Effect of surface treatment and sterilization processes on the corrosion behavior of NiTi shape memory alloy
Bibliographic record
Abstract
Nickel-titanium (NiTi) alloy derives its biocompatibility and good corrosion resistance from a homogeneous oxide layer mainly composed of TiO(2), with a very low concentration of nickel. In this article, we described the corrosion behavior of NiTi alloys after mechanical polishing, electropolishing, and sterilization processes using cyclic polarization and atomic absorption. As a preparative surface treatment, electropolishing decreased the amount of nickel on the surface and remarkably improved the corrosion behavior of the alloy by increasing the mean breakdown potential value and the reproducibility of the results (0.99 +/- 0.05 V/SCE vs. 0.53 +/- 0. 42). Ethylene oxide and Sterrad(R) sterilization techniques did not modify the corrosion resistance of electropolished NiTi, whereas a steam autoclave and, to a lesser extent, peracetic acid sterilization produced scattered breakdown potential. In comparing the corrosion resistance of common biomaterials, NiTi ranked between 316L stainless steel and Ti6A14V even after sterilization. Electropolished NiTi and 316L stainless-steel alloys released similar amounts of nickel after a few days of immersion in Hank's solution. Measurements by atomic absorption have shown that the amount of released nickel from passive dissolution was below the expected toxic level in the human body. Auger electron spectroscopy analyses indicated surface contamination by Ca and P on NiTi during immersion, but no significant modification in oxide thickness was observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".