Influence of Filler-Polymer Interface on Performance of Silicone Nanocomposites
Bibliographic record
Abstract
The increase in voltage level and compactness of electrical equipment result in demands for electrical insulations that have high breakdown strength, high thermal conductivity, and high electrical resistivity. Use of dielectric polymer nanocomposites is a promising approach that has great advantages over the traditional materials. This article explores the influence of alumina nanofillers, both treated and untreated, on properties of silicone rubber-based nanocomposites. Composite samples made of 10 wt% and 20 wt% filler loading are prepared using high shear (HS) and electrostatic disperser (ES) techniques with the aim of achieving maximum dispersion of fillers in the silicone matrix. Effects of filler type, filler concentration and mixing method on morphological changes, thermal, erosion, and electrical properties are analyzed. Scanning electron micrographs (SEMs) showed better filler dispersion in composites prepared using ES than using HS mixer. Thermogravimetric analysis and thermal conductivity measurements revealed enhanced thermal stability and conductivity with increasing filler loadings. Additionally, ES composites showed high erosion resistance. Composites containing treated alumina performed better than those containing untreated alumina. In total, composites prepared with treated alumina using the ES method showed marked improvement in thermal properties and erosion resistance due to homogeneous filler dispersion imparting high number of filler-matrix interfaces and stronger bonding as visualized from SEMs and dielectric spectroscopy data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 | 0.001 |
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 teacher head, 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".