Thermally Conductive Styrene-Butadiene Rubber/Boron Nitride Nanotubes Composites
Bibliographic record
Abstract
The use of boron nitride nanotubes (BNNTs) for fabrication of thermally conductive composites has been explored in the last years. Their elevated thermal conductivity and high mechanical properties make them ideal candidates for reinforcement in polymeric matrices. However, due to their high tendency to agglomerate, a physical or chemical treatment is typically required for their successful incorporation into polymer matrices. Our previous study about the dispersibility of BNNTs allowed determination of good solvents for dispersion. Here, we performed a similar characterization on styrene-butadiene rubber (SBR) to determine its solubility parameters. Although these two materials possess different solubility parameters, it was possible to bridge this gap by employing a binary mixture. The solvent casting approach followed by hot pressing was chosen as a suitable method to obtain thermally conductive SBR/BNNT composites. The resulting nanocomposites showed up to 35% of improvement in thermal conductivity and a 235% increase in storage modulus in the frequency sweep, when a BNNT loading of 10 wt% was used. However, the viscoelastic properties in the amplitude sweep showed a negative effect with the increase in BNNT loading. A good balance in thermal conductivity and viscoelastic properties was obtained for the composite at a BNNT loading of 5 wt%.
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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 | 0.001 |
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 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".