Electrospinning as a new method of preparing nanofilled silicone rubber composites
Bibliographic record
Abstract
In this paper, electrospinning is shown to be an effective method for overcoming the agglomeration of nanosilica in silicone rubber nanocomposites. Silicone rubber nanocomposites with 7 nm silica were produced using conventional mixing and electrospinning processes. Scanning electron microscopy is used to demonstrate homogenous filler dispersion in electrospun samples as compared to conventional mixed samples. Thermo-gravimetric analysis and infrared-laser-based thermal tests show significant improvements in thermal stability and heat transfer in electrospun nanocomposites. However, no significant improvements in either the mechanical properties or relative permittivity between the electrospun and conventional mixed samples were found. The improved thermal properties of the electrospun silicone rubber nanocomposites are attributed to the significantly reduced nanofiller agglomeration and their uniform dispersion.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".