Surface wettability of aligned electrospun micro- and nano-fibers
Bibliographic record
Abstract
The surface wettability of solid materials is important to many engineering applications. Structured surfaces with electrospun micro-and nano-fibers can effectively control the wettability of surfaces. The objective of this work is to quantify the influences of fiber parameters, including fiber radius and distribution, and inter-fiber distance, on the wettability of surfaces coated with aligned fibers. A thermodynamic model for the wetting process of a droplet across aligned fibers shows that the droplet needs to overcome a free energy barrier in wetting each fiber, and that the energy barrier increases with fiber radius and droplet basal width. Then the relationships between energy barriers and the preceding fiber parameters are experimentally validated. Results show that two adjacent aligned electrospun fibers, with a mean radius of 0.9 m, can pin a 2 L droplet from spreading to its equilibrium energy state by the energy barriers at an inter-fiber distance larger than 800 m. As the inter-fiber distance reduces below 800 m, the reduced energy barriers cannot pin the droplet so three fibers are needed. The minimum inter-fiber distance for three fibers is 400 m, and further reducing the inter-fiber distance needs more fibers to pin the droplet. For fibers with a mean radius of 0.45 m, the minimum inter-fiber distance for two fibers to pin the droplet increases to ~1050 m because of the reduced energy barrier. However, the energy barriers that have been overcome consume the energy of the droplet, so a contact angle greater than 150 can be maintained when the inter-fiber distance is below ~130 m. The findings of this research guide the wettability control of surfaces coated by electrospun fibers, and the design of "rose-petal" surfaces for applications such as droplet transportation.
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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".