Evolution of some dielectric properties of polypropylene-nanoclay composites with DC poling
Bibliographic record
Abstract
Space charge and DC conductivity evolution with DC poling of two types of polypropylene-based nanocomposites containing different concentrations of nanofiller were investigated. The two nanofillers used were natural and synthetic organoclays. It was observed that the optimal concentration of a nanofiller for mitigating space charge in polypropylene (PP) is 2-wt% and between 2 and 4-wt% for the natural and synthetic clay, respectively. Excessive quantity of nanofiller could lead to overlapping of the interaction zone of double layers formed at the nanoparticle/host material interfaces, which promotes charge transport. Under DC field this overlapping increases conductivity of a nanocomposite and thus could minimize the benefit of incorporating nanofillers into PP. The total charge stored in unfilled PP increased continuously with time but increased only slightly for filled specimens. After only 504 h of DC poling, at -25 kV/mm, the conductivity of specimens containing 6-wt% of natural clay and 8-wt% of synthetic clay reached ≈ 6 times the level for unfilled PP. This observation could be related to crossing the percolation threshold of these composites. The effect of platelet size on space charge mitigation reported by other authors has been confirmed in this work, i.e. nanofillers with smaller platelets mitigate space charge more efficiently then nanofillers with larger platelets.
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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".