Growth Dynamics During Dropwise Condensation on Nanostructured Superhydrophobic Surfaces
Bibliographic record
Abstract
Condensation on superhydrophobic nanostructured surfaces offers new opportunities for enhanced energy conversion, efficient water harvesting, and high performance thermal management. Such surfaces are designed to be Cassie stable, which minimize contact line pinning and allow for passive shedding of condensed water droplets at sizes smaller than the capillary length. In this work, we investigated in situ water condensation on superhydrophobic nanostructured surfaces using environmental scanning electron microscopy (ESEM). The “Cassie stable” surfaces consisted of silane coated silicon nanopillars with diameters of 300 nm, heights of 6.1 μm, and spacings of 2 μm, but allowed droplets of distinct suspended (S) and partially wetting (PW) morphologies to coexist. With these experiments combined with thermal modeling of droplet behavior, the importance of initial growth rates and droplet morphology on heat transfer is elucidated. The effect of wetting morphology on heat transfer enhancement is highlighted with observed 6× higher initial growth rate of PW droplets compared to S droplets. Consequently, the heat transfer of the PW droplet is 4–6× higher than that of the S droplet. To compare the heat transfer enhancement, PW and S droplet heat transfer rates are compared to that of a flat superhydrophobic silane coated surface, showing a 56% enhancement for the PW morphology, and 71% degradation for the S morphology. This study provides insight into importance of local wetting morphology on droplet growth rate during superhydrophobic condensation, as well as the importance of designing CB stable surfaces with PW droplet morphologies to achieve enhanced heat transfer during dropwise condensation.
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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".