Fabricating Tunable Superhydrophobic Surfaces Enabled by Surface‐Initiated Emulsion Polymerization in Water
Bibliographic record
Abstract
Abstract Fabricating controllable superhydrophobic surfaces remains challenging in various fields ranging from chemical industries to biomedical engineering. Conventional methods commonly require volatile organic solvents and the assistance of special surface deposition and modification equipment, which are detrimental to environment and limit their applications in micro‐devices. Herein, an equipment‐free method is reported to directly transform fluorinated monomer micro‐droplets into hydrophobic polymer particles on flat substrate surfaces in water, simultaneously depositing hydrophobic coatings with tunable surface structures. The as‐prepared surfaces show superior superhydrophobicity and great stability in extreme conditions (e.g., varying acidity, basicity, and heating conditions), and excellent anti‐fouling property. Meanwhile, surface hydrophobicity can be manipulated by adjusting emulsion droplet number density and reaction time. Hence, superhydrophobic surfaces with tunable hydrophobicity gradients have been successfully fabricated in one pot. This study provides an equipment‐free method to facilely fabricate controllable superhydrophobic surfaces, with great potential in the development of smart superhydrophobic materials in various engineering and industrial applications.
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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".