Investigation of Dynamic Mechanical Behavior of Nanosilica Filled Carbon-Kevlar-Epoxy Polymer Hybrid Nanocomposite
Bibliographic record
Abstract
Reinforcement of epoxy-carbon-Kevlar fabric composite with the addition of nanosilica has resulted in the evolution of new hybrid polymer nanocomposite, which results in the improved mechanical properties of polymer hybrid nanocomposite. The current investigation concentrated on the dynamic mechanical behavior of unfilled and nanosilica filled carbon-Kevlar-epoxy polymer composite with five and four layers of carbon and Kevlar woven fibers respectively with epoxy matrix (5C4K). Nanosilica was mixed into the epoxy at different weight percentages (wt.%) of 0, 0.5, 1.0, and 1.5. The laminates were fabricated using the vacuum-assisted resin infusion moulding (VARIM) technique. The dynamic mechanical properties, storage modulus, loss modulus, damping factor (tan delta), and glass transition temperature was investigated using a dynamic-mechanical analyzer at temperature ranging from 25 to 165 degrees Celsius. The test specimens were prepared in accordance with the ASTM D4065 standard to investigate dynamic mechanical analysis (DMA) of the hybrid polymer nanocomposite. The results of the tested specimens for dynamic mechanical behaviors of carbon-Kevlar-epoxy hybrid nanocomposites are very much influenced by the presence of nanosilica. The storage modulus, loss modulus for nanosilica added hybrid polymer composites were more than the unfilled ones and the damping factor (tan delta) was observed more in an unfilled composite.
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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 | 0.000 |
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 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".