Enhanced high-temperature tribological performance of fluorinated tetrahedral amorphous carbon (ta-C:F) coatings in sliding applications
Bibliographic record
Abstract
Abstract This study investigated the dry sliding behaviour of fluorinated tetrahedral amorphous carbon (ta-C:F) coatings against uncoated 52100 steel at temperatures ranging from 25 °C to 300 °C. The ta-C:F coatings demonstrated significant reductions in both the coefficient of friction(COF) during the running-in stage and at steady state, particularly within the temperature range of 25 °C to 200 °C, surpassing the performance of well-established a-C:H coatings. SEM analyses revealed the formation of transfer layers on the contact surface of 52100 steel when tested against ta-C:F coatings at temperatures up to 200 °C, while none were detected at temperatures ≥250 °C. Raman spectroscopy indicated a transition from sp 3 to sp 2 carbon structures in the carbonaceous transfer layers with increasing temperature, and XPS scans confirmed an increase in fluorine (F) concentration within these layers, correlating with reduced COF. The comparative analysis at 120 °C emphasized the intrinsic advantages of ta-C:F coatings in high-temperature applications, demonstrating a nearly 50% lower COF (0.08) when compared to traditional boundary-lubricated steel-to-steel sliding contacts. These findings have significant implications for enhancing the efficiency and durability of various mechanical systems, particularly in industries like automotive and manufacturing.
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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) | 0.001 | 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".