Influence of the 316 L Stainless Steel Interface on the Stability and Barrier Properties of Plasma Fluorocarbon Films
Bibliographic record
Abstract
Coatings are known to be one of the more suited strategies to tailor the interface between medical devices and the surrounding cells and tissues once implanted. The development of coatings and the optimization of their adhesion and stability are of major importance. In this work, the influence of plasma etching of the substrate on a plasma fluorocarbon ultrathin coating has been investigated with the aim of improving the stability and the corrosion properties of coated medical devices. The 316 L stainless steel interface was subjected to two different etching sequences prior to the plasma deposition. These plasma etchings, with H(2) and C(2)F(6) as gas precursors, modified the chemical composition and the thickness of the oxide layer and influenced the subsequent polymerization. The coating properties were evaluated using flat substrates submitted to deformation, aging into aqueous medium and corrosion tests. X-ray photoelectron spectroscopy (XPS), time of flight-secondary ion mass spectrometry (ToF-SIMS), ellipsometry, and atomic force microscopy (AFM) were performed to determine the effects of the deformation and the aging on the chemistry and morphology of the coated samples. Analyses showed that plasma etchings were essential to promote reproducible polymerization and film growth. However, the oxide layer thinning due to the etching lowered the corrosion resistance of the substrate and affected the stability of the interface. Still, the deformed samples did not exhibited adhesion and cohesion failure before and after the aging.
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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 | 0.001 |
| 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".