Spatial Control of Condensation and Freezing on Superhydrophobic Surfaces with Hydrophilic Patches
Bibliographic record
Abstract
Abstract Certain natural organisms use micro‐patterned surface chemistry, or ice‐nucleating species, to control water condensation and ice nucleation for survival under extreme conditions. As an analogy to these biological approaches, it is shown that functionalized, hydrophilic polymers and particles deposited on the tips of superhydrophobic posts induce precise topographical control over water condensation and freezing at the micrometer scale. A bottom‐up deposition process is used to take advantage of the limited contact area of a non‐wetting aqueous solution on a superhydrophobic surface. Hydrophilic polymer deposition on the tips of these geometrical structures allows spatial control over the nucleation, growth, and coalescence of micrometer‐scale water droplets. The hydrophilic tips nucleate water droplets with extremely uniform nucleation and growth rates, uniform sizes, an increased stability against coalescence, and asymmetric droplet morphologies. Control of freezing behavior is also demonstrated via deposition of ice‐nucleating AgI nanoparticles on the tips of these structures. This combination of the hydrophilic polymer and AgI particles on the tips was used to achieve templating of ice nucleation at the micrometer scale. Preliminary results indicate that control over ice crystal size, spatial symmetry, and position might be possible with this method. This type of approach can serve as a platform for systematically analyzing micrometer‐scale condensation and freezing phenomena, and as a model for natural systems.
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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".