Effects of nanotitania on structure-dielectric properties of polypropylene blends
Bibliographic record
Abstract
Polypropylene (PP) satisfies many of the requirements as power cable insulation thanks to its high thermal stability, ability to withstand high voltages, and eco-friendly properties. Due to the high stiffness and brittleness of PP, the extrusion of PP as power cable insulation becomes difficult. Therefore, elastomers have been blended with PP to enhance PP’s flexibility. Nevertheless, one common issue arises when blending elastomer with PP is the degradation of PP/elastomer blends’ dielectric properties. Recently, nanodielectrics have drawn utmost attention as a promising material system for improving the overall performance of polymer-based insulating materials, but such a material system has yet to be pursued for PP blend materials. The current work therefore investigates the effects of titania (TiO 2 ) on the structure and dielectric properties of PP/ethylene-based elastomer (EBE) and PP/propylene-based elastomer (PBE). While adding high amounts of TiO 2 degrades the blend materials’ breakdown strength, the inclusion of small amounts (0.5 wt% and 1 wt%) of TiO 2 improves the breakdown strength of the blend materials by as much as 10%. Notably, the effect of TiO 2 is more apparent on PP/EBE system over PP/PBE system. Mechanisms underlying changes in the breakdown strength of the materials, under both alternating current (AC) and direct current (DC) fields, are analyzed.
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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".