PEO Ceramic Coating on Mg-10%wt. Zn for Potential Biological Application
Bibliographic record
Abstract
Magnesium-zinc (Mg-Zn) alloys are strong candidates as medical implant materials due to their good biocompatibility and relatively high strengths. To manipulate the degradation of Mg-Zn alloy in the human body, a plasma electrolytic oxidation (PEO) treatment was applied to Mg-10%wt. Zn in this study because it produces a coating that is non-harmful to the human body and the process is inexpensive and environmentally friendly. Potentiodynamic polarization corrosion tests, performed in a simulated body fluid (Hanks’ Balanced Salt Solution) were applied to the coated and uncoated Mg-Zn samples. The results of the testing showed that the coated Mg-Zn exhibited higher corrosion resistance than the substrate. With the PEO coating thickness of 7.2 microns, the corrosion current density was reduced by 1.00 μA/cm2 from the uncoated Mg-Zn respectively, indicating a significant reduction in the degradation rate between pure Mg-Zn and coated Mg-Zn from 7.0 to 3.7 kg/year. A pin-on-disc tribometer was employed to measure the coefficient of friction (COF) for the coated and uncoated Mg-Zn samples, lubricated with and without Hanks’ solution. The measured COF of the coated sample was very low (averaging to be about 0.22 under the lubricated condition) and comparable to that of the substrate which exhibited an averaged COF of 0.13 under the lubricated condition.
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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".