A Novel Framework to Study the Role of Ground and Fumed Silica Fillers in Suppressing DC Erosion of Silicone Rubber Outdoor Insulation
Bibliographic record
Abstract
This paper investigates the effect of ground and fumed silica fillers on suppressing DC erosion in silicone rubber. Fumed silica and ground silica fillers are incorporated in silicone rubber at different loading levels and comparatively analyzed in this study. Outcomes of the +DC inclined plane tracking erosion test indicate a better erosion performance for the fumed silica filled composite despite having a lower thermal conductivity compared to the ground silica composite. Results of the simultaneous thermogravimetric and thermal differential analyses are correlated with inclined plane tracking erosion test outcomes suggesting that fumed silica suppresses depolymerization and promotes radical based crosslinking in silicone rubber. This finding is evident as higher residue is obtained with the fumed silica filler despite being filled at a significantly lower loading level compared to ground silica. The surface residue morphology obtained, and the roughness determined for the tested samples of the composites in the dry-arc resistance test indicate the formation of a coherent residue with the fumed silica filled composite. Such coherent residue could act as a barrier to shield the unaffected material underneath the damaged surface during dry-band arcing, thereby preventing progressive erosion. The outcomes of this study suggest a significant role for fumed silica promoting more interactions with silicone rubber to suppress DC erosion compared to ground silica fillers.
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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".