Effect of Si and C concentration on the microstructure, and the mechanical, tribological and electrochemical properties of nanocomposite TiC/a-SiC:H/a-C:H coatings prepared by plasma enhanced chemical vapor deposition
Bibliographic record
Abstract
The nanocomposite TiC/a-SiC:H/a-C:H (presented as Ti-Si-C) coatings attract considerable interest due to their possible applications such as wear protective coatings, diffusion barriers, and materials for solar cells and electrical contacts. In order to explore new film properties and open new opportunities, in the present work, we prepare a series of C-rich Ti-Si-C coatings with different Si and C concentrations using plasma enhanced chemical vapor deposition, and we systematically investigate the effect of elemental composition on the microstructure, and on the mechanical, tribological and electrochemical properties. XRD and XPS analyses demonstrate that the Ti-Si-C coatings mainly consist of nanocrystalline (nc-) TiC embedded in amorphous (a-) SiC:H and a-C:H matrices. Ti-Si-C coatings with a high Si concentration possess enhanced mechanical properties (high hardness), while those with additional C exhibit superior tribological behaviors. The increase of Si and/or C concentrations leads to a grain size refinement of the TiC nanocrystals and to an expansion of the amorphous phase. This in turn substantially enhances their corrosion resistance. Ti-Si-C coatings with the highest Si or C contents exhibit the best corrosion performance among the tested samples by improving the corrosion resistance of the SS410 substrate by a factor of ∼400.
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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".