Plasma Immersion Ion Implantation of a Fe-Mn-C Based Steel for Biomedical Applications: Effect of Gases and Treatment Times on the Surface Properties
Bibliographic record
Abstract
Current research on biodegradable iron-based alloys mainly focuses at regulating the material degradation rate, as well as its biological behavior, especially from the point of view of the hemocompatibility and cytocompatibility. In fact, fine-tuning of the surface roughness, morphology and chemical composition can improve the functional response of the material. For that purpose, a surface modification strategy, namely plasma immersion ion implantation (PIII), is proposed to perform the selective modification of surface properties without affecting the bulk ones. In this work, the influence of treatment time (timp = 15, 60 and 120 min.) and implanted species (O, N or C) on the surface properties of a Fe-13Mn-1.2C resorbable alloy was investigated. The findings demonstrated that varying the process gas and the exposition time led to a variety of topographies, surface energies and chemical compositions. XPS analyses and depth profiles clearly showed the impact of the process parameters on the surface features and element distribution, due to implanted species penetration into the alloy. The implanted samples showed a delayed clotting time, thus a better hemocompatibility. In contrast, nitrogen-treated surfaces displayed a more pronounced hemolytic behavior, whereas oxygen and methane did not. PIII implantation appears to be a versatile solution for fine-tuning surface topography, composition and biological properties, making the process promising for the improvement of metallic biodegradable vascular implants.
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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".