The Effect of the Formation of Superelastic NiTi Phase on Static and Dynamic Corrosion Performance of Ni-P Coating
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Bibliographic record
Abstract
The addition of superelastic NiTi particles is a great benefit to the toughness of the Ni-P coating. Nonetheless, NiTi nanopowder costs 10 times more than Ti nanopowder. Therefore, in the present study, to reduce the cost, Ni-P-NiTi composite coatings were prepared on AISI 1018 steel substrates by the electroless incorporation of Ti nanoparticles into Ni-P followed by the annealing of Ni-P-Ti coatings. The effect of the formation of a superelastic NiTi phase on static and dynamic corrosion performance was investigated. It was found that the annealed Ni-P-Ti coating (i.e., Ni-P-NiTi coating) has much higher static corrosion resistance than the as-deposited Ni-P coating. The dynamic corrosion rates in the absence of abrasive particles are 10 times higher than the static corrosion rates of the coatings. The dynamic corrosion rates in the presence of abrasive particles are one order of magnitude higher than the dynamic corrosion rates in the absence of abrasive particles. The formation of a superelastic NiTi phase considerably improved the static and dynamic corrosion performance of the Ni-P coating. In the absence of abrasive particles under flowing condition, the dynamic corrosion resistance of the annealed Ni-P-Ti coating (i.e., Ni-P-NiTi coating) is 19 times higher than that of the as-deposited Ni-P coating. In the most aggressive environment (in the presence of abrasive particles), the dynamic corrosion resistance of the annealed Ni-P-Ti coating (i.e., Ni-P-NiTi coating) is four times higher than that of the as-deposited Ni-P coating. The annealed Ni-P-Ti coating (i.e., Ni-P-NiTi coating) can be used in applications where high corrosion resistance is required, especially in an extremely aggressive environment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it