel content. X‐ray diffraction (XRD) analysis and transmission electron microscopy (TEM) micrographs showed that the dynamic vulcanization process improves the dispersion level of nanoclay in the final TPVs. It was found that the presence of nanoclay influences the crosslinking reaction, mainly through its effect on the continuity index of the EPDM phase. POLYM. ENG. SCI., 2012. © 2011 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".