Aging assessment of silicone rubber materials under corona discharge accompanied by humidity and UV radiation
Bibliographic record
Abstract
Abstract High voltage (HV) outdoor insulators are subjected to both electrical and environmental stresses, which may lead to their failure. Among the causes, corona discharge, humidity and UV radiation are considered to be the most damaging factors. Efforts are therefore underway to investigate new materials for improving the performance of insulating systems. In this research work, silicone-based room temperature vulcanized samples filled with alumina trihydrate (ATH), silicon dioxide and magnesium hydroxide (MH) were prepared and exposed to AC corona discharge for a duration of 110 h. The electric discharge was also accompanied by UV radiation and two different humidity levels. Following aging of the test samples, diagnosis was conducted to assess their integrity. Measurements based on determining the static contact angle demonstrated the loss of hydrophobicity of all the materials, while hydrophobicity recovery phenomena revealed that ATH-doped materials demonstrated a comparatively higher increase in the contact angle than in samples filled with silicone dioxide (silica) and MH. Scanning electron microscopy analysis revealed deep cracks and block-like structures on their surfaces. Similarly, energy-dispersive X-ray analysis indicated the signs of surface oxidation of the aged samples. However, the data of elemental composition exhibited the loss of filler contents as well as that of carbon from the base matrix. The overall assessment showed that resistance to suppress aging is influenced by both the filler type and its concentration in the investigated composites. The ATH-filled composites exhibited outstanding performance when exposed to the rigors of corona discharge and other environmental stresses. This research contributes to materials science and HV engineering by addressing the development of composites for enhanced insulator performance, with future aspects lying in the utilization of nano-composites for advanced functionality and durability.
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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) | 0.000 | 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".