Synthesis of Si–C–N coatings by thermal Plasmajet chemical vapour deposition applying liquid precursors
Bibliographic record
Abstract
Abstract Materials in the system Si–C–N feature excellent properties for wear protection applications, even at elevated temperatures, and an excellent thermal shock resistance. As these materials have no melting point, coatings have to be manufactured via a synthesis. Conventional chemical vapour deposition (CVD) processes have the disadvantage of low deposition rates. Thermal Plasmajet CVD processes with liquid feedstock feature the highest deposition rates among the gas‐phase synthesis processes. Single and triple DC torches and HF torches with supersonic nozzles have successfully been applied to produce Si–C(–N) coatings on different steel, aluminium, titanium and copper alloys, as well as on graphite. Besides chlorosilanes, hexamethyldisiloxane, tetramethyldisiloxane and hexamethyldisilazane have been used as liquid single precursors. Deposition rates up to 1500 μm h −1 have been achieved. The coatings show cauliflower, columnar or dense morphology and an amorphous or nanocrystalline structure. The formation of both α‐ and β‐Si 3 N 4 has been verified by X‐ray diffraction. The application of chlorosilanes always results in chlorine‐containing coatings. The chlorine causes severe corrosion in the interface to mild carbon steel substrates. The processes are compared taking into account their characteristics concerning the injection modes, gas temperature and velocity profiles determined by enthalpy probe measurements. The process conditions are correlated to the coating microstructure and the adhesion to the substrates and guidelines for the optimum production of Si–C–N coatings by Plasmajet CVD are deduced. Emission spectroscopy is used to determine the mechanisms of the coating formation. Full dissociation of the liquid feedstock in the plasma jet has been verified. Copyright © 2001 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".