Nano-Thick Amorphous Oxide Layer Produced by Plasma on Type 316L Stainless Steel for Improved Corrosion Resistance Under Plastic Deformation
Bibliographic record
Abstract
Localized corrosion constitutes a major concern in medical devices made of stainless steel. The conventional approach to circumvent such a problem is to convert the surface polycrystalline microstructure of the native oxide layer to an amorphous oxide layer, a few micrometers thick. This process cannot, however, be used for devices such as stents that undergo plastic deformation during their implantation, especially those used in vascular surgery for the treatment of cardiac, neurological, and peripheral vessels. This work explores the feasibility of producing a nano-thick plastic-deformation resistant amorphous oxide layer by plasma-based surface modifications. By varying the plasma process parameters, oxide layers with different features were produced and their properties were investigated before and after clinically-relevant plastic deformation. These properties and the related corrosion mechanisms were mainly evaluated using the electrochemical methods of open-circuit potential, cyclic potentiodynamic polarization, and electrochemical impedance spectroscopy. Results showed that, under optimal conditions, the resistance to corrosion and to the permeation of ions in a phosphate buffered saline, even after deformation, was significantly enhanced.
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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) | 0.000 | 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".