Influence of nitrogen addition on the wear performance of lightweight (AlCoCrNiSiTi)100-xNx thin films developed by magnetron sputtering
Bibliographic record
Abstract
(AlCoCrNiSiTi) 100-x N x thin films were developed using a pulsed DC four-source closed field magnetron sputtering system with varying nitrogen gas flow ratios (R N = 0, 0.25, and 0.40). The impact of nitrogen addition on the elemental composition, deposition rate, morphology, roughness, and microstructure are examined in this work. With a rise in the nitrogen content, all thin films showed an amorphous structure and a greater presence of covalent nitride bonds with light-weight elements such as Al, Si, and Ti. Hardness increased significantly with the addition of nitrogen, rising from 7.9 ± 0.6 GPa to 10.2 ± 0.3 GPa. The thin film with the highest nitrogen content demonstrated superior wear resistance, as indicated by the highest H/E r and H 3 /E r 2 ratios. Microtribological testing under dry air conditions revealed a notable enhancement in wear resistance with the addition of nitrogen, reducing the wear rate from 1.76-2.63 × 10 -6 mm 3 /Nm for the nitrogen-free thin film to 0.18–0.37 × 10 -6 mm 3 /Nm for the thin film deposited at R N = 0.40. Further analysis using Schiffmann’s model highlighted a shift from plastic-dominated behavior in nitrogen-free thin films to elastic-dominated behavior in nitrogen-containing thin films, presenting the correlation between mechanical properties and wear resistance of the developed thin 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) | 0.000 | 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".