Simulations of Graphene Oxide Dispersions as Discotic Nematic Liquid Crystals in Couette Flow Using Ericksen-Leslie (EL) Theory
Bibliographic record
Abstract
The objective of this study was to simulate the flow of graphene oxide (GO) dispersions, a discotic nematic liquid crystal (DNLC), using the Ericksen-Leslie (EL) theory. GO aqueous suspension, as a lubricant, effectively reduces the friction between solid surfaces. The geometry considered in this study was two cylinders with a small gap size, which is the preliminary geometry for journal bearings. The Leslie viscosity coefficients calculated in our previous study were used to calculate the stress tensor in the EL theory. The behavior of GO dispersions in the concentration range of 15 mg/mL to 30 mg/mL, shown in our recent experiments to be in the nematic phase, was investigated to obtain the orientation and the viscosity profile. The viscosities of GO dispersions obtained from numerical simulations were compared with those from our recent experimental study, and we observed that the values are within the range of experimental uncertainty. In addition, the alignment angles of GO dispersions at different concentrations were calculated numerically using EL theory and compared with the respective theoretical values, which were within 1% error. The anchoring angles corresponding to viscosity values closest to the experimental results were between 114 and 118 degrees. Moreover, a sensitivity analysis was performed to determine the effects of different ratios of the elasticity coefficients in EL theory. Using this procedure, the same study could be extended for other DNLCs in different geometries.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".