Graphene as an additive in complex lithium grease: A comprehensive analysis of friction, wear and thermal behaviour
Bibliographic record
Abstract
This study investigates the tribological performance of graphene-enhanced complex lithium greases, focusing on friction reduction, wear resistance, and thermal stability. Various weight percentages of graphene (0.5 wt%, 0.75 wt%, 1 wt% 2 wt%) were added into the grease matrix, and their effects were evaluated through multiple experimental tests, including the four-ball wear test, thermal stability assessments and water resistance tests. The results demonstrated that lower graphene concentrations, particularly 0.5 wt%, offered the best balance of performance, providing significant reductions in friction and wear while improving thermal stability and water resistance. Higher concentrations, while improving thermal stability, exhibited diminishing returns in tribological performance due to agglomeration. This research highlights the potential of graphene as a lubricant additive for industrial applications, especially in environments requiring high thermal resistance and mechanical stability. Future work should focus on opti-mizing dispersion techniques and exploring the synergy between graphene and other nanomaterials to further enhance grease 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".