A Facile and Strategic Approach to Superhydrophobic Fibrous Structure with Biaxially Aligned Electrospun Porous Fibers
Bibliographic record
Abstract
Abstract Electrospun fibrous structures can be developed superhydrophobic while remaining breathable. However, current electrospinning‐based methods for developing superhydrophobicity require complex fabrication processes and multiple raw materials, lacking a facile approach to superhydrophobicity using electrospun fibers. Inspired by a cultural relic known as Plain Gauze Gown, a fibrous structure consisting two layers of porous fibers aligned in warp and weft directions by simple electrospinning is developed. Through investigation of wetting behavior, an unique Cassie–Baxter‐“restoring” (CaRe) wetting contributing to a stable Cassie–Baxter state is unveiled in the developed structure. The CaRe wetting ensures that droplet stays on the upper‐layer fibers even at a sparse inter‐fiber distance, enabling strategically reaching superhydrophobicity by lowering structure solidity. Remarkably, the developed structure with water contact angle between 159° and 162° and roll‐off angle from 10° to 3° has a water vapor transmission rate of 20.8 kg m−2 d−1, which is the highest value among all superhydrophobic electrospun structures reported, and it is also waterproof and semi‐transparent. These features make the structure suitable for a wide range of applications, including developing waterproof, breathable, and superhydrophobic membrane with simple preparation and low cost, and as a surface layer for wearable electronics that facilitates sweat evaporation and prevents water intrusion.
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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) | 0.000 | 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".