Degradation of polymer dielectrics with nanometric metal-oxide fillers due to surface discharges
Bibliographic record
Abstract
Recent research has indicated that dielectric properties of polymer insulating materials might be improved by the inclusion of nanosized particles dispersed in the polymer matrix. Insulating materials in power apparatus are often exposed to surface discharges in the course of normal operation. Surface degradation due to continued exposure to such discharges may cause deterioration of the surface, and could ultimately lead to catastrophic failure. The current work investigates the effect of inclusion of nanometric particles on the ability of a polymeric dielectric to resist degradation when exposed to surface discharges. The dielectric material used was epoxy resin, while nanosized alumina (Al2O3) and titania (TiO2) were used as fillers. Surface discharge tests were carried out on the specimens. The degraded surfaces were studied using a scanning electron microscope (SEM). Surface roughness measurements were made using a laser surface profilometer (LSP). It was observed that the degradation was greater for unfilled epoxy specimens than that for filled ones. Atomic force microscopy (AFM) and energy dispersive X-ray analysis (EDX) were used to identify surface changes in the dielectric material due to degradation. It has been conclusively shown that addition of even very small volume fractions of nanoparticles increases the resistance of the material to degradation due to surface discharges. A possible mechanism for surface degradation in nanocomposites has been proposed.
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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 | 0.001 |
| 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".