Suppression of silicone rubber erosion by alumina trihydrate and silica fillers from dry-band arcing under DC
Bibliographic record
Abstract
This paper describes the suppression of dry-band arcing erosion of silicone rubber by alumina tri-hydrate and silica fillers in the DC inclined plane test, employing the wavelet based multiresolution analysis of leakage current. The third detail component of the leakage current as decomposed by the wavelet-based multiresolution analysis is shown to be an indicator of the effectiveness of the filler type in suppressing erosion by dry-band arcing. The addition of alumina tri-hydrate or silica filler to silicone rubber increases the thermal conductivity of the composites, retarding the development of the eroding temperature, and thus the evolution of the third detail. Additional effect is also obtained for the dehydration enthalpy, of alumina tri-hydrate in silicone rubber at a filler level of 30 wt%, in impeding the development of hot spots on the tested surface. A reduction in the magnitude of the third detail is evident with filler level, indicating that the increasing volume of silica or alumina tri-hydrate reduces the temperature of the dry-band arcing plasma. Comparable levels of the leakage current third detail is found between 30 wt% alumina tri-hydrate and silica filled composites which suggests the water of hydration plays a minor role in diluting the SiR but at 58 wt% an internal oxidation mechanism that produces gases diluting the arcing phase appears to suppress erosion.
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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".