Effect of structure on the tribology of ultrathin graphene and graphene oxide films
Bibliographic record
Abstract
The friction and wear properties of graphene and graphene oxide (GO) with varying C/O ratio were investigated using friction force microscopy. When applied as solid lubricants between a sliding contact of a silicon (Si) tip and a SiO2/Si substrate, graphene and ultrathin GO films (as thin as 1-2 atomic layers) were found to reduce friction by ∼6 times and ∼2 times respectively as compared to the unlubricated contact. The differences in measured friction were attributed to different interfacial shear strengths. Ultrathin films of GO with a low C/O ratio of ∼2 were found to wear easily under small normal load. The onset of wear, and the location of wear initiation, is attributed to differences in the local shear strength of the sliding interface as a result of the non-homogeneous surface structure of GO. While the exhibited low friction of GO as compared to SiO2 makes it an economically viable coating for micro/nano-electro-mechanical systems with the potential to extend the lifetime of devices, its higher propensity for wear may limit its usefulness. To address this limitation, the wear resistance of GO samples with a higher C/O ratio (∼4) was also studied. The higher C/O ratio GO was found to exhibit much improved wear resistance which approached that of the graphene samples. This demonstrates the potential of tailoring the structure of GO to achieve graphene-like tribological 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 | 0.001 |
| 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".