Natural dropwise condensation of humid air on engineered flat surfaces: An experimental study
Bibliographic record
Abstract
This study investigates the natural dropwise condensation of humid air on various surfaces, with different material and textures. A comparative study is performed between micro and nanotextured surfaces, for the first time. The present study finds that microtextured superhydrophobic substrates can outperform nanotextured by two to three times for moist air condensation. This is attributed to the higher heat transfer area despite a higher contact angle hysteresis. The present study also proposes a novel correlation from over 700 experimental results for the condensation heat transfer coefficient considering relative humidity, humid air temperature, surface temperature, apparent contact angle, contact angle hysteresis, and inclination angle. Contact angle and its hysteresis were found to have optimal values in maximizing the heat transfer coefficient. Inclination angle and relative humidity were also substantial factors, with the vertical orientation outperforming the horizontal by up to 20%, and relative humidity found to linearly affect the heat transfer coefficient. These findings offer valuable insights into humid air dropwise condensation, particularly relevant for dehumidification and atmospheric water harvesting systems whose advancement relies heavily on condensation heat transfer coefficients. • Condensation on standard and engineered surfaces was experimentally investigated. • Micro surfaces outperformed nano-textured and standard surfaces by up to three times. • A novel correlation for dropwise condensation in humid air was developed. • An optimal apparent contact angle was observed for the highest condensation rate. • The inclination effect on condensing substrate performance was up to 20%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".