Conductive, Anti-Corrosion, Self-Healing Smart Coating Technology Incorporating Graphene-Based Nanocomposite Matrix
Bibliographic record
Abstract
Chromate conversion coatings have been in service for decades providing robust corrosion protection to a wide variety of aluminum alloys. However, it is also known that anti-corrosive coatings containing Cr6+ contributes to DNA damage, cause cancer and are not environmentally friendly. Consequently, regulatory restrictions over the use Cr6+ were established to mitigate the environmental damage and health problems. To answer to this hurdle and to meet the emergent need for environmentally friendly anti-corrosive coatings, we have successfully developed an innovative coating that combines anti-corrosive, low electrical resistance, and self-healing properties. First, we present two different coatings, that aim to display low electrical resistance properties: one containing only graphene and the other containing Zn nanoparticles and graphene. Confocal laser imaging and SEM microscopy was used to observe the morphology of the coatings. The electrical resistance was measured using the 4-wire connection Kelvin method. We compare the anticorrosive response for both coatings under neutral salt spray test (NSSt). Raman spectroscopy was performed before and after to understand the effect of NSSt corrosive species on the coatings. Then, we select the coating with lower electrical resistance, and we program on it a self-healing mechanism to boost its life service. Finally cyclic voltammetry is performed to confirm the excellent blocking properties of the tested coatings. All the coatings presented in this work are applied on aluminum AA 2024T351 and the optimal spray parameters for nanofillers dispersion are obtained. Our findings show great potential for preventing corrosion and compatibility with fully automated large-scale applications in different fields such as aerospace, automotive, construction, submarines and many more.
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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".