Biocorrosion and biocompatibility of Zr–Cu–Fe–Al bulk metallic glasses
Bibliographic record
Abstract
Owing to their unique chemo‐physical and structural characteristics, amorphous bulk metallic glasses (BMGs) are of great demand for fabrication of variety of advanced and innovative products including surgical and biomedical tools and devices. In this study, a series of Ni‐free Zr‐based BMGs in Zr–Cu–Fe–Al system are fabricated using copper‐mold casting technique, and their biocorrosion and biocompatibility are evaluated with respect to their corrosion behavior in the phosphate buffered saline (pH = 7.4) solution. Anodic polarization curves, scanning electron microscopy combined with energy‐dispersive X‐ray, and wettability analyses are conducted to characterize the surfaces of BMG samples. The biocompatibility of the BMG and control samples is studied by comparing cell–substrate interactions among different samples. It is found that Zr 60 Cu 20 Fe 10 Al 10 displays a higher passive region compared with that of Zr 60 Cu 22.5 Fe 7.5 Al 10 , but both BMGs exhibit lower corrosion resistance compared with Ti–6Al–4V alloy. By addition of titanium to Zr–Cu–Fe–Al system (Zr 60 Ti 6 Cu 19 Fe 5 Al 10 ), a significant increase in the passive region of the polarization curve is detected. The cell culture experiments reveal that the number of attached and grown cells is significantly higher on the surface of the treated BMGs as compared with Ti–6Al–4V substrates and the culture plate as controls. There is no noticeable difference in cellular morphology among the BMG samples, and no cytotoxicity is detected. We speculate that the interaction of water molecules and matrix proteins with the surfaces of BMGs plays an important role in cell–substrate interactions and improved cell response. Copyright © 2013 John Wiley & Sons, Ltd.
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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".