Corrosion, Wear, and Antibacterial Behaviors of Hydroxyapatite/MgO Composite PEO Coatings on AZ31 Mg Alloy by Incorporation of TiO2 Nanoparticles
Bibliographic record
Abstract
Plasma electrolytic oxidation (PEO) is a promising surface treatment for generating a thick, adherent coating on valve metals using an environmentally friendly alkaline electrolyte. In this study, the PEO method was used to modify the surface of AZ31 Mg alloy. The composite coatings were formed in a phosphate-based electrolyte containing hydroxyapatite nanoparticles (NPs) and different concentrations (1, 2, 3, and 4 g/L) of TiO2 NPs. The results showed that the incorporation of TiO2 NPs in the composite coatings increased the porosity, coating thickness, surface roughness, and surface wettability of the coatings. The corrosion-resistance results of coatings in simulated body fluid (SBF) were tested for up to 72 h and all coatings showed superior corrosion resistance compared to the bare substrate. Among samples containing TiO2, the sample containing 1 g/L TiO2 had the highest inner layer resistance (0.51 kΩ·cm2) and outer resistance (285 kΩ·cm2) and the lowest average friction coefficient (395.5), so it had the best wear and corrosion resistance performance. The antibacterial tests showed that the higher the concentration of TiO2 NPs, the lower the adhesion of bacteria, resulting in enhanced antibacterial properties against S. aureus. The addition of 4 g/L of TiO2 NPs to the electrolyte provided an antibacterial rate of 97.65% for the 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) | 0.000 | 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".