Tribological Capabilities of Graphene and Titanium Dioxide Nano Additives in Solid and Liquid Base Lubricants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it