Properties of a carbon filled cyclic olefin copolymer
Bibliographic record
Abstract
Abstract This article investigates electrical conductivity and rheological aspects of cyclic olefin copolymer (COC) composites containing both carbon fiber (CF) and carbon black (CB) at various concentrations. The different formulations of carbon filled COC were compression molded in such a manner that the formed circular sheets exhibited preferred in‐plane filler orientation. Through‐plane and in‐plane conductivity were measured by 2‐probe and 4‐probe methods, respectively, while an ARES rheometer in dynamic mode was employed to measure the storage modulus and complex viscosity. It was found that formulations with CF:CB ratios around 3 and where the CB content was close or below its critical percolation concentration resulted in higher electrical conductivity while maintaining the viscosity of the composite at a level acceptable for polymer processing machinery. For those composites containing both fillers, collaborative associations between the CB and CF fillers were found in the established percolating network structure, producing measured conductivities which exceeded the estimated values by the additive rule by up to sixfold. An empirical expression to handle hybrid filler systems is proposed in this work based on the standard percolation model. © 2007 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 45: 1808–1820, 2007
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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".