Electrodeposition of FeW-graphene composites: Effect of graphene oxide concentration on microstructure, hardness and corrosion properties
Bibliographic record
Abstract
Graphene has emerged as excellent reinforcement for electrodeposited metallic composites. The poor stability of graphene in electrochemical baths makes it challenging to obtain uniform composite coatings. In this work, we investigate the possibility to electrodeposit FeW-graphene coatings with organic stablizers. Polydiallyldimethylammonium chloride is selected to stabilize the graphene oxide which is added into the electrolyte in various concentrations. Scanning electron microscopy and Raman analysis confirmed the successful co-deposition of graphene in all the coatings. The composition of the FeW matrix remained unaffected by the addition of graphene, while an increase in the crystallinity of the structure of the composites was observed. Graphene was retained even after the coatings were heat treated at 400 °C for 1 hour. The hardness and the corrosion resistance of the FeW-graphene composite were largely improved: a 22% increase in hardness and an 80% increase in corrosion resistance were measured compared to the graphene-free coating.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".