Influence of Nanofiller Dispersion on Electrical and Mechanical Properties of Epoxy Alumina Nanocomposites
Bibliographic record
Abstract
The electrical and mechanical properties of the epoxy alumina nanocomposites depend on the uniform dispersion of the nanofiller in the epoxy matrix. Epoxy alumina nanocomposites were prepared using 1, 3, and 5 wt% of alumina nanofiller, and electrical and mechanical properties were analyzed using experimental and modelling studies. Water droplet initiated corona inception voltage (CIV) was identified using fluorescence fiber technique and by Ultra High Frequency (UHF) technique, under AC and DC voltages. The CIV formed due to water droplet have reduced drastically with increase in number of droplets and fluorescent fiber technique found to be more sensitive to identify water droplet initiated discharges. A micro mechanical model was proposed to analyse the combined effect of the interphase and agglomeration properties of the alumina nanoparticles on the tensile strength of epoxy alumina nanocomposites. Variation in ϕagg, Eagg, and E were analysed by adopting the non-parametric distribution of alumina nanoparticles and Young’s modulus increased with the increment in the alumina nanofiller dispersion level. The presence of aggregated particles exhibits a negative effect on the tensile properties of nanocomposites.
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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 | 0.000 | 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".