Hybrid Fillers for Thermal Conductivity and Erosion Resistance Enhancements in Silicone Composites
Bibliographic record
Abstract
The addition of micro-sized fillers for reinforcing the matrix in a composite is well known. However, the addition of small amounts of nanofillers can have a great impact on the properties of a composite. In this study, electrical and thermal properties of silicone rubber filled with micro-sized silica and nano-sized hexagonal boron nitride (h-BN) have been analyzed. Addition of as low as 5% h-BN fillers has significantly improved the thermal properties as reflected in less than half the weight loss compared to only silica filled composites with laser erosion, although any noticeable improvements with thermal conductivity is observed. Low values of thermal conductivity in h-BN filled composites can be due to high concentrations of silica fillers that mask the point to point contacts for h-BN fillers. Similarly, the dielectric spectroscopy data can be related to the interactions between silica, h-BN fillers, and silicone rubber within the composites.
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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 | 0.000 | 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 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".