Comparative study of tribological behaviours of different base greases enhanced by graphene nano platelets
Bibliographic record
Abstract
Graphene is one of the strongest allotropes in carbon family.Applications of graphene are found in many industries such as coating, sensors, electronics, light processing, energy, and environmental sectors.Myriad studies have proven that graphene has excellent tribological capabilities.In fact, multilayer structure of graphene allows layers to shear between the mating surfaces to reduce friction.In addition, its extensive mechanical properties allow graphene particles to work as nano bearings to mitigate metal-to-metal contact and reduce wear.In this study, graphene nano particles were evaluated at 1%w/w concentration using the lithium-base general purpose grease (NL-1), the water proof general purpose grease (NL-2) and the extreme pressure grease (NL-3).To characterize graphitic defects and topography of graphene platelets, micro-Raman spectroscopy and transmission electron microscopy (TEM) were utilized at high magnification.For tribological evaluation, shaft-on-plate tribometer was used to test grease at different loads and rotating speeds.The results show that all three nano greases have lower average friction coefficient (AFC) in comparison with control sample (pure grease).Among them, the water resistive grease (NL-2) has the best performance followed by the extreme pressure grease (NL-3) and the lithium based grease (NL-1) respectively.
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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".