Growth Dynamics During Dropwise Condensation on Nanostructured Superhydrophobic Surfaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.003 | 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 it