Use of electrospinning to disperse nanosilica into silicone rubber
Bibliographic record
Abstract
Nanomaterials have attracted considerable attention due to their unique physicochemical properties. However, agglomerations of nanofillers in nanocomposites have hindered the potential improvements to their properties. A novel technique of infusing nanosilica into silicone rubber with reduced agglomeration is reported in this paper. The technique involves electrospinning of silicone fibers, embedded with nanosilica, and incorporating the fibers into silicone rubber. Compared to high shear mechanical mixing, a larger volume fraction of nanosilica can be dispersed more uniformly into silicone rubber by electrospinning. The morphology of nanosilica composites was characterized by means of scanning electron microscopy and thermal heating tests using an infrared laser suggest an improvement to the thermal conductivity. These results suggest that electrospinning can tear apart particle agglomerations thereby improving the dispersion of nanoparticles into silicone rubber matrix.
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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) | 0.002 | 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".