Surface Modification of an Absorbable Bimodal Fe-Mn-Ag Alloy by Nitrogen Plasma Immersion Ion Implantation
Bibliographic record
Abstract
Recently, Fe-Mn-based alloys have been increasingly catching the attention of the scientific community, because of their tunable and outstanding mechanical properties, and suitable degradation behavior for biomedical applications. In spite of these assets, their corrosion rate (CR) is, in general, too low to satisfy the requirements that need to be met for cardiovascular device applications, such as stents. In fact, the CR is not always the same for all of the degradation stages of the material, and in addition, a finely tuned release rate, especially during the first steps of the corrosion pattern, is often demanded. In this work, a resorbable bimodal multi-phase alloy Fe-3Mn-1Ag was designed by mechanical alloying and spark plasma sintering (SPS) to accelerate the corrosion rate. The presence of several phases, for example α-Fe, α-Mn, γ-FeMn and Ag, provided the material with excellent mechanical properties (tensile strength UTS = 722 MPa, tensile strain A = 38%) and a higher corrosion rate (CR = 3.2 ± 0.2 mm/year). However, higher corrosion rates, associated with an increased release of degradation elements, could also raise toxicity concerns, especially at the beginning of the corrosion pattern. In this study, The focus of the present work was the control of the CR by surface modification, with nitrogen plasma immersion ion implantation (N-PIII) treatment that was applied to mechanically polished (MP) samples. This plasma treatment (PT) improved the corrosion resistance of the material, assessed by static degradation immersion tests (SDITs), especially during the first degradation stages. Twenty-eight days later, the degradation rate reached the same value of the MP condition. Nitrogen compounds on the surface of the substrate played an important role in the corrosion mechanism and corrosion product formation. The degradation analysis was carried out also by potentiodynamic tests in modified Hanks' balanced salt solution (MHBSS), and Dulbecco's phosphate buffered saline solution (DPBSS). The corrosion rate was higher in MHBSS for both conditions. However, there was no significant difference between the corrosion rate of the PT in DPBSS (CR = 1.9 ± 0.6 mm/year) and in MHBSS (CR = 2 ± 1.4 mm/year). The cell viability was assessed with human vein endothelial cells (HUVECs) via an indirect metabolic activity test (MTT assay). Due to the lower ion release of the PT condition, the cell viability increased significantly. Thus, nitrogen implantation can control the in vitro corrosion rate starting from the very first stage of the implantation, improving cell viability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
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 teacher head, 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".