Improving the tracking and erosion resistance of silicone rubber using Fe<sub>2</sub>O<sub>3</sub> and platinum catalyst
Bibliographic record
Abstract
Abstract In typical HTV silicone rubber for composite insulators subjected to a 4.5 kV inclined plane (IP) test, alumina trihydrate loading exceeds 40 wt%, which limits the rubber content and weakens the antiaging properties. This study compares two methods for enhancing tracking and erosion resistance while reducing ATH content to meet IP test requirements. Increasing Fe2O3 content from 1.5 wt% to 3 wt% promotes the formation of mullite. The formed ceramic layer isolates heat and oxygen and hence facilitates the specimen to pass the IP tests with less content of ATH. By contrast, Pt/NS system cannot suppress the tracking efficiently by forming the SiOC ceramics at high ATH loading. The decomposition of ATH will generate water vapor and lead to holes in the surface. Both moisture and oxygen absorbed have a detrimental effect on the suppression mechanism of the Pt/NS system. Consequently, it is recommended that the content of Fe2O3 can be increased slightly to lower the ATH content in the traditional HTV SiR formula used for the composite insulator. Besides, when the content of ATH is relatively high, the use of the Pt/NS system is not recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".