Electrochemical Behaviours of Titanium Nitride (TiN) and Chromium Nitride (CrN) Based PVD Coating Systems
Bibliographic record
Abstract
Hard coatings applied to steel components prevent corrosion attacks while at the same time mitigate erosion attack. However, the presence of process related through-coating defects such as pin holes, voids and growth defects, provides accesses for corrosive media to metal substrates, initializing pitting corrosion and eventually resulting in coating failure. This research studies the corrosion behavior of PVD TiN and CrN (CrSiCN) coated steels in 3.5 wt. % NaCl aqueous solution using potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques. The results revealed that in a coating-substrate system, effective diffusion coefficient and diffusion layer thickness control the corrosion resistance; both factors are found to be related to coating microstructure. A denser and thicker coating structure are shown to have lower effective diffusion coefficients and greater effective diffusion layer thicknesses and consequently provided a high resistance to electrochemical corrosion.
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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 | 0.001 | 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".