The role of interfacial compatibilization upon the microstructure and electrical conductivity threshold in polypropylene/expanded graphite nanocomposites
Bibliographic record
Abstract
Abstract Attempts have been made to evaluate the effect of interface and degree of interfacial interaction upon electrical conductivity threshold in polypropylene/expanded graphite (PP/EG) nanocomposites, and dispersion state of graphite nanosheets. For this purpose, maleic anhydride grafted polypropylene (PPgMA) and maleic anhydride grafted EPDM (EPDMgMA) were used as compatibilizer. Nanocomposite samples containing 1–5 vol% of EG were prepared by melt mixing method using laboratory scale internal mixer. Characterization was carried out by using X‐ray diffraction (XRD), differential scanning calorimeter (DSC), thermo‐gravimetric analysis (TGA), scanning electron microscope (SEM), transmission electron microscope (TEM), and rheo‐mechanical spectroscopy (RMS). The conductivity measurements were carried out by using four point probe method according to ASTM D991. Results showed that the conductivity threshold is controlled by the extent of interfacial interaction between PP and EG. So, better conductivity was obtained using PPgMA as compatibilizer which causes higher level of interaction between PP and EG, and therefore better dispersion of the EG nanolayers in the polymer matrix. On the other hand, high levels of compatibilizers, especially EPDMgMA, caused formation of separated aggregates of EG shelled with the compatibilizer, which results in the reduction of conductivity of the nanocomposites. This finding has been verified by SEM, RMS, and conductivity measurements. Effects of EG nanolayers on crystalline structure and thermal decomposition temperature of the nanocomposites have also been investigated by DSC and TGA, respectively. Copyright © 2009 John Wiley & Sons, Ltd.
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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".