Effect of additive concentration and temperature on the tribological performance of eco-friendly bonded MoS2-PEEK solid lubricants for aerospace applications
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Bibliographic record
Abstract
Spray-bonded molybdenum disulfide (MoS 2 ) coatings are widely used in space and aerospace components for their low friction and wear resistance. Conventional formulations often rely on toxic additives/dopants such as Pb-based compounds and Sb 2 O 3 to improve performance, raising environmental and health concerns. This study investigates polyetheretherketone (PEEK) as an eco-friendly alternative additive and evaluates how it affects the tribological behavior of bonded MoS 2 coatings at both room temperature (25 °C) and low-temperature (–50 °C). Coatings with PEEK concentrations (1.4, 1.9, 2.4, 2.9, and 3.4 wt.%) were prepared and systematically tested under ball-on-flat reciprocating sliding. The coatings were then characterized through nanoindentation, confocal microscopy, SEM/EDS, FIB-SEM, and Raman spectroscopy. Results showed that PEEK had a significant effect on wear behavior, while the steady-state friction coefficient remained statistically unchanged. Increasing PEEK content increased H/E and H 3 /E 2 ratios, enhancing matrix ductility and crack resistance. Wear resistance improved up to 2.9 wt.% PEEK, where the coating exhibited the shallowest wear track and lowest wear rate. This optimum composition balanced matrix compliance and load-bearing capacity, promoting stable tribo/transfer film. Conversely, excessive PEEK content (3.4 wt.%) disrupted MoS 2 percolation pathways and uniform load distribution, resulting in reduced wear resistance. Although MoS 2 –PEEK coatings showed inferior tribological performance than commercial MoS 2 –Sb 2 O 3 coating (Everlube 620C), they offer a sustainable route, with additive optimization potentially closing the performance gap. At –50 °C, all coatings showed negligible wear and low steady-state friction coefficient (0.04–0.06), likely attributed to the formation of interfacial ice films that masked compositional differences.
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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 it