Temperature-driven tribological behaviour of PEO-coated AZ31 sliding against MoS₂-coated steel
Bibliographic record
Abstract
Plasma electrolytic oxidation (PEO) treatment typically enhances the wear resistance of lightweight alloys but also significantly increases the coefficient of friction (COF). To address this limitation, particularly for PEO treated magnesium alloys, this study explores the use of MoS₂-coated steel counterfaces under lubricated sliding conditions up to 100 °C. The results showed that PEO-coated AZ31 sliding against MoS₂-coated steel exhibited consistently low COF and wear rates across the temperature range of 25 °C to 100 °C. At temperatures above 50 °C, a stable MoS₂-rich tribolayer formed on the PEO surface, effectively reducing friction and wear, particularly as the oil film thinned with increasing temperature. A slight increase in COF was observed between 75 °C and 100 °C, attributed to the formation of MoO₃, which reduced the lubricating effectiveness of the MoS₂ tribolayer. In contrast, uncoated AZ31, lacking a tribolayer, showed significant increases in friction and wear due to surface oxidation and thermally activated plastic deformation. This study demonstrates that pairing PEO-coated AZ31 with MoS₂-coated steel counterfaces is an effective strategy for lowering COF and wear rates at temperatures up to 100 °C, indicating potential for improving the tribological performance of magnesium alloys in high-temperature applications such as the automotive and aerospace industries. • PEO and MoS₂ reduced COF and enhanced AZ31's performance under high-temp lubrication. • PEO-coated AZ31 vs. MoS₂-steel showed low COF and wear from 25 °C to 100 °C. • Above 50 °C, MoS₂ tribolayer on PEO maintained low friction as uncoated AZ31's wear increased. • Between 75 and 100 °C, MoO₃ formation slightly increased COF, reducing MoS₂’s lubrication effectiveness.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".