Optical characteristics and color of TiN/SiN1.3 nanocomposite coatings
Bibliographic record
Abstract
Decorative and protective coatings deposited by dry methods are very attractive due to the environmental restrictions on traditional wet chemical techniques. In this context, nanocomposite hard coatings were fabricated by plasma enhanced chemical vapor deposition from TiCl4/SiH4/N2/H2/Ar gas mixtures at substrate temperatures of 300 and 500 °C. Their optical characteristics such as refractive index, extinction coefficient, luminosity, and colors were quantitatively determined by spectroscopic ellipsometry and spectrophotometry. Pure TiN exhibited a metal-like behavior, and its optical properties were modeled by the Drude (free carrier) approach. Nanocomposite films consisting of about 5–10 nm size TiN grains incorporated in an amorphous SiN1.3 matrix were modeled by a sum of Drude and Lorentz (interband) transitions. Optical properties of the films were explained by their morphology and chemical structure, investigated by a multitechnique approach using scanning electron microscopy, transmission electron microscopy, elastic recoil detection in the time-of-flight regime, Auger electron spectroscopy, x-ray photoelectron spectroscopy, and Raman spectroscopy. Subsequent addition of Si to TiN caused a complex transformation from a polycrystalline to nanocomposite microstructure, which adopted a predominantly amorphous character. This was accompanied by a transition from a metallic to a dielectric behavior in terms of the optical response and electronic properties.
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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".