The behaviour of plasma-functionalized graphene nanoflake nanofluids during phase change from liquid water to solid ice
Bibliographic record
Abstract
Emerging nanofluid-based technologies for cooling, transport, and storage applications have previously been enhanced through the use of graphene nanoflake (GNF) nanofluids. Many of the beneficial effects of GNFs have now been documented, though little work has yet been completed to characterize the morphological behaviour of GNF nanofluids both during and after the phase change process. In this study, the crystallization behaviour of sessile water droplets was evaluated for two plasma-functionalized, hydrophilic GNF concentrations (20 and 100 ppm) at three driving force temperatures (-5 °C, -10 °C, and -20 °C). At low driving forces, the GNFs were wholly expelled from the solid matrix due to low crystallization velocities. At high driving forces, more rapid crystallization rates resulted in the entrapment of GNFs within the air bubbles and inter-dendritic spaces of the solid droplet. However, individual particle dispersion was not achieved within the solid matrix at any driving force. Furthermore, for all experimental conditions, the functionalized GNF clusters which formed during freezing did not disperse spontaneously upon melting as drying-like effects may have altered the attraction properties of their surfaces and destabilized the suspension. Compared to previous studies using multi-walled carbon nanotubes, the GNFs were found to have higher liquid mobility at the solid front, provide less resistance to that front as it ascended, and be better dispersed after melting. These effects may have been geometrical; the square nanoflake geometry does not result in any physical particle entanglement.
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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) | 0.001 | 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".