Microstructure and properties of as‐cast Zr‐2.5Nb‐1X (X = Ru, Mo, Ta and Si) alloys for biomedical application
Bibliographic record
Abstract
Abstract The microstructure and properties of as‐cast Zr‐2.5Nb‐1X (X = Ru, Mo, Ta and Si) alloy are screened to explore novel biomedical zirconium alloys for magnetic resonance applications. Corresponding microstructure and phase transformation were characterized using X‐ray diffraction (XRD), scanning electron microscope (SEM) and transmission electron microscope (TEM). Hardness test, magnetic detection and electrochemical corrosion measurements are taken to present properties. The results show that all alloys consist of α‐Zr, β‐Zr and ω‐Zr. α‐Zr and β‐Zr mainly exist in the form of parallel and intersecting plates, and nanoscale ω‐Zr is dispersed in β‐Zr plate. Especially, blocky ω‐Zr with needle‐like α‐Zr is only found in plate‐free blocks of Zr‐2.5Nb‐1Mo/Ru alloy. The orientation relationship (OR) between α‐ Zr and ω‐Zr follows // and //( 011) ω . Combining this OR with the OR between β‐Zr and ω‐Zr, the transformation relationship between β‐Zr/ω‐Zr and α‐Zr is also discussed. Zr‐2.5Nb‐1Ru alloy with high corrosion potential (− 0.500 V), low corrosion rate (0.949 μm·year –1 ) and low magnetic susceptibility (92 × 10 −6 ) shows great potential to be a novel biomedical implant with magnetic resonance imaging compatibility. Based on the experimental results, the possible relationship among alloying elements, microstructure and properties has been established in these Zr‐2.5Nb‐1X alloys.
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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".