Tribochemical stability and friction mechanisms of graphene nanoplatelets in engine oil at elevated temperatures
Bibliographic record
Abstract
The tribochemical behaviour and thermal stability of graphene nanoplatelets (GNPs) as boundary-lubricating additives in engine oil were investigated through steel-on-steel sliding tests from 25 °C to 120 °C. At 25 °C, GNPs had negligible effect on coefficient of friction, COF: 0.083 in oil-only vs. 0.081 in oil + GNPs). At 50 °C and 80 °C, the COF decreased significantly with GNP addition—from 0.106 to 0.077 (~27 %) and from 0.114 to 0.088 (~22 %), respectively. At 120 °C, only a modest reduction was observed (0.139 to 0.123), indicating a decline in additive effectiveness at elevated temperature. X-ray photoelectron spectroscopy (XPS) and Raman analysis showed that the tribolayer remained chemically stable up to ~80 °C, with predominant C–C/C–H bonding and limited structural disorder. At and above 80 °C, oxidation became evident, with a shift to O–C=O species and increased D and D′ band intensities. HR-TEM revealed bending and fragmentation of graphene layers embedded within an Fe₂O₃-rich tribofilm, with interlayer spacings increasing to 0.34–0.36 nm. These results indicate that GNPs reduce friction and wear by forming a carbon-rich tribolayer, but oxidation and structural degradation beginning near 80 °C limit their stability at higher temperatures. The findings provide insight into the temperature-dependent structural evolution of graphene under boundary-lubricated conditions and suggest the need for stabilization strategies to extend its high-temperature tribological performance.
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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) | 0.002 | 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".