A new ternary alloy Ti26Zr24Nb for biomedical application : behavior in corrosion, wear, and tribocorrosion
Bibliographic record
Abstract
Titanium (Ti)-based alloys with only β-phase have arisen the interest of academics and industrials for bone implants due to their mechanical properties close to those of hard tissues, and for the capability of allowing with β-stabilizers, totally biocompatible elements like Nb, Ta, and Zr. However, there is no consensus about the most adequate composition and, in many cases, tribocorrosion behavior is not considered during their development. New ternary alloy Ti26Zr24Nb as biomaterial is the matter of study of this work regarding wear and corrosion resistances, and the tribocorrosion behavior of this alloy in contact with pH 7, deaerated Hanks solution at 37 °C to simulate a body fuid. All samples have had surface prepared according to the same protocol and a subtract characterization previously and after the electrochemical, dry wear, and tribocorrosion experiments. Results showed high corrosion resistance, with constant open circuit potential (~− 200 mV) and low corrosion current density (~0.9×10−8 A/cm2 ) and important pitting resistance, as well as higher coefcient of friction (COF) for both wear (0.69) and tribocorrosion (0.65) tests than those reported in the literature and, additionally, less wear under tribocorrosion condition compared to dry wear test.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".