On the Interface between Plasma Fluorocarbon Films and 316L Stainless Steel Substrates for Advanced Coated Stents
Bibliographic record
Abstract
As intravascular biomedical devices, metallic stents are particularly susceptible to corrosion induced by the physiological environment, causing the degradation of mechanical properties and leading to the release of toxic and carcinogenic ions from the SS316L bulk. Therefore, several works have been focused on the development of an ultra-thin fluorocarbon coating that could act both as a drug-carrier for in-stent restenosis and as an anti-corrosion barrier. However, the increase of the corrosion performance was limited by the inevitable permeability of the coating, which exposed some of the sensitive interfacial region to the corrosive environment. Indeed, in previous works, adhesion and growth rate of the film were promoted by the removal of the native oxide layer of the stainless steel which is inhomogeneous, brittle and mechanically unstable. Further refinements of the interface are therefore required in order to enhance the overall corrosion performance without compromising the fluorocarbon film properties and adhesion. Hence, the aim of this work was to enhance the corrosion behaviour of coated SS316L by the creation of a controlled interfacial oxide layer. The native oxide layer was first removed under vacuum and the bare metal surface was subjected to a plasma-reoxidation treatment. Tafel measurements were used to assess the corrosion rates of the specimens. Coated and uncoated modified interfaces were also characterized by X-Ray Photoelectron Spectroscopy (XPS) and Atomic Force Microscopy (AFM).
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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".