Microstructure and tribological performance of nanocomposite Ti–Si–C–N coatings deposited using hexamethyldisilazane precursor
Bibliographic record
Abstract
Thick nanocomposite Ti–Si–C–N coatings (20–30 μm) were deposited on Ti–6Al–4V substrate by magnetron sputtering of Ti in a gas mixture of Ar, N2, and hexamethyldisilazane (HMDSN) under various deposition conditions. Microstructure and composition of the coatings were studied using scanning electron microscopy, x-ray diffraction, and energy dispersive x-ray spectroscopy, while the mechanical and tribological properties of these coatings were studied using Rc indentation, and micro- and nanoindentations, solid particle erosion testing, and ball-on-disk wear testing. It has been observed that the Si concentration of these coatings is varied from 0% (TiN) to 15% (Ti–Si–C–N), while the structure of these coatings is similar to the nanocomposite Ti–Si–N coatings and consists of nanocrystalline B1 structured Ti(C,N) in an amorphous matrix of SiCxNy with the grain size of 5−>100 nm, depending on the coating preparation process. These coatings exhibit excellent adhesion when subjected to Rc indentation tests. The microhardness of these coatings varies from 1200 to 3400 HV25, while the nanohardness varies from 10 to 26 GPa. Both the microhardness and nanohardness are slightly lower than those of similar coatings prepared using trimethylsilane. However, the erosion test using a microsand erosion tester at both 30° and 90° incident angles shows that these coatings have very high erosion resistance and up to a few hundred times of improvement has been observed. These coatings also exhibit very high resistance to sliding wear with a low coefficient of friction of about 0.2 in dry sliding. There are a few advantages of using the HMDSN precursor to prepare the Ti–Si–C–N coatings over conventional magnetron sputtered deposition of Ti–Si–N coatings including composition uniformity, precursor handling safety, and high deposition rate. The coatings can be applied to protect gas turbine compressor blades from solid particle erosion and steam turbine blades from liquid droplet erosion, as well as other mechanical components that experience severe abrasion. These coatings may also be used in areas where both high wear resistance and low friction are required.
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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".