Effect of interface roughness on the tribo-corrosion behavior of diamond like carbon coatings on titanium alloy
Bibliographic record
Abstract
Substrate surface morphology can significantly affect the functional performance and durability of the subsequently deposited coatings. In the present work, diamondlike carbon films were prepared by radio frequency plasma enhanced chemical vapor deposition on Ti-6Al-4V alloy substrates with different premediated surface roughness parameters (average roughness Ra, Skewness Rsk and Kurtosis Rku), and their mechanical, electrochemical, and tribo-corrosion properties were studied. The surface parameters, the microstructure, and the chemical composition were assessed by optical profilometry, scanning electron microscopy, Raman spectroscopy, and energy dispersive spectroscopy. The mechanical properties were evaluated using depth-sensing indentation and scratch testing, and the films' tribo-corrosion behavior was determined using a reciprocating tribometer in a ball-on-flat configuration with the tribological contact (Al2O3 counterpart) immersed in a 3.5% NaCl sea waterlike solution. The evolution of the corrosion potential as a function of time before, during, and after the wear tests indicated that the tribo-corrosion behavior is strongly affected by the surface roughness parameters. The potential of samples with Ra = 20 nm was unaffected by the rubbing process under the chosen tribological conditions compared to samples with higher Ra values. A similar trend was observed for samples with negative Rsk as opposed to those with Rku values greater than 3. The poor tribo-corrosion behavior of samples with Ra > 20 nm and high Rku (greater than 3) is mainly due to the significant height of asperities that constitute initiation sites for stress and strain failure on the surface. The predominant degradation mechanism was abrasive wear for samples with high surface roughness, tested under dry wear conditions. In the tribo-corrosion process, a synergy between the abrasive wear and corrosion was found to contribute to the overall material loss.
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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".