Influence of Micro-arc Oxidation Coatings on Corrosion Performances of AZ80 cast alloy
Bibliographic record
Abstract
To enhance the corrosion performance of cast AZ80 alloy, micro-arc oxidation (MAO) coating was synthesized at various processing current densities in a basic silicate-fluoride solution. The microstructure, composition, corrosion performance and Mott-Schottky characteristics of MAO coatings at different processing current densities were investigated using various microscopic characterization and electrochemical methods. It was found that a thinner (5.04 μm) MAO coating produced at the low processing current density obtained a more uniform and smaller discharge pores morphology and higher fluoride content compared to the other coatings produced at higher current densities. The open-circuit potential, corrosion current density and polarization resistance values of this coating were -1.28 V vs. Ag/AgCl electrode, 0.00589 μA/cm 2 and 1.53×10 6 Ω∙cm 2 in 3.5 wt.% NaCl, respectively. After the Mott-Schottky test, the analysis of the coating showed that the uncoated and MAO coated AZ80 alloy exhibited p-type semiconductor characteristics. For the MAO coated specimens, the coating synthesized at the lower applied current density showed lower acceptor concentration and highly negative flat band potential. These features are associated with the reduced reactivity and improved corrosion resistance of this new MAO coating.
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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".