Structural, physical, chemical, and biological surface characterization of thermomechanically treated Ti‐Nb‐based alloys for bone implants
Bibliographic record
Abstract
Abstract Metastable near‐beta Ti‐21.8Nb‐6Zr and Ti‐19.7Nb‐5.8Ta (at%) alloys were subjected to a thermomechanical treatment comprising cold rolling (CR) with a true strain of e = 0.3 and post‐deformation annealing (PDA) in the 500–900°C temperature range to ensure the superelastic behavior which is important for bone implants. It was found that PDA resulted in formation of about 1–2 μm‐thick oxide layer on the Ti‐Nb‐Zr and Ti‐Nb‐Ta alloy samples; the layer was mainly composed of TiO2, in rutile and anatase modifications. The structure, the phase and chemical compositions, and some surface‐sensitive properties of the alloys were compared to those of Ti‐50.7Ni and Ti‐Grade2 reference materials. These surface layers (especially that of the Ti‐Nb‐Zr alloy) demonstrated a promising combination of high cohesion strength (load causing surface layer fracture is over 25 N), hardness (∼12 GPa), and hydrophilicity (contact angle ∼40°). Surface modification by controlled oxidation during air annealing increases corrosion resistance and enhances in vivo osteoinductive properties of Ti‐Nb‐Zr alloys by changing the surface microrelief, increasing the surface wettability, and improving the mechanical characteristics, thus laying the foundation for the development of medical implants with prolonged service life. So, it was confirmed that the same thermomechanical treatment, which creates conditions for the superelastic behavior of the bulk metal (CR: e = 0.3 + PDA = 500–700°C for 1 hr), would also create a strong, protective and biocompatible layer on the implant surface.
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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) | 0.001 | 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".