Bibliographic record
Abstract
Abstract Drop impact on a heated surface not only displays intriguing flow motion but also plays a crucial role in various applications and processes. We examine the impact dynamics of a water drop on both heated flat and nanostructured surfaces, with a wide range of impact velocity (V) and surface temperature (Ts) values. Via high-speed imaging and temperature measurements, we construct phase diagrams of different impact outcomes on these heated surfaces. Like those on the heated flat surface, water drops can deposit, spread, rebound, or break-up with atomizing on the heated nanostructures as V and Ts are increased. We find a significant influence of nanostructures on the impact dynamics by generating particular events in specific parameter ranges. For example, events of splashing, gentle central jetting, and violent central jetting are observed on and thus triggered by the heated nanostructures. The heated nanotextures with high roughness can easily trigger the splashing and the central jetting. Our data of the normalized maximum spreading diameter for the heated surfaces display distinct trends at low and high Weber number (We) ranges, where We compares the kinetic to surface energy of the impacting droplet. Finally, compared with the flat surface, the dynamic Leidenfrost temperature (TLD) for We ≈ 10 is decreased (by ≈60 °C) by the high-roughness nanotextures. In addition, our experimental data of TLD is consistent with a model prediction proposed by balancing the droplet dynamic and vapor pressure.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".