Dielectric response of high density polyethylene/SiO<inf>2</inf> composites
Bibliographic record
Abstract
The objective of this research is to measure and quantitatively model the complex permittivity of PE composites. Dielectric mixing laws are helpful in designing and modeling composites. However, simple algebraic mixing laws do not accurately predict the effective permittivity of multiphase composites. Popular algebraic mixing laws are based on volume fractions and complex permittivities of both phases only. Thus, they do not distinguish between material types with different microstructures. Most of the different two-phase mixing laws inaccurately predict the measured complex permittivities of HDPE composites. Nanocomposites have been prepared by melt compounding using a twin screw extruder. The imaginary part of the complex permittivity of the three-phase composites depends significantly on the presence of a water layer at the surface of the nanoparticles. In this paper, a comparison between the analytical solution for heterogeneous material with an interfacial layer between filler particles and the PE matrix and the experimental results is presented for various PE/SiO2nanocomposites. The experimental results were obtained from frequency domain dielectric spectroscopy in the 10-1Hz to 105Hz frequency range.
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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".