Molecular orientation and solvent affinity in electrospun fibers of miscible blends
Bibliographic record
Abstract
The detailed structural characterization of electrospun fibers is crucial for understanding their processing-structure-properties relationships and optimizing their preparation. While many advanced applications of electrospun fibers incorporate multiple components, our current knowledge is predominantly based on one-component fibers, raising questions about its applicability to more complex materials. In this work, we investigate electrospun fibers composed of miscible blends of polystyrene (PS) with poly(2,6-dimethyl-1,4-phenylene oxide) (PPO) to identify the key factors that impact their structure. Confocal Raman microscopy is employed to quantify the molecular orientation of PS and PPO at the single fiber level. The results reveal that PPO is much more oriented than PS at all compositions, with a widening gap as the PPO content increases. This unexpected behavior for a miscible blend coincides with a broadening of the glass transition, attributed to increased composition fluctuations at higher PPO content. The results suggest that a difference in solvent affinity between the two polymers, where PPO is less solvated than PS, reduces the relaxation of PPO and promotes that of PS, especially at high PPO content. This work demonstrates that electrospun fibers of miscible blends do not behave as a mere average of the properties of their constituents. Instead, the relative polymer-solvent affinity emerges as a central factor shaping their molecular organization. • Molecular orientation in electrospun PS:PPO fibers deviates from expectations. • PPO orientation is high and rises with PPO content; PS orientation is negligible. • The orientation gap correlates with composition fluctuations in the miscible blend. • PPO's lower solvent affinity hinders relaxation and increases its orientation. • Relative polymer-solvent affinity is key to shaping the structure in binary fibers.
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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".