Nanofluid Drop Impact on Heated Surfaces
Bibliographic record
Abstract
We experimentally elucidate the impact dynamics of ethylene glycol (EG) droplets laden with both hydrophilic and hydrophobic SiO 2 nanoparticles (NPs) onto a flat heated surface in non-boiling, boiling, and Leidenfrost regimes. We use seven nanofluid concentrations ( C p ), ranging from 0.89 to 64.3 wt %, and control the surface temperature ( T s ) between 100 and 400 °C, while the nanofluid droplet’s impact velocity is constant at 0.22 ± 0.02 m/s. Phase diagrams of impact outcomes are established to illustrate the effect of the additive nanoparticles on the droplets’ impact dynamics, revealing that nanoparticles modify droplet impact behaviors differently in each regime. In the non-boiling regime, the droplet spreading profile remains unaffected by nanoparticles up to C p < 11.9 wt % before reaching the maximum spreading diameter (β max ). For nanofluid drops with higher nanofluid concentration, the increasing viscosity with concentration is likely to be the primary factor that affects the droplets’ spreading profile in the non-boiling regime T s ≲ T sat ≈ 200 °C, as the saturation temperature. In the boiling regime 200 °C < T s ≲ 350 °C, a small amount of nanoparticle addition ( C p = 0.89 wt %) promotes atomization regardless of nanoparticle wettability. Finally, manifested in a complete rebound due to an intervening vapor layer, the Leidenfrost temperature ( T L ) of the nanofluid droplets is affected by both nanofluid concentration and nanoparticles’ wettability. The nanofluid droplets' T L increases with higher nanofluid concentration; moreover, this Leidenfrost temperature increment is more significant for EG droplets laden with hydrophobic nanoparticles. Our results quantitatively reveal the significant influence of nanoparticle concentrations and wettability on drop spreading, impact outcome, and Leidenfrost temperature on heat surfaces, potentially benefiting applications in coating, spraying, and cooling.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".