Electrochemical Corrosion Behavior of Niobium Alloys as Metallic Bio-implants
Bibliographic record
Abstract
Abstract Alloying is one of the most important methods to improve the corrosion resistance and mechanical properties of metals. Previous studies show that the corrosion resistance of niobium Nb could be increased by Ta alloying in alkali solutions. Nevertheless, compared with Ti alloys, only a few Nb alloys were reported to increase their corrosion resistance. In this work, the mechanical properties and microstructure of Nb-Ta-Re, Nb-Re, Nb-Pd, and Nb-Pt alloys were studied by nano-indentation measurements and X-ray diffraction, respectively. The corrosion behavior of these alloys was compared by electrochemical methods, including open circuit potential measurement, potentiodynamic, and electrochemical impedance spectroscopy, in phosphate buffered saline (PBS) and sodium fluoride solutions. The properties of the passive films formed on these alloys were characterized by X-ray photoelectron spectroscopy. The results showed that Ta or Re alloying could increase the hardness and elastic modulus of Nb, however, these alloying elements did not significantly modify the phase structure of Nb. All of these Nb alloys show high corrosion resistance in PBS and NaF-containing PBS solutions, and no pitting behavior was observed. Re and Ta alloying could further increase the corrosion resistance of Nb, however, small amounts of Pd and Pt alloying did not significantly affect the corrosion resistance of Nb.
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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".