Tribological Capabilities of Graphene and Titanium Dioxide Nano Additives in Solid and Liquid Base Lubricants
Bibliographic record
Abstract
In this study, the tribological behavior of both liquid (oil) and semi-liquid (grease) lubricants enhanced by multilayer graphene nano platelets and titanium dioxide nano powder was evaluated using ball-on-disk and shaft-on-plate tribo-meters. Oil samples for both 2D graphene nano platelets (GNP) and titanium nanopowders (TiNP) were prepared at three concentrations of 0.01 %w/w, 0.05 %w/w and 0.1 %w/w. In addition, 0.05% w/w mixtures of GNP and TiNP were prepared with three different ratios to analyze collective effects of both nano additives on friction and wear properties. For semi-liquid lubricants, 0.5% w/w concentrations were prepared for both nano additives for shaft-on-plate tests. Viscosity and oxidation stability tests were conducted on the liquid-base lubricants. Nano powders of both additive and substrate were analyzed using transmission electron microscopy (TEM) and scanning electron microscopy (SEM). In addition, Raman spectroscopy was conducted to characterize the graphene and titanium dioxide. The study shows that adding graphene and titanium dioxide individually sacrifices either the wear or friction of lubricants. However, use of both additives together can enhance friction resistance and wear preventive properties of a liquid lubricant significantly. For a semi-liquid lubricant, the use of both additives together and individually reduces friction compared to base grease.
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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".