Hybrid Carbon–Silver Nanofillers for Composite Coatings with Near Metallic Electrical Conductivity
Bibliographic record
Abstract
Polymeric composite materials are now very well established in all areas of engineering and are still increasingly being used to replace metallic counterparts. As an important advantage, composite materials hold the promise of multifunctionality, that is, fine tuning the material composition to synthetically achieve specific requirements. Among these requirements, the electrical conductivity is still limited to values orders of magnitude below those of typical metals. An approach to conductive fillers, which involves taking advantage of the favorable properties of both carbonaceous and metallic fillers to provide near metallic conductivity to the surface of carbon fiber reinforced polymer composites is herein presented by the authors. The synthesis of hybrid core‐shell high aspect ratio carbon–silver nanoparticles with a continuous silver coating, which allows both a low contact resistance between individual fillers and a greater connectivity owing to the wire‐like morphology of the particles is achieved by the authors. Incorporating the nanoparticles in an epoxy matrix yields a conductivity of 2.5 × 105 S m−1 at a loading of 6.3 vol% for a corresponding material density is of 1.9 g cm−3. The conductivity reached is high enough even to divert emulated lightning strike energy.
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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".