The intrinsically high pitting corrosion resistance of mechanically polished nitinol in simulated physiological solutions
Bibliographic record
Abstract
Nitinol wires have been widely used in many biomedical applications, such as cardiovascular stent due to their superelasticity and shape memory effect. However, their corrosion properties and the related biocompatibility are not well understood, and the reported results are controversial. In this study, we evaluate the pitting corrosion property of nitinol, titanium, nickel, and 316L stainless steel (316LSS) wires with different surface roughnesses in a saline solution at 37 °C. The cyclic potentiodynamic polarization results show that mechanically polished nitinol and Ti wires are highly resistant to pitting corrosion, while Ni and 316LSS wires are susceptible to pitting corrosion. Electrochemical impedance spectroscopy is used to study the interface of oxide film/solution and all mechanically polished nitinol wires are covered by 2-3 nm thick films formed under open circuit potential. Furthermore, the electronic structures and semiconducting properties of passive films on nitinol, Ti and Ni wires are studied by Mott-Schottky analysis. Passive films formed on nitinol and Ti exhibit n-type semiconducting characteristics, whereas films on Ni show p-type semiconducting characteristics. Scanning Kelvin Microscopy is used to measure the surface potential difference between common inclusions from the nitinol matrix and the results indicate that the inclusions are more electrochemically noble than the nitinol matrix. Band energy theory is used to model the electrochemical interface between the passive films of nitinol and the solution under different applied potential conditions. A mechanism for the strong pitting corrosion resistance of nitinol in saline solution is proposed.
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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) | 0.000 | 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".