Microstructure and bio‐corrosion behavior of Mg–Zn and Mg–Zn–Ca alloys for biomedical applications
Bibliographic record
Abstract
The microstructure and bio‐corrosion behavior of binary Mg–xZn (x = 1.25, 2.5, 4) and ternary Mg–Ca–xZn (x = 1.25, 2.5, 4) alloys have been studied using scanning electron microscopy (SEM), electrochemical, and immersion tests. Microstructure analysis indicated that the binary Mg–Zn alloys are composed of primary α‐Mg matrix and Mg12Zn13 phases, while, ternary Mg–Ca–Zn alloys are composed of α‐Mg, Mg2Ca, and IM1 (Ca3MgxZn15−x) (4.6 ≤ x ≤ 12) phases or α‐Mg, IM1 and IM3 (Ca2Mg5Zn13) phases. Electrochemical results showed that Mg–4Zn alloy has lowest corrosion rate among binary alloys. At constant Ca content of 0.8 wt.%, the addition of Zn up to 1.25 wt.% decreased the corrosion rate, while further addition of Zn increased the corrosion rate of ternary alloys. Immersion tests results demonstrated that the formation of Zn oxide layer in binary Mg–Zn alloy and evolution of eutectic phase (α‐Mg + IM1 + Mg2Ca) significantly retard the bio‐degradation rate of the ternary alloys.
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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".