<i>In situ</i> dynamic vulcanization process in preparation of electrically conductive PP/EPDM thermoplastic vulcanizate/expanded graphite nanocomposites: Effects of state of cure
Bibliographic record
Abstract
Abstract In situ melt dynamic vulcanization process has been employed to prepare electrically conductive polypropylene (PP)/ethylene–propylene–diene rubber (EPDM) (40/60 wt %) thermoplastic vulcanizates (TPVs) incorporated by expanded graphite (EG) as a conductive filler. Maleic anhydride grafted PP (PP‐g‐MAH) was used as compatibilizer and a sulfur curing system was designed and incorporated to vulcanize the EPDM phase during mixing process. Developed microstructures were characterized using scanning electron microscopy (SEM), melt rheomechanical spectroscopy (RMS), X‐ray diffraction (XRD), and transmission electron microscopy (TEM) and were correlated with electrical conductivity behavior. For comparison, another class of TPV/EG nanocomposites was fabricated using a commercially available PP/EPDM‐based TPV via both direct and masterbatch melt mixing process. Conductivity of the nanocomposites prepared by in situ showed no significant change during dynamic vulcanization till the mixing torque reached to the stationary level where micro‐morphology of the cured rubber droplets was fully developed, and conductivity abrupt was observed. In situ cured nanocomposites showed higher insulator to conductor transition threshold (3.15 vol % EG) than those based on commercially available TPV. All electrically conductive in situ prepared TPV nanocomposites exhibited reinforced melt elasticity with pseudosolid‐like behavior within low frequency region in dynamic melt rheometry indicating formation of physical networks by both EG nanolayers and crosslinked EPDM droplets. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
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".