Effects of Reduced Graphene Oxide (rGO) at Different Concentrations on Tribological Properties of Liquid Base Lubricants
Bibliographic record
Abstract
In this study, reduced graphene oxide (rGO) nano platelets were used as an additive to enhance friction and wear properties of oil-based lubricants by preparing three samples at 0.01% w/w, 0.05% w/w, and 0.1% w/w concentrations. To analyze the direct effect of rGO nano platelets on tribological properties, 99.9% pure oil was used as a liquid lubricant. A comparative tribological study was done by performing a ball-on-disk wear test in situ under harsh conditions, which was further analyzed using a non-contact 3D optical profilometer. Morphological evaluation of the scar was done using transmission and scanning electron microscopy (TEM, SEM) at micro and nano levels. The lubricants’ physical properties, such as viscosity and oxidation number, were evaluated and compared for all samples including pure oil (control sample) as per ASTM standards. Findings of all these tests show that adding rGO nano platelets at 0.05% w/w showed significant reduction in friction at high speed and in wear up to 51.85%, which is very promising for increasing the life span of moving surfaces in machinery. Oxidation and viscosity tests also proved that adding rGO nano platelets to all samples does not sacrifice the physical properties of the lubricant, while it improves friction and wear properties.
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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".