Effect of oxygen incorporation on structural and properties of Ti–Si–N nanocomposite coatings deposited by reactive unbalanced magnetron sputtering
Bibliographic record
Abstract
Ti–Si–N–O nanocomposite coatings with different contents of oxygen were deposited by a combined dc/rf reactive unbalanced magnetron sputtering process in an Ar+N2+O2 mixture atmosphere. The composition, structure, mechanical, and tribological properties of the as-deposited coatings were analyzed by energy dispersive analysis of x-rays, x-ray diffraction (XRD), nanoindentation, and pin-on-disk tribometer experiments, respectively. It was found that in the range of lower oxygen content with atomic ratio of O∕N⩽0.72, the tribological properties of the Ti–Si–N–O coatings are evidently improved, in comparison with the coating without oxygen incorporation. At O∕N=0.72, the friction coefficient and wear rate of the as-deposited coatings are reduced to 20% and 45%, respectively. Meanwhile, however, their hardness was not reduced, but, on the contrary, slightly increased. With increasing oxygen content further to O∕N⩾0.72, coating hardness decreased significantly. The friction coefficient of the as-deposited coatings decreased monotonously with the increase of oxygen content in the whole composition range investigated. The wear rate of the coatings exhibited a minimum value at around O∕N=0.72. In the lower range of O∕N, wear rate decreased significantly due to the lubricant effect of oxygen incorporation, while in the higher range of O∕N, wear rate increased gradually due to the weakening of coating hardness. XRD patterns revealed that the as-deposited coatings were mainly crystallized in cubic TiN phase, accompanied with minority of rutile structure titania in the case of higher oxygen incorporation.
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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) | 0.000 | 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".