Six-Month Long <i>In Vitro</i> Degradation Tests of Biodegradable Twinning-Induced Plasticity Steels Alloyed with Ag for Stent Applications
Bibliographic record
Abstract
Twinning-induced plasticity (TWIP) Fe-Mn-C steels are biodegradable metals with far superior mechanical properties to any biodegradable metal, including Mg alloys, used in commercially available devices. For this reason, the use of Fe-Mn-C alloys to produce thinner and thinner implants can be exploited for overcoming the device size limitations that biodegradable stents still present. However, Fe-Mn steels are known to form a phosphate layer on their surface over long implantation times in animals, preventing device degradation in the required timeframe. The introduction of second phases in such alloys to promote galvanic coupling showed a short-term promise, and particularly the use of Ag looked especially effective. Nonetheless, the evolution of the corrosion mechanism of quaternary Fe-Mn-C-Ag alloys over time is still unknown. This study aims at understanding how corrosion changes over time for a TWIP steel alloyed with Ag using a simple static immersion setup. The presence of Ag promoted some galvanic coupling just in the first week of immersion; this effect was then suppressed by the formation of a mixed carbonate/hydroxide layer. This layer partly detached after 2 months and was replaced by a stable phosphate layer, over which a new carbonate/hydroxide formed after 4 months, effectively hindering the sample degradation. Attachment of phosphates to the surface matches 1-year outcomes from animal tests reported by other authors, but this phenomenon cannot be predicted using immersion up to 28 days. These results demonstrate that immersion tests of Fe-based degradable alloys can be related to animal tests only when they are carried out for a sufficiently long time and that galvanic coupling with Ag is not a viable strategy in the long term. Future works should focus more on surface modifications to control the interfacial behavior rather than alloying in the bulk.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".