Behavior of Surface-Functionalized Multiwall Carbon Nanotube Nanofluids during Phase Change from Liquid Water to Solid Ice
Bibliographic record
Abstract
Multiwall carbon nanotube (MWCNT) nanofluids have been shown to enhance the crystallization process of water to ice. While the beneficial effects of MWCNTs on phase change processes are well-documented, little work has been conducted to investigate the behavior of MWCNTs during and after exposure to freezing conditions. In this work, the crystallization morphology of water droplets containing surface-functionalized hydrophilic MWCNTs was evaluated at three driving force temperatures and two concentrations of nanofluid. At low supercoolings, the MWCNTs are completely expelled from the crystal matrix due to slow solidification rates. At high supercoolings, the MWCNTs are embedded in the solid droplet within air volumes and interdendritic regions as a result of rapid crystallization speeds. The results show that the dispersion of MWCNTs within the solid ice matrix itself was not achieved at these levels of supercooling. Under all conditions, freezing of the colloidal system results in destabilization of the MWCNTs and loss of dispersion. These effects are important considerations for applications requiring successful freeze/thaw cycling of nanofluid systems as well as in the storage and transport of colloidal suspensions.
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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".