Surface modification and direct plasma amination of L605 CoCr alloys: on the optimization of the oxide layer for application in cardiovascular implants
Bibliographic record
Abstract
Stents are cardiovascular devices used to treat atherosclerosis, and are deployed into narrowed arteries and implanted by expansion to reopen the biological lumen. Nevertheless, complications after implantation are still observed in 10-14% of the implantations. Therefore, functionalizing these devices with active molecules to improve the interfacial effects with the surrounding tissue strongly impacts their success. A plasma-based procedure to directly graft biomolecules to the surface of cobalt chromium alloys, without any polymeric coating, has been recently reported. Assuring the stability of the coating during plastic deformation generated during the implantation whilst avoiding the corrosion of the surface is crucial. This study explores different surface treatments to be used as a pre-treatment for this novel procedure. The effects of (i) electropolishing, (ii) thermal treatments, and (iii) the plasma immersion ion implantation of oxygen on the chemical composition, roughness, wettability and efficiency during the plasma-amination procedure whilst avoiding cracks after deformation, thus maintaining corrosion resistant behaviour, were investigated by XPS, AFM, ToF-SIMS imaging and depth profile, and WCA. Furthermore, the hemocompatibility of the surface and cell viability assays were also performed. Results showed that all of the treatments created a different surface chemical composition: EP mainly of chromium oxide, PIII with a layer of cobalt oxide and TT with a mixture of oxides, as observed by XPS and ToF-SIMS. Moreover, EP was the process that generated a surface with the highest efficiency to amination and the most corrosion resistance among the treatments, and it appeared as the most suitable pre-treatment for stent functionalization.
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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".