Biomedical NiTi and β-Ti Alloys: From Composition, Microstructure and Thermo-Mechanics to Application
Why this work is in the frame
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Bibliographic record
Abstract
A comprehensive, bottoms-up characterization of two of the most widely used biomedical Ti-containing alloys, NiTi and β-Ti, was carried out applying a novel combination of neutron diffraction, neutron prompt-gamma activation, surface morphology, thermal analysis and mechanical tests, to relate composition, microstructure and physical-chemical-mechanical properties to unknown processing history. The commercial specimens studied are rectangular (0.43 × 0.64 mm~0.017 × 0.025 inch) wires, in both pre-formed U-shape and straight extended form. Practical performance was quantitatively linked to the influence of alloying elements, microstructure and thermo-mechanical processing. Results demonstrated that the microstructure and phase composition of β-Ti strongly depended on the composition, phase-stabilizing elements in particular, in that the 10.2 wt.% Mo content in Azdent resulted in 41.2% α phase, while Ormco with 11.6 wt.% Mo contained only β phase. Although the existence of α phase is probable in the meta-stable alloy, the α phase has never been quantified before. Further, the phase transformation behavior of NiTi directly arose from the microstructure, whilst being highly influenced by thermo-mechanical history. A strong correlation (r = 0.878) was established between phase transformation temperature and the force levels observed in bending test at body temperature, reconfirming that structure determines performance, while also being highly influenced by thermo-mechanical history. The novel methodology described is evidenced as generating a predictive profile of the eventual biomechanical properties and practical performance of the commercial materials. Overall, the work encompasses a reproducible and comprehensive approach expected to aid in future optimization and rational design of devices of metallic origin.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it