Corrosion and in vitro cytocompatibility investigation on the designed Mg-Zn-Ag metallic glasses for biomedical application
Bibliographic record
Abstract
In the present work, seven Mg-Zn-Ag alloys with the nominal composition of Mg96-xZnxAg4 (x = 17, 20, 23, 26, 29, 32, 35 in at.%) were prepared by induction melting and single-roller melt-spinning. The X-ray diffraction (XRD) analyses indicate the metallic glasses with three composition of Mg73Zn23Ag4, Mg70Zn26Ag4, and Mg67Zn29Ag4 were obtained successfully. The differential scanning calorimetry (DSC) measurement was used to obtain the characteristic temperature of Mg-Zn-Ag metallic glasses for the glass-forming ability analysis. The maximum glass transition temperature (Trg) was found to be 0.525 with a composition close to Mg67Zn29Ag4, which results in the best glass-forming ability. Moreover, the immersion test in simulated body fluid (SBF) demonstrate the relative homogeneous corrosion behavior of the Mg-Zn-Ag metallic glasses. The corrosion rate of Mg-Zn-Ag metallic glasses in SBF solution decreases with the increase of Zn content. The sample Mg67Zn29Ag4 has the lowest corrosion rate of 0.19 mm/yr, which could meet the clinical application requirement well. The in vitro cell experiments show that the Madin-Darby canine kidney (MDCK) cells cultured in sample Mg67Zn29Ag4 and its extraction medium have higher activity. However, the Mg-Zn-Ag metallic glasses exhibit obvious inhibitory effect on human rhabdomyosarcoma (RD) tumor cells. The present investigations on the glass-forming ability, corrosion behavior, cytocompatibility and tumor inhibition function of the Mg-Zn-Ag based metallic glass could reveal their biomedical application possibility.
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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 | 0.002 | 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 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".