Effect of surface treatment of NiTi alloy on its corrosion behavior in Hanks’ solution
Bibliographic record
Abstract
The effect of surface modification of NiTi shape memory alloy on its corrosion behavior in Hanks' solution was determined. The near-equi-atomic super elastic NiTi (Ni 55.8 wt %) alloy used for this study was provided by Memry USA. The surfaces of heat-treated samples were modified by mechanical polishing (MP), electropolishing (EP), and electropolishing followed by chemical passivation (CP). As-heat-treated samples with straw-colored oxide finishes (SCO) and blue-colored oxide finishes (BO) also were included in the study. Surface analysis was performed using auger electron spectroscopy (AES), atomic force microscopy (AFM), and contact angle measurements (CAM). It was shown that surface roughness increased in the order CP < EP < SCO < BO < MP. The nickel release within the five groups of NiTi samples, as determined by atomic absorption spectrophotometry, reduced in time over the measured period. The level of Ni ions released over a 25-day immersion period was highest in the SCO sample (0.002 microg/day). This Ni level is negligible compared with the daily intake of Ni in an ordinary diet. The auger electron spectroscopy (AES) analyses indicated that before immersion in Hanks' physiologic solution, the main surface composition of all the samples was titanium and nickel, with a small amount of oxygen, carbon, and sulphur as contaminants. And the surface oxide thickness of the different samples increased in the order CP < EP < MP < BO < SCO. On the other hand, for the electrodes treated under the same conditions, the mean breakdown potential value decreased in the order BO > MP > CP > EP > SCO while the corrosion current density and rate increased in the order CP < SCO < EP < BO < MP.
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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".