Influence of particle arrangement on the permittivity of an elastomeric composite
Bibliographic record
Abstract
Elastomers are used as dielectric layers contained between the parallel conductive plates of capacitors. The introduction of filler particles into an elastomer changes its permittivity ε. When particle organization in a composite is intentionally varied, this alters its capacitance. Using numerical simulations, we examine how conductive particle chains introduced into polydimethylsiloxane (PDMS) alter ε. The effects of filler volume fraction ψ, interparticle d and interchain spacing a, zigzag angle θ between adjacent particles and overall chain orientation, particle size r, and clearance h between particles and the conductive plates are characterized. When filler particles are organized into chainlike structures rather than being just randomly distributed in the elastomer matrix, ε increases by as much as 85%. When particles are organized into chainlike forms, ε increases with increasing ψ and a, but decreases with increasing d and θ. A composite containing smaller particles has a higher ε when ψ<9% while larger particles provide greater enhancement when ψ is larger than that value. To enhance ε, adjacent particles must be interconnected and the overall chain direction should be oriented perpendicular to the conductive plates. These results are useful for additive manufacturing on electrical applications of elastomeric composites.
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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".