Effect of blending and nanoclay on dielectric properties of polypropylene
Bibliographic record
Abstract
This paper investigates the effects of organomodified montmorillonite (clay) and styrene-(ethylene-co-butylene)-styrene triblock copolymer (SEBS) on the morphological and electrical properties of polypropylene (PP). A series of binary PP-clay nanocomposites along with nanocomposites having a blend matrix were prepared. The nanofillers were found to be well-embedded into the polymeric matrix with a high degree of dispersion. The microstructure of the blend matrix revealed a co-continuous structure for the equal proportion of the two polymers. This was shown to control the localization of nanofiller by triggering them to migrate into the SEBS phase, mostly accommodating in the interface and creating a strong network which eventually resulted in more exfoliation of clay platelets and comparable/superior electrical properties comparing to binary nanocomposites. The incorporation of clay resulted in a solid-like rheological behavior which was more enhanced in blend nanocomposites due to the stronger network of nanofiller. The dielectric spectra of the nanocomposites revealed two major relaxation processes aroused by the presence of clay. A new relaxation process was observed for the nanocomposites with the blend matrix, related to the SEBS phase. Both blending and nanofiller inclusion resulted in less accumulated space charge. A significant improvement in the AC breakdown strength of PP was witnessed upon addition of clay. Despite the less inherent breakdown strength of SEBS, the blend nanocomposites showed even more enhanced breakdown properties confirming the further improvement of nanofiller network structure.
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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".