Effect of silver in thermal treatments of Fe-Mn-C degradable metals: Implications for stent processing
Bibliographic record
Abstract
Twinning-induced plasticity (TWIP) steels are considered excellent materials for manufacturing products requiring extremely high mechanical properties for various applications including thin medical devices, such as biodegradable intravascular stents. It is also proven that the addition of Ag can guarantee an appropriate degradation while implanted in human body without affecting its bioactive properties. In order to develop an optimized manufacturing process for thin stents, the effect of Ag on the recrystallization behavior of TWIP steels needs to be elucidated. This is of major importance since manufacturing stents involves several intermediate recrystallization annealing treatments. In this work, the recrystallization mechanism of two Fe-Mn-C steels with and without Ag was thoroughly investigated by microstructural and mechanical analyses. It was observed that Ag promoted a finer microstructure with a different texture evolution, while the recrystallization kinetics resulted unaffected. The presence of Ag also reduced the effectiveness of the recrystallization treatment. This behavior was attributed to the presence of Ag-rich second phase particles, precipitation of carbides and to the preferential development of grains possessing a {111} orientation upon thermal treatment. The prominence of {111} grains can also give rise to premature twinning, explaining the role of Ag in reducing the ductility of TWIP steels already observed in other works. Furthermore, in vitro biological performances were unaffected by Ag. These findings could allow the design of efficient treatments for supporting the transformation of Fe-Mn-C steels alloyed with Ag into commercial products.
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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".