Impact of polydispersity on polymer solutions and electrospun nanofibers
Bibliographic record
Abstract
The extensional rheology of low-viscosity liquids is a field still in its infancy, yet it plays a critical role in numerous applications. One prominent example is the production of nanoscale fibers through electrospinning, where the rheological properties of polymer solutions are crucial for achieving consistent fiber formation and controlled morphologies. This study investigates the impact of polymer polydispersity on the rheological properties of solutions and their ability to produce electrospun nanofibers. Polymer blends with varying polydispersity but identical average molecular weights (Mw) were prepared using polystyrene (PS) dissolved in dimethylformamide (DMF) and polyethylene oxide (PEO) dissolved in water. The rheological behavior of these solutions was characterized using Capillary Breakup Extensional Rheometry (CaBER), and their processability was evaluated through electrospinning. Results demonstrate that solutions with higher polydispersity exhibit greater elasticity compared to those with narrower molecular weight distributions. Furthermore, electrospinning such solutions yielded nanoscale fibers of superior quality. This work underscores the critical role of elongational elasticity in producing high-quality nanofibers, which can be enhanced by increasing polymer polydispersity. Additionally, the CaBER rheometer proved to be an effective tool for optimizing the rheological properties of polymer solutions for electrospinning applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".