Evaluation of the Corrosion Protection of Ultra-Thin Plasma Fluorocarbon Film Deposited on 316L Stainless Steel for Long-Term Stable Stents
Bibliographic record
Abstract
Commonly made of 316L stainless steel and nitinol, metallic intravascular stents are medical devices used to scaffold a biological lumen, most often diseased arteries. While stenting procedures reduce the risk of restenosis, they do not eliminate it completely. Furthermore, other common complications observed are thrombosis, inflammation and corrosion of the stents. The corrosion of the device is induced by blood flow which provokes a degradation of its mechanical properties and leads to a high risk of release of potentially toxic metallic compounds, such as nickel-based oxides and metal ions. To lower these clinical complication rates and to prevent the corrosion of the metallic stent structure, coated stents have been developed during the last decade. Indeed, the coating is expected to improve the surface biocompatibility and corrosion resistance without compromising the stainless steel mechanical properties required for the stent implantation. The Food and Drug Administration (FDA) has already provided guidance on a series of non-clinical test protocols, methods and reports to evaluate the safety and effectiveness of intravascular stents. Properties such as the stability, durability, and adhesion of a stent coating, prior and after deployment, must be clearly assessed to demonstrate its efficiency. This study wants to evaluate the effectiveness against general and local corrosion of an ultra-thin fluorocarbon film deposited by plasma on pre-treated stainless steel. Cyclic polarization tests were used to measure the coating capacity to protect the substrate from localized corrosion and Tafel plot corrosion measurements were used to evaluate the general corrosion behaviour of uncoated and coated, flat and deformed samples.
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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".