Electrochemical Corrosion and In Vitro Bioactivity of Nano-Grained Biomedical Ti-20Nb-13Zr Alloy in a Simulated Body Fluid
Bibliographic record
Abstract
The bioactivity and the corrosion protection for a novel nano-grained Ti-20Nb-13Zr at % alloy were examined in a simulated body fluid (SBF). The effect of the SPS's temperature on the corrosion performance was investigated. The phases and microstructural details of the developed alloy were analyzed by XRD (X-ray Diffraction), SEM (Scanning Electron Microscopy), and TEM (Transmission Electron Microscope). The electrochemical study was investigated using linear potentiodynamic polarization and electrochemical impedance spectroscopy in a SBF, and the bioactivity was examined by immersing the developed alloy in a SBF for 3, 7, and 14 days. The morphology of the depositions after immersion was examined using SEM. Alloy surface analysis after immersion in the SBF was characterized by XPS (X-ray Photoelectron Spectroscopy). The results of the bioactivity test in SBF revealed the growth of a hydroxyapatite layer on the surface of the alloy. The analysis of XPS showed the formation of protective oxides of TiO₂, Ti₂O₃, ZrO₂, Nb₂O₅, and a Ca₃(PO₄)₂ compound (precursor of hydroxyapatite) deposited on the alloy surface, indicating that the presented alloy can stimulate bone formation. The corrosion resistance increased by increasing the sintering temperature and the highest corrosion resistance was obtained at 1200 °C. The improved corrosion protection was found to be related to the alloy densification. The bioactivity and the corrosion resistance of the developed nanostructured alloy in a SBF renders the nanostructured Ti-20Nb-13Zr alloy a promising candidate as an implant material.
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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".