Electrical and dielectric properties of poly(vinyl alcohol)/starch/graphene nanocomposites
Bibliographic record
Abstract
Electrical and dielectric properties of poly(vinyl alcohol) (PVA) films, and PVA/starch blend and its nanocomposites with graphene were investigated. The tested materials were prepared via solution mixing and an evaporative casting technique using glycerol as a plasticizer. Differential scanning calorimetric (DSC) measurement data was used to calculate the percentage of crystallinity and glass transition temperature ( ). Distribution of starch and graphene in the PVA matrix was determined from field emission scanning electron microscopy (FESEM). Effects of the plasticizer and graphene loading on the DC and AC electrical conductivities of the PVA/starch blend were studied. The impact of graphene loadings on the dielectric permittivity (ϵ′), dielectric loss tangent (tan δ), complex electric modulus (M*), and complex impedance (Z*) as a function of frequency were reported. The DC conductivity of PVA was increased with the addition of glycerol and starch. The permittivity of PVA films and PVA/starch/graphene nanocomposites showed a strong frequency‐dependent behaviour in a low frequency zone. The addition of graphene to the PVA/starch blend reduced the area under the semicircles of the Nyquist plot.
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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".