Development of Adjustable High- to Low-Adhesive Superhydrophobicity Using Aligned Electrospun Fibers
Bibliographic record
Abstract
Superhydrophobic surfaces based on electrospun fibrous structures exhibit advantages of additive manufacturing and enable the passage of gases. Compared to randomly deposited fibers, directionally aligned fibers improve the control of surface wetting by a specified fiber orientation and predictable liquid–fiber contact interface. In this article, we create superhydrophobicity with adjustable adhesion based on the understanding of droplet wetting behavior on directionally aligned fibers. Directionally aligned polystyrene fibers with different diameters and interfiber distances ( l ) are produced using electrospinning with a rotating fin collector. The wetting behavior of droplets on the surfaces dressed by aligned fibers is characterized, and a thermodynamic model of wetting behavior is established to guide the experimental studies. As a result, high-adhesive superhydrophobicity is achieved on weak hydrophobic substrate surfaces dressed by aligned polystyrene fibers with a diameter of 1.8 μm and l between 5 and 130 μm. Water droplets (2 μL) exhibit a maximum contact angle of 156° and adhere to the fiber-dressed surfaces by tilting upside down. Low-adhesive superhydrophobicity is achieved by introducing an additional layer of aligned fibers to increase the transition energy barrier. On the dual-layer structure with an upper-layer l of 9 μm, droplets show a contact angle of 155° and can readily roll off the surface. Moreover, increasing the upper-layer l to 15 μm reserves the surface to high-adhesive superhydrophobicity.
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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 | 0.001 |
| 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 | 0.001 |
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".