Effect of powder preparation on degradation behavior and cytotoxicity of sintered porous biodegradable FeMnC alloys for biomedical applications
Bibliographic record
Abstract
• The mixture of a mechanically-milled prealloyed and blended FeMnC powders led to enhanced degradation rates of porous FeMnC sintered alloys;. • Increasing the proportion of the prealloyed powder reduced Mn evaporation, resulting in the formation of up to 60 wt.% of austenite in samples sintered from a mixture containing 75 wt.% of this powder;. • A complex microstructure, consisting of ferrite, austenite, and martensite, was observed in samples sintered from mixtures containing up to 50 wt.% prealloyed milled powder;. • All sintered samples demonstrated interesting cell viability exceeding 80 % at 1 % extract dilution after 24 h of cell viability test, and were non-hemolytic, with a hemolysis <1 %. Biodegradable implants have emerged in biomedical applications, particularly for orthopedic fixations, cardiovascular stents, and tissue engineering scaffolds. Unlike permanent implants, they are designed to degrade and be reabsorbed after implantation in the body, mitigating the need for additional surgeries and reducing associated complications. In particular, Fe-Mn-C alloys constitute a new class of promising metallic materials for medical applications due to their outstanding mechanical properties and their biological performances. This study focuses on improving the degradation rates and cytotoxicity of sintered Fe-Mn-C alloys produced using the powder metallurgy process. To evaluate the impact of different powder preparation methods on material properties, two types of powders were used: (1) MX, prepared by mixing Fe, Mn, and C powders for 1 h; and (2) MM, obtained by mechanically milling the same powders for 10 h. Four mixtures with varying proportions of MX and MM were prepared. Two groups of samples were produced: one entirely from MX (A0), and another containing MM at 25 wt.% (A25), 50 wt.% (A50), and 75 wt.% (A75). All samples exhibited a complex microstructure comprising ferrite, martensite, and residual austenite. Degradation behavior assessment in Hanks’ solution over 14 days showed that adding MM increased the degradation rate, from around 0.04 mmpy for A0 to 0.12 mmpy for A25. Notably, all samples showed similar cell viability, in the range of 83 %–89 % for 1 % extract dilution, and were non-hemolytic, with a hemolysis percentage below 1 %.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".