Electrically Conductive Silver Nanoparticles‐Filled Nanocomposite Materials as Surface Coatings of Composite Structures
Bibliographic record
Abstract
Silver nanoparticles‐filled nanocomposite materials are fabricated and used as electrically conductive coatings for aerospace composite structures. Silver nanoparticles are first synthesized, then mixed with polymer solutions, and applied onto the surface of carbon fiber composite substrates either by casting or spraying technique. Two design strategies are studied: the reduction of nanofillers contact resistance using a conductive polymer as binder and the addition of a second polymer to fabricate a ternary biphasic nanocomposite for the further improvement of mechanical resistance of the coatings. The coatings are then annealed at different temperatures up to 200 °C and characterized using various techniques in order to evaluate their morphology, electrical resistivity, and mechanical performance. The thermal annealing considerably improves the adhesion of the coatings to the composite substrates as well as the scratch resistance of the coatings. A significant improvement (approx. six orders) of electrical properties is achieved for a ≈10 μm‐thick coating film after the thermal annealing. The best results are achieved with silver nanoparticles mixed with the conductive polymer binder with the maximum resistivity of 4.2 × 10−3 Ω g cm−2. The conductivity achieved here is fairly close to that values required in order for the materials to be used for coating of composite structures in potential aerospace applications such as electromagnetic interference shielding and lighting strike protection.
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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".