The Effect of the Formation of Superelastic NiTi Phase on Static and Dynamic Corrosion Performance of Ni-P Coating
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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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 | 0.001 |
| 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".