Freezing of a spreading droplet
Bibliographic record
Abstract
HYPOTHESIS: The wettability of a droplet on pre-cooled surface can be predicted by the Overall Energy Balance (OEB) approach incorporating factors such as inertial, viscous, surface energy, gravitational energy, and heat transfer. For a critical Stefan number, it is also hypothesized that nucleation or recalescence occurs more rapidly, while total droplet freezing is delayed, a behavior attributed to the cessation of three-phase contact line (TPCL) movement. EXPERIMENTS: In this study, we have employed a jet-based deposition technique to ensure that the deposited droplet is free from any unwarranted external body forces. The substrates examined include copper, aluminum, brass, and stainless steel selected for their varying thermal conductivities. Substrate temperatures ranged from -20C∘-0C∘. High-speed and infrared thermal cameras were utilized to capture the physical phenomena during the droplet deposition and freezing. FINDINGS: The theoretical model, developed using the OEB approach, closely matched the experimental observations, validating the hypothesis. The study demonstrated that dimensionless numbers, including Weber, Reynolds, Bond, Stefan, and Peclet numbers, govern droplet spreading on pre-cooled substrates. It was confirmed that supercooling effects are negligible when droplets are deposited using a jet-based technique. The spreading rate of a freezing droplet was found to be proportional to the substrate temperature, with slower rates observed at lower temperatures. Additionally, the total freezing time of the droplet was the highest at -20C∘, despite nucleation occurring fastest at this temperature.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".