Investigation on the Mechanical and Corrosion Properties of ZnMnSr Alloys for Biodegradable Orthopedic Implants
Bibliographic record
Abstract
Zinc‐based alloys with good biocompatibility and corrosion resistance are potential candidates for bioabsorbable implants. Herein, a series of Zn99−x Mn x Sr1 (x = 0.2, 0.3, 0.5, and 0.9 wt%) alloys are investigated using single‐channel vertical extrusion (SCVE) and equal‐channel angular pressing (ECAP) procedures, respectively. It is found that the amount of Mn significantly affects the mechanical properties of alloys. For example, the yield strength, tensile strength, and elongation of Zn–1Sr alloys can reach up to 204.73 ± 3.6 MPa, 244.04 ± 6.45 MPa, and 47.1 ± 3% with 0.99 wt% Mn addition after the SCVE process, which can meet the requirement of orthopedic implant applications. Moreover, the mechanical properties of current alloys are reduced with the combined SCVE + ECAP process, which may be due to more aggregation with the secondary phase within the matrix hcp(Zn). Furthermore, the polarization curves and immersion tests show that the corrosion rate is accelerated for the alloys processed with the combined SCVE + ECAP process (0.34–0.171 mm/y) compared with the ones processed only with SCVE (0.011–0.032 mm/y). The present finds provide a contribution for biodegradable Zn‐based alloys development.
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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".