Wetting of Hydrophilic Electrospun Mats Produced by Blending SEBS with PEO–PPO–PEO Copolymers of Different Molecular Weight
Bibliographic record
Abstract
The interaction of electrospun mats with water is critical for many possible applications, and the water contact angle on the surface is the parameter usually measured to characterize wetting. Although useful for hydrophobic surfaces, this approach is limited for hydrophilic mats, where wicking also has to be considered. In this case, it is still unclear how the fiber surface chemical composition and morphology will affect the wetting behavior of electrospun mats. In this work, wetting was studied with different hydrophilic membranes produced by blending thermoplastic elastomer poly(styrene)-b-poly(ethylene-butylene)-b-poly(styrene) (SEBS) with amphiphilic poly(ethylene oxide)-b-poly(propylene oxide)-b-poly(ethylene oxide) (PEO-PPO-PEO) molecules. Three different types of PEO-PPO-PEO, with different molar masses, PEO content, and physical form were used. The effect of these differences on the wetting behavior of the electrospun mats was evaluated by contact angle goniometry, wicking measurements, and different imaging techniques. X-ray photoelectron spectroscopy was used to characterize the surface chemical composition. The smaller molecules quickly saturated the surface at low concentrations, making the mats hydrophilic. The sheath of PEO-PPO-PEO also resulted in fast absorption of water, when comparing the saturated and nonsaturated surfaces. Longer PEO chain-ends seemed to hinder complete segregation and also led to a higher activation time when in contact with water. Liquid PEO-PPO-PEO was easily leached by water.
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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".