Tribological and thermal performance of graphene-enhanced lithium-based greases: impact of concentration on friction, wear, and stability
Bibliographic record
Abstract
In this study, lithium-based greases enhanced with varying concentrations of graphene (0.5 wt.%, 1 wt.%, and 2 wt.%) were evaluated for their tribological and thermal performance. The Four Ball Wear Test, thermal imaging and thermogravimetric analysis (TGA) were used to assess the impact of graphene on friction reduction, wear resistance and thermal stability. The 0.5 wt.% graphene-enhanced grease demonstrated the most favourable results, with superior friction reduction, wear resistance and consistent lubrication over time. This is attributed to the uniform dispersion of graphene, which promoted the formation of a stable tribo-film and enhanced thermal conductivity. At higher concentrations (1 wt.% and 2 wt.%), graphene agglomeration led to diminished tribological performance, with increased friction and faster thermal degradation. TGA results further confirmed the superior thermal stability of the 0.5 wt.% sample, with delayed onset of decomposition compared to the other formulations. These findings suggest that a graphene concentration of 0.5 wt.% is optimal for improving the overall performance of lithium-based greases, providing a balance between friction reduction, thermal stability and wear resistance.
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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 | 0.001 |
| 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 | 0.001 |
| 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".