Characteristics of the Structure, Mechanical, and Tribological Properties of a Mo-Mo2N Nanocomposite Coating Deposited on the Ti6Al4V Alloy by Magnetron Sputtering
Bibliographic record
Abstract
Mo-Mo2N nanocomposite coating was produced by reactive magnetron sputtering of a molybdenum target, in the atmosphere, of Ar and N2 gases. Coating was deposited on Ti6Al4V titanium alloy. Presented are the results of analysis of the XRD crystal structure, microscopic SEM, TEM and AFM analysis, measurements of hardness, Young’s modulus, and adhesion. Coating consisted of α-Mo phase, constituting the matrix, and γ-Mo2N reinforcing phase, which had columnar structure. The size of crystallite phases averaged 20.4 nm for the Mo phase and 14.1 nm for the Mo2N phase. Increasing nitrogen flow rate leads to the fragmentation of the columnar grains and increased hardness from 22.3 GPa to 27.5 GPa. The resulting coating has a low Young’s modulus of 230 GPa to 240 GPa. Measurements of hardness and Young’s modulus were carried out using the nanoindentation method. Friction coefficient and tribological wear of the coatings were determined with a tribometer, using the multi-cycle oscillation method. Among tested coatings, the lowest friction coefficient was 0.3 and wear coefficient was 10 × 10−16 m3/N∙m. In addition, this coating has an average surface roughness of RMS < 2.4 nm, determined using AFM tests, as well as a good adhesion to the substrate. The dominant wear mechanism of the Mo-Mo2N coatings was abrasive wear and wear by oxidation. The Mo-Mo2N coating produced in this work is a prospective material for the elements of machines and devices operating in dry friction conditions.
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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".