Influence of Substrate Heating and Nitrogen Flow on the Composition, Morphological and Mechanical Properties of SiNx Coatings Aimed for Joint Replacements
Bibliographic record
Abstract
Silicon nitride (SiNx) coatings are promising for joint replacement applications due to their high wear resistance and biocompatibility. For such coatings, a higher nitrogen content, obtained through an increased nitrogen gas supply, has been found to be beneficial in terms of a decreased dissolution rate of the coatings. The substrate temperature has also been found to affect the composition as well as the microstructure of similar coatings. The aim of this study was to investigate the effect of the substrate temperature and nitrogen flow on the coating composition, microstructure and mechanical properties. SiNx coatings were deposited onto CoCrMo discs using reactive high power impulse magnetron sputtering. During deposition, the substrate temperatures were set to 200 °C, 350 °C or 430 °C, with nitrogen-to-argon flow ratios of 0.06, 0.17 or 0.30. Scanning and transmission electron spectroscopy revealed that the coatings were homogenous and amorphous. The coatings displayed a nitrogen content of 23–48 at.% (X-ray photoelectron spectroscopy). The surface roughness was similar to uncoated CoCrMo (p = 0.25) (vertical scanning interferometry). The hardness and Young’s modulus, as determined from nanoindentation, scaled with the nitrogen content of the coatings, with the hardness ranging from 12 ± 1 GPa to 26 ± 2 GPa and the Young’s moduli ranging from 173 ± 8 GPa to 293 ± 18 GPa, when the nitrogen content increased from 23% to 48%. The low surface roughness and high nano-hardness are promising for applications exposed to wear, such as joint implants.
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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) | 0.000 | 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 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".