The Effect of Deposition Parameters on the Structure and Mechanical Properties of Chromium Oxide Coatings Deposited by Reactive Magnetron Sputtering
Bibliographic record
Abstract
Appropriate conditions for depositing hard Cr2O3 coatings by reactive sputtering techniques have yet to be defined. To fill this gap, the effect of principal deposition parameters, including deposition pressure, temperature, Cr-target voltage, and Ar/O2 ratio, on both the structure and mechanical properties of chromium oxide coatings was investigated. A relationship between processing, structure, and the mechanical properties of chromium oxide coatings was established. Scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, and X-ray Photoelectron Spectroscopy (XPS) were used to characterize the morphology, structure, and chemical compositions of the coatings that were prepared. An optical profilometer was employed to measure both the roughness and thickness of the coatings. The hardness and Young’s modulus of the coatings both as-deposited and after annealing conditions were measured by nanoindentation. The results showed that depositing hard Cr2O3 coatings is a highly critical task, requiring special deposition conditions. Cr2O3 coatings with a high hardness of approximately 25 GPa could be achieved at room temperature, at a low pressure of 1.6 × 10−1 Pa, where Cr-target voltage and oxygen content were 260 V and between 15–25 vol % of total gas, respectively. A dense stoichiometric Cr2O3 structure was found to be responsible for the high chromium oxide coating hardness observed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".