Dielectric properties of exfoliated graphite reinforced flouroelastomer composites
Bibliographic record
Abstract
Abstract Dielectric relaxation behavior of nano graphite reinforced flouroelastomer composites has been studied as a function of variation in filler in the frequency range of 0.01–105Hz. The effect of variation in filler loadings on the complex and real parts of impedance was distinctly visible which has been explained on the basis of interfacial polarization of fillers in a heterogeneous medium and relaxation dynamics of polymer chains in the vicinity of fillers. The electric modulus formalism has been utilized to further investigate the conductivity and relaxation phenomenon. The frequency dependence of AC conductivity has been investigated by using Percolation theory. The phenomenon of percolation in the composites has been discussed based on the measured changes in electric conductivity and morphology of composites at different concentrations of the filler. The percolation threshold as studied by DC conductivity occurred in the vicinity of 2.5–3.5 phr of filler loading. Scanning electron microscope microphotographs showed agglomeration of the filler above this concentration and formation of a continuous network structure. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2009
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".