Insight into Microstructure Evolution and Corrosion Mechanisms of K2ZrF6/Al2O3-Doped Hot-Dip Aluminum/Micro-Arc Oxidation Coatings
Bibliographic record
Abstract
In this study, we investigated the impact of K2ZrF6/Al2O3 composite additives on the microstructure evolution and corrosion behavior of ceramic coatings formed through micro-arc oxidation (MAO) treatment on hot-dip aluminum-coated 316L stainless steel surfaces. Our findings revealed the successful preparation of micro-arc oxidation ceramic coatings, presenting a dual-layer structure consisting of a porous micro-arc oxidation ceramic outer layer and a relatively dense/thick hot-dip aluminum inner layer. The incorporation of K2ZrF6/Al2O3 composite additives induced a self-sealing effect on the ceramic coating surface. Optimal coating performance was achieved with a composite additive concentration of 7.5 g/L, resulting in remarkable improvements not only in thickness, hardness, and surface smoothness but also in corrosion resistance. This research introduces a pioneering investigation of K2ZrF6/Al2O3 composite additives in the context of micro-arc oxidation technology, offering fresh perspectives and methodologies for the development of highly corrosion-resistant materials.
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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) | 0.000 | 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 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".