Fabrication and characterization of electrospun composite materials
Bibliographic record
Abstract
In this work, the characteristics of silicone rubber (SiR) and epoxy composites prepared by using the electrospinning technique are compared with conventionally prepared composite materials. Both micron sized silica fillers and silica/aluminum nanofillers are used in preparing the composites. The technique of electrospinning was used to enhance the dispersion of nanofillers by embedding the fillers in the polymer. The thermo-gravimetric analysis(TGA) of the composites has shown that the interfacial bonding between the fillers and the polymer matrix is strong in those samples prepared using the ES technique, which agrees with the uniform filler morphology observed in the SEM images. Further, it has been shown that heat resistance, tensile strength, and elongation at break for electrospun SiR samples are better than those for conventionally prepared samples. Similarly, for epoxy composites prepared using ES technique, a significant improvement in thermal properties have been obtained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".