Enhancing tribological performance: A comprehensive review of graphene-based additives in lubricating greases
Bibliographic record
Abstract
• Comparison of studies on graphene, GO, and rGO in various grease formulations • Chemical modifications enhance graphene dispersion and agglomeration control • Tribological outcomes vary by particle size, concentration and grease type • Visual collations synthesize and compare results across multiple studies on greases • Recommendations address industrial applications and sustainable grease designs The integration of carbon-based additives, such as graphene, graphene ox- ide (GO), and reduced graphene oxide (rGO), into lubricating greases has attracted significant interest in the field of tribology. These materials exhibit unique properties such as exceptional mechanical strength, low interlayer shear resistance, and high thermal conductivity, which act to enhance the performance of lubricating greases. This review paper explores grease formation, types, and performance, focusing on the potential advantages and limitations of graphene derivatives as lubricant additives. Graphene has been shown to reduce friction and wear, improve load-carrying capacity, and enhance thermal stability through various research projects. Despite the promising results, challenges such as effective dispersion, scalability of synthesis, and grease structure compatibility remain. This paper provides a comprehensive overview of current research, highlighting the benefits, limitations, and future directions for graphene-based additives in lubricating greases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".