The Electrochemical and Mechanical Behavior of Bulk and Porous Superelastic Ti‒Zr-Based Alloys for Biomedical Applications
Bibliographic record
Abstract
Titanium alloys are well recognized as appropriate materials for biomedical implants. These devices are designed to operate in quite aggressive human body media, so it is important to study the corrosion and electrochemical behavior of the novel materials alongside the underlying chemical and structural features. In the present study, the prospective Ti‒Zr-based superelastic alloys (Ti-18Zr-14Nb, Ti-18Zr-15Nb, Ti-18Zr-13Nb-1Ta, atom %) were analyzed in terms of their phase composition, functional mechanical properties, the composition and structure of surface oxide films, and the corresponding corrosion and electrochemical behavior in Hanks' simulated biological solution. The electrochemical parameters of the Ti-18Zr-14Nb material in bulk and foam states were also compared. The results show a significant difference in the functional performance of the studied materials, with different composition and structure states. In particular, the positive effect of the thermomechanical treatment regime, leading to the formation of a favorable microstructure on the corrosion resistance, has been revealed. In general, the Ti-18Zr-15Nb alloy exhibits the optimum combination of functional characteristics in Hanks' solution, while the Ti-18Zr-13Nb-1Ta alloy shows the highest resistance to the corrosion environment. The Ti-18Zr-14Nb-based foam material exhibits slightly lower passivation kinetics as compared to its bulk equivalent.
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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".