Evaluation of the Adhesion of Ultra-Thin Teflon-Like Films Deposited by Plasma on 316L Stainless Steel for Long-Term Stable Drug-Eluting Stents
Bibliographic record
Abstract
Metallic intravascular stents are medical devices commonly made of 316L stainless steel or nitinol used to scaffold a biological lumen, most often diseased arteries, after balloon angioplasty. Stenting procedures reduce the risk of restenosis, but do not eliminate it completely. Indeed, restenosis remains the principal cause of clinical complications, leading to up to 30 % of failure after 3 months of implantation. During the last few years, several works have been focused on the development of an appropriate coating able to act as a carrier for specific anti-restenosis drugs. Moreover, this coating would act as an anti-corrosive barrier, thus inhibiting the release of potentially toxic ions. Actually, the main challenges in stent coatings are to synthesize a biocompatible polymer coating resistant to blood flow, wall shear stress and tensile force after the stent deployment which results in a permanent strain of up to 25%. The adhesion and chemical resistance after deployment are critical properties to investigate for the improvement of the long-term reliability of polymer coated stent. The aim of this study was to evaluate the effect of a 25% equivalent plastic deformation on chemical, mechanical and adhesion properties of Teflon-like films deposited on 316L stainless steel. These properties were studied by chemical spectroscopy and atomic force microscopy. Teflon-like films were deposited by pulsed plasma glow discharges on flat electropolished 316L stainless steel. An original method has been developed to induce the deformation, and preliminary results have showed that the 12 nm thick Teflon-like films successfully resist to deformations of up to 25%.
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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".