Friction and wear properties of base oil enhanced by different forms of reduced graphene
Bibliographic record
Abstract
In this study, the tribological effects of three different forms of reduced graphene oxide (rGO)-2D nano-additives in base oil were investigated. Reduced graphene oxide nanoplatelets were manufactured using a modified Hummers’ method. However, different filtration methods were used to obtain rGO nanoplatelets at three different bulk densities. After adding nano-additives to the base oil at 0.01%w/w concentration, physical and chemical characterization tests were performed such as viscosity test, four-ball wear test, rotating pressure vessel oxidation test (RPVOT), resistivity test and friction coefficient test. The presented results show that material-1 with the lowest bulk density and less lattice defect can perform better by reducing wear of the material by 10.63% as well as the coefficient of friction (COF) by 6.3% with respect to the base oil and under test conditions. The presented results show the promising effect of rGO as nano-additives to fluid lubricants on wear preventive properties without compromising the physical and chemical characteristics of the lubricants.
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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) | 0.000 | 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".