Clinoenstatite/Tantalum Coating for Enhancement of Biocompatibility and Corrosion Protection of Mg Alloy
Bibliographic record
Abstract
Biodegradable Mg alloys have appeared as the most appealing metals for biomedical applications, particularly as temporary bone implants. However, issues regarding high corrosion rate and biocompatibility restrict their application. Hence, in the present work, nanostructured clinoenstatite (CLT, MgSiO3)/tantalum nitride (TaN) was deposited on the Mg-Ca-Zn alloy via electrophoretic deposition (EPD) along with physical vapor deposition (PVD) to improve the corrosion and biological characteristics of the Mg-Ca-Zn alloy. The TaN intermediate layer with bubble like morphology possessed a compact and homogenous structure with a thickness of about 950 nm while the thick CLT over-layer (~15 μm) displayed a less compact structure containing nano-porosities as well as nanoparticles with spherical morphology. The electrochemical tests demonstrated that the as prepared CLT/TaN film is able to substantially increase the anticorrosion property of Mg-Ca-Zn bare alloy. Cytocompatibility outcomes indicated that formation of CLT and TaN on the Mg bare alloy surface enhanced cell viability, proliferation and growth, implying excellent biocompatibility. Taken together, the CLT/TaN coating exhibits appropriate characteristic including anticorrosion property and biocompatibility in order to employ in biomedical files.
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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".