Manganese-enhanced strength and corrosion resistance of extruded Mg-0.7Ca alloys for biodegradable orthopedic implants
Bibliographic record
Abstract
Achieving an optimal balance between strength and corrosion resistance remains a significant challenge in the development of biodegradable Mg-Ca alloys for orthopedic applications. The present study investigates the influence of Mn additions at 0.35 and 0.83 wt% on the microstructure, mechanical performance, and corrosion resistance of extruded Mg-0.7Ca alloys. The Mn addition caused substantial grain refinement, significantly reducing the grain size. A moderate Mn addition of 0.35 wt% promoted a fully recrystallized, equiaxed grain structure, whereas 0.83 wt% retained unrecrystallized regions with high local misorientation. Mechanical strength increased with Mn, with peak ductility achieved at 0.35 wt% before declining at higher Mn due to strain accumulation. Immersion and electrochemical tests demonstrated that corrosion resistance improved at 0.35 wt% Mn, supported by its highest charge transfer resistance, but slightly reduced at 0.83 wt% Mn in simulated body fluid (SBF). Time-of-flight secondary ion mass spectrometry analysis revealed Cl⁻ aggregations on the Mn-free and 0.83 wt% Mn alloys, which were not seen on the 0.35 wt% Mn alloy, implying reduced chloride interaction. These findings demonstrate that targeted Mn alloying enables simultaneous enhancement of strength and corrosion resistance, with 0.35 wt% Mn offering an optimal balance option for biodegradable orthopedic implants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".