Enhancing the Electrospinnability of Low Molecular Weight Polymers Using Small Effective Cross-Linkers
Bibliographic record
Abstract
The electrospinning of continuous fibers normally requires the presence of a network of topological entanglements in solution, limiting the spinnability of low molecular weight polymers. Here, we show that a supramolecular approach can improve, and even render possible, the electrospinning of low molecular weight polymers, illustrated here with poly(4-vinylpyridine) (P4VP), by creating effective (i.e., physical) cross-links via hydrogen bonding or coordination interactions in solution using small molecules. The addition to P4VP solutions in dimethylformamide of 4,4′-biphenol (BiOH), which hydrogen bonds to P4VP, and nitromethane, a poor solvent for P4VP that increases BiOH hydrogen bonding to P4VP, decreases the concentration needed to prepare fibers of 50 kg/mol P4VP by a factor of 2 and enables the formation of unbeaded fibers. Hydrogen bonding in solution is quantified by infrared spectroscopy, and the impact of the supramolecular interactions on the P4VP concentration needed to form a physical network is shown by rheological studies. BiOH can also be removed by sublimation without damaging the fibers. Replacing BiOH by 4-hydroxy-4′-biphenylcarboxylic acid (HBCA), whose acid group hydrogen bonds more strongly than OH to P4VP, or by just 1% ZnCl 2 (relative to pyridine), which promotes metal coordination interactions with P4VP, improves the cross-linking efficiency still further. Most spectacularly, HBCA enables the electrospinning of unbeaded fibers of P4VP with the very low molecular weight of 5.2 kg/mol, which is well below the entanglement molecular weight. These results establish the supramolecular cross-linking approach as a powerful strategy for preparing nanofibers of pure polymers having limited electrospinnability.
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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".