Biomedical NiTi and β-Ti Alloys: From Composition, Microstructure and Thermo-Mechanics to Application
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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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 | 0.001 | 0.001 |
| 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".