Degradation Behaviour of Metallic Biomaterials for Degradable Stents
Bibliographic record
Abstract
The short-term need of scaffolding function of stent and the prevention of potential longterm complication of permanently implanted stent have directed to the original idea of biodegradable stent. Selecting and developing materials showing appropriate mechanical and degradation properties are key steps for the development of this new class of medical devices. Therefore, the study of their in vitro degradation behaviour is mandatory for the selection of potential candidate materials suited in vivo. In this work, the degradation behaviour of current studied biodegradable metals including three magnesium alloys (Mg, AM60B and AZ91D), pure iron and Fe-35Mn was investigated. The tests were performed in a simulated blood plasma solution at 37±0.1 oC, using three different methods; potentiodynamic polarization, static immersion, and dynamic test in a test-bench which mimics the flow condition in human coronary artery. Degradation rate was determined as ion release rate measured by using atomic adsorption spectroscopy (AAS) and also estimated from weight loss and corrosion current. Surface morphology and chemical composition of corroded specimens were analyzed by using SEM/EDS. The three degradation methods provide consistent results in corrosion tendency, where Mg showed the highest corrosion rate followed by AZ91D, AM60B, Fe-35Mn and iron. Potentiodynamic polarization gives a rapid estimation of corrosion behaviour and rate. Static immersion test shows the effect of time on the degradation rate and behaviour. Dynamic test provides the closest approach to the environment after stent implantation and its results show the effect of the flow on the materials degradation. In conclusion, the three investigated methods can be applied for screening, selecting and validating materials for degradable stent application before going further to in vivo assessments.
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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".