Tailoring the Morphology and Orientation in Immiscible Binary Polymer Blends during Melt-Electrospinning
Bibliographic record
Abstract
This work constitutes the first comprehensive study on morphology development in melt-electrospun fibers prepared from binary immiscible polymer systems, and in this particular work, we consider a model system composed of polystyrene (PS) and poly(ε-caprolactone) (PCL). The results demonstrate that the levels of alignment and stretching of the dispersed phase in the resulting fibers can be controlled by carefully tuning material properties such as the composition and viscosity ratio of the phases as well as processing parameters such as the applied voltage and drum collector speed. The addition of a PS- b -PCL block copolymer solves the critical issue of process instability caused by morphology coarsening, especially for co-continuous systems, by saturating and stabilizing the PS/PCL interface. As a result, the fibrils formed inside the fibers are more homogeneous and display a smaller diameter, compared to the uncompatibilized system, a topic largely unexplored in fiber-forming processes. By extracting next the PCL phase, highly porous and self-supporting fibers composed of sub-μm, interconnected and intertwined PS fibrils are obtained, with a specific surface 35× higher compared to the initial fibers, an important feature for applications requiring high surface areas and/or textured surfaces such as catalysis, scaffolds for cell culture, filtration, and surface adsorption. Overall, this work exposes for the first time the comprehensive interplay between composition, interface, viscosity ratio, and processing parameters in an elongational flow-field type of environment associated with the formation of polymer microfibers.
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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".