Highly elastic hard PECVD TiSiC:H/a-SiC:H coatings with enhanced erosion and corrosion resistance: The trampoline effect
Bibliographic record
Abstract
Advanced protective coatings providing high resistance to solid particle erosion as well as corrosion require system designs that combine the controlled dissipation of impact energy with the suppressed diffusion of corrosive media. In the present work, we propose and investigate a coating architecture benefiting from a “trampoline” energy-damping effect in which a hard TiSiC:H layer on top of an elastic a-SiC:H underlayer is prepared by plasma enhanced chemical vapor deposition on aerospace-grade titanium (Ti6Al4V) alloy and stainless steel 410 (SS410) substrates provided with a Cr adhesion layer. In the first part of the work, we study the effect of hydrogen in the individual a-SiC:H films (determined by elastic recoil detection) on their morphology (using scanning electron microscopy) and the mechanical and tribological properties. The films exhibit a highly advantageous combination of properties such as high hardness (>20 GPa), high elastic recovery of up to 80%, low friction coefficient (μ = 0.15 against alumina counterpart), and excellent resistance to plastic deformation and elastic resilience, expressed by the hardness (H), reduced Young's modulus (Er), and the H/E, H3/Er2, and H2/Er ratios. In particular, the measured elastic strain-to-failure of the coatings reached an unusually high value of H/Er > 0.2, thus exceeding the super-elastic limit. Simultaneously, the a-SiC:H films provided an excellent corrosion and wear protection documented by a corrosion current that was found 103–104 times lower and a wear rate that was 34 times lower compared to the values for the bare SS410 substrate. When a top TiSiC:H layer (H = 30 GPa) was applied to complete a total thickness of 25 μm, the TiSiC:H/a-SiC:H system reduced the solid particle erosion rate (Al2O3 microparticles 50 μm in diameter, speed of 36 m/s, and 90° impact angle) by a factor of 37 for films exhibiting a composite H/E ratio of 0.26. The results of the present work show that hard and highly elastic a-SiC:H-based multilayer coating systems with selectively controlled mechanical, tribological, and corrosion properties are promising candidates for the protection of metallic materials in harsh environments.
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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".