Compressible flow exfoliation of graphene : a multiscale study
Bibliographic record
Abstract
The field of two-dimensional (2D) nanomaterials has gained significant interest over the last few decades in numerous applications because of their unique properties that exhibit when a bulk material is reduced to its 2D form. A wide variety of 2D layered materials have been synthesized by a newly developed compressible flow exfoliation (CFE) process, which has considerable advantages over current top-down approaches. In this study, computational fluid dynamics (CFD) and classical molecular dynamics (MD) are used to investigate the interactions of gas particles with pristine, unfunctionalized graphene sheets during the CFE process and try to understand the atomistic mechanism of layer separation. The thermal vibration of graphene layers caused by the elevated temperature of a flowing gas medium can accelerate the exfoliation tendency. However, it is insufficient to overcome the binding energy between graphene layers while the gas particles are static. Therefore, a range of one-directional flow velocities is applied to the compressible fluids based on the experimental findings, and dispersion of graphene is observed when the velocity exceeds the supersonic flow condition. Analyzing the dynamic properties of exfoliation, it is established that sliding in the parallel direction is the preferable exfoliation mechanism of graphene than vertical separation. Furthermore, the upstream pressure plays a fundamental role in controlling the gas density and flow velocities during exfoliation. It is also observed that a heavier gas is less conducive for delaminating graphene than a lighter gas because of its higher atomic mass and lower flow rates at identical conditions. The findings of this study provide more flexibility to synthesize graphene and other 2D materials at a multitude of processing conditions using compressible gases.
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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 | 0.001 |
| 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".