Preparation of interfacially compatibilized PP‐EPDM thermoplastic vulcanizate/graphite nanocomposites: Effects of graphite microstructure upon morphology, electrical conductivity, and melt rheology
Bibliographic record
Abstract
Abstract Electrically conductive PP/EPDM dynamically crosslinked thermoplastic vulcanizate (TPV)/expanded graphite (EG) has been successfully prepared via melt compounding of maleic anhydride grafted polypropylene (PP‐g‐MA)/EG masterbatch and a commercially available TPV material. Correlation between graphite microstructure, and electrical conductivity as well as melt rheological behavior has been studied. Natural graphite flake (NGF), graphite intercalated compound (GIC), and exfoliated graphite (EG) have been employed and compared. Scanning electron microscopy (SEM) showed the presence of 100–200 nm nanolayers in the structure of PP‐g/EG masterbatches, whereas thinner platelets (1.5–2.5 nm) were revealed by transmission electron microscopy (TEM). Better dispersion of the graphite nanolayers in the microstructure of TPV/PP‐g‐MA/EG composite was verified, as the 7.3 Å spacing between the aggregated graphite nanolayers could not be observed in the XRD pattern of this material. TPV/PP‐g/EG nanocomposites exhibited much lower conductivity percolation threshold (φc) with increased conductivity to 10−5 S/cm at EG wt % of 10. Higher nonlinear and nonterminal melt rheological characteristics of dynamic elastic modulus (G′) at low frequency region was presented by the TPV/PP‐g/EG nanocomposites, indicating the formation of nanoscopic conducting multiple networks throughout the continuous TPV matrix. Maleated PP was found to be much more effective in separating EG nanolayers which is attributed to the higher interfacial interaction between PP‐g‐MAH and EG, synergized with its multiporous structure. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2008
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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".