Refractory high entropy TiTaZrHfW-N/Si3N4 nano-layered alloy thin film’s oxidation resistance
Bibliographic record
Abstract
Refractory high entropy TiTaZrHfW-N/Si 3 -N 4 nano-layered alloy thin films are investigated to study the effect of nano-layered architecture and silicon (Si) mean content on their structural, mechanical, thermal properties and oxidation behavior. The films are deposited using direct current (DC) magnetron sputtering of separate Si and TiTaZrHfW targets. The Si mean content is controlled by tailoring the power discharge applied to the Si target. The deposition process led to a nano-layered architecture where Si 3 N 4 (amorphous) and TiTaZrHfW-N (nano-crystalized NaCl FCC type structure) are alternated. By increasing the thickness of Si 3 N 4 nano-layers, the Si mean content increases. All coatings are found to have good thermal stability after annealing under vacuum at 900 °C. Increasing Si mean content reduces the film’s hardness; however, the annealing treatment at 900 °C improves it. A super-hardness of 41 GPa is found for the post-annealed Si-free film. Si 3 N 4 nano-layers enhance the oxidation resistance at elevated temperatures of 600, 700, and 800 °C. This oxidation resistance is further enhanced by increasing the nano-layer’s period and also by increasing the density of the films.
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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".