Electrochemical Study of Nb-10Ta-1Re and Ti-6Al-4v Alloys for Dental Implant Applications
Bibliographic record
Abstract
Abstract Dental implants are exposed to a wide range of pH values of saliva and different concentrations of fluoride. In order to prevent plaque and caries formation, prophylactic products have been used more commonly for dental treatments. These products contain different concentrations of fluoride, for example; bucal rinses, toothpastes and gels contain 200ppm, 1000-1500 ppm and 10000-20000 ppm of fluoride respectively with the pH varying from acidic values to neutral ones. In this study, the effects of fluoride concentration and pH value on the corrosion behavior of Nb-10Ta-1Re and Ti-6Al-4V alloys in artificial saliva at 37°C were investigated by electrochemical measurements. For both alloys, electrochemical impedance spectroscopy and potentiodynamic polarization results showed that the corrosion rate increased with decreasing pH; also, increasing fluoride concentrations resulted in an increase in the corrosion rate. EIS results suggested that the oxide layer of Ti-6Al-4V alloy became porous in artificial saliva with high fluoride concentrations; on the contrary, Nb-10Ta-1Re alloy retained its compact oxide layer. Scanning electron microscopy results showed that after 5 days of immersion the oxide layer was washed away from the surface of Ti-6Al-4V alloy. For Nb-10Ta-1Re alloy; however, the polishing scratches were evident. At low pH values and high fluoride concentrations Nb-10Ta-1Re alloy showed better corrosion resistance in comparison with Ti-6Al-4V alloy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".