Influence of Surface Wettability on Bubble Formation and Motion
Bibliographic record
Abstract
Bubble dynamics plays an important role in boiling heat transfer, and surface wettability affects bubble behaviors. In the present work, the effects of surface superhydrophilicity (SHI) and superhydrophobicity (SHO) on bubble dynamics are experimentally studied by observing the formation and motion behaviors of air bubbles and vapor bubbles on varied surfaces. For air bubbles to better mimic vapor bubbles, the air bubbles are introduced in a water pool by injecting airflow from a through hole of the surface. Air bubble tests are first conducted on homogeneous SHO and SHI surfaces, respectively. It is observed that surface wettability significantly affects the bubble size and departure frequency. To discover the dynamic behaviors of a bubble under both SHI and SHO, a biphilic surface with SHI and SHO areas is fabricated, and air bubbles are injected right on the biphilic border between the two areas. It is observed the wettability contrast significantly displaces the air bubbles, which spread only onto the SHO area. The biphilic surface is fabricated for the pool boiling test. Vapor bubbles are observed at different stages of the nucleate boiling, showing surface effects similar to the observations of air bubbles. Not only does this study present the influence of surface wettability on air and vapor bubble behaviors but also it provides useful implications for understanding and optimizing the biphilic surface design for enhancing boiling heat transfer.
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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".