Plasma-immersion ion implantation surface oxidation on a cobalt-chromium alloy for biomedical applications
Bibliographic record
Abstract
Co-Cr alloys such as L605 are widely applied for the manufacture of medical devices, including tiny cardiovascular stents. The presence of potentially toxic and allergenic release of Ni, Co, and Cr ions from these devices remains an unsolved concern. Surface modification by oxygen plasma immersion implantation (PIII) could be an excellent technique to create a dense and thin passive oxide layer on a relatively complex shape of a tiny device, such as a stent, thus reducing the potential release of metallic ions. The effect of oxygen PIII was investigated on L605 alloy specimens, from 5 to 50 mTorr gas pressures, and under pulsed bias voltages from -0.1 to -10 kV. The surface chemistry was investigated by x-ray photoelectron spectroscopy, while its morphology and surface energy were evaluated, respectively, by atomic force microscopy and scanning electron microscopy and by a sessile drop static contact angle. Electrochemical characterization was performed by potentiodynamic tests in the saline solution. Mechanical properties of the modified surface layer, specifically film adhesion and hardness (H), were assessed by scratch and nanoindentation tests. Results shown that the oxidized layers were composed of a mixture of Co and Cr oxides and hydroxides and were rich in Co. The corrosion rate was considerably reduced after O PIII, even for treatments using low bias voltage (-0.1 kV) and with consequent low oxygen implantation depth. Moreover, O PIII also improved surface hardness. The oxidized layers were found to have good adhesion and to be scratch resistant.
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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".