Dielectric properties of Boron Nitride and silica epoxy composites
Bibliographic record
Abstract
Epoxies are widely used materials, especially in high voltage insulation for its mechanical and dielectric properties. Boron Nitride is an attractive filler because of its high insulating properties associated with a high thermal conductivity. Nanometric Silica used as filler is actually widely studied in the literature and enhancement of dielectric breakdown and insulating properties has been shown. Using a combination of these two fillers in an epoxy matrix may produce a new material with improved properties. Samples containing only micrometric filler (Boron Nitride), only nanometric filler (Silica) and both fillers were prepared by gravity moulding. A pure epoxy sample was also prepared to quantify the enhancement properties. Broadband Dielectric Spectroscopy was used to study the influence of nanoparticles on the relaxation mechanisms in the composites. An additional relaxation peak was highlighted for samples containing nanosilica between the local β relaxation, associated with crankshaft motions of the hydroxylether groups, and the main α relaxation, corresponding to glass transition cooperative movements. This relaxation phenomenon was found to be associated with the water bounded at the surface of the silica particles. Further relaxation investigation will be performed to clarify the influence of water, its state and its role.
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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".