Corrosion behavior of TiN and TiCN coatings synthesized by PVD on the spark plasma sintered NiTi substrate
Bibliographic record
Abstract
TiN and TiCN coatings have garnered widespread attentions in the field of materials science and engineering because of their exceptional characteristics, including high melting point, excellent thermal conductivity, remarkable chemical stability, superior corrosion and wear resistance, and notable biocompatibility. These properties make them highly suitable for coating various alloys, and as a result, they have been successfully applied in numerous applications. The aim of this research study is to delve into the corrosion behavior of spark plasma sintered NiTi substrates that were coated with TiN and TiCN employing physical vapor deposition (cathodic arc technology). In order to comprehensively analyze the corrosion response, potentiodynamic polarization and electrochemical impedance spectroscopy techniques were employed. To gain deeper insights into the impact of the coating, a meticulous comparison was conducted between the corrosion resistance of the uncoated specimen and that of the coated ones. The results showcased a significant enhancement in corrosion resistance for both coated samples when compared to the uncoated NiTi substrate. However, it was found that the TiN-coated specimen showed even higher corrosion resistance than the TiCN-coated counterpart. These findings highlight the superiority of TiN coatings in terms of corrosion resistance when applied on the NiTi substrate.
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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".