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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".