Development of Reinforced Polyester/Graphene Nanocomposite Showing Tailored Electrical Conductivity
Bibliographic record
Abstract
Production process was chosen in order to be readily scalable at the industrial level. The resin/graphene mixture was prepared through high shear mixing at six different weight concentrations between 0% and 10%. Samples were subsequently produced by compression molding. The electrical properties were measured both in-the-plane and across-the-plane using, respectively, a four-point probe and a two-electrode system. The two-electrode system was a dielectric spectrometer, and accordingly, the across-the-plane measurements were conducted in the frequency-domain. Mechanical measurements were conducted using conventional three-point bending and impact setups. The percolation threshold was found to be in the range of 3-5 wt.% concentration, for which the conductivity showed a 7 orders of magnitude increase. These results were quite similar to the samples containing around 50 wt.% of glass fibers. Surprisingly, the in-the-plane conductivity was found to be lower than the bulk conductivity, contrary to what was found with the same filler for thermoplastic composites prepared by melt compounding. No significant increase in mechanical properties as a function of filler loading was observed, except maybe a slight increase in the material toughness.
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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".