Improved Thermal Properties and Erosion Resistance of Silicone Composites With Hexagonal Boron Nitride
Bibliographic record
Abstract
In this article, silicone rubber (SiR) is filled with industry preferred micro-silica and specialty hexagonal boron nitride (h-BN) nanosheets, by varying the ratio between the two fillers, while keeping the filler to matrix ratio the same in all three composites. The fillers are added to SiR using an electrostatic disperser which aids in homogenizing the filler-polymer mixture by means of shearing force along with elongation. The performance of synthesized silicone composites are evaluated using thermal, erosion resistance, morphological, mechanical, and high-temperature dielectric properties. Addition of as low as 5 wt% h-BN fillers significantly improved the thermal properties; 6%–20% improvement in thermal conductivity and in less than half the weight loss compared to only silica filled composites with laser erosion. The morphological and mechanical analyses showed improved filler dispersion and filler to silicone matrix interaction owing to good filler dispersion achieved through the electrostatic dispersing method. The dielectric properties measured in wide frequency range, between 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> and 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Hz and at elevated temperatures (up to 175 °C) showed a negligible effect of increase in h-BN content. Thus, this article implies that low amounts of h-BN in silica-silicone composites improve thermal conductivity and erosion resistance significantly and not altering the dielectric responses of these hybrid composites even at elevated temperatures. Hence these composites can find applications both in the high voltage industry and as electronic packaging materials.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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 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".