Fabrication of conductive polymer nanofibers through SWNT supramolecular functionalization and aqueous solution processing
Bibliographic record
Abstract
Polymeric thin films and nanostructured composites with excellent electrical properties are required for the development of advanced optoelectronic devices, flexible electronics, wearable sensors, and tissue engineering scaffolds. Because most polymers available for fabrication are insulating, one of the biggest challenges remains the preparation of inexpensive polymer composites with good electrical conductivity. Among the nanomaterials used to enhance composite performance, single walled carbon nanotubes (SWNTs) are ideal due to their unique physical and electrical properties. Yet, a barrier to their widespread application is that they do not readily disperse in solvents traditionally used for polymer processing. In this study, we employed supramolecular functionalization of SWNTs with a conjugated polyelectrolyte as a simple approach to produce stable aqueous nanotube suspensions, that could be effortlessly blended with the polymer poly(ethyleneoxide) (PEO). The homogeneous SWNT:PEO mixtures were used to fabricate conductive thin films and nanofibers with improved conductivities through drop casting and electrospinning. The physical characterization of electrospun nanofibers through Raman spectroscopy and SEM revealed that the SWNTs were uniformly incorporated throughout the composites. The electrical characterization of SWNT:PEO thin films allowed us to assess their conductivity and establish a percolation threshold of 0.1 wt% SWNT. Similarly, measurement of the nanofiber conductivity showed that the electrospinning process improved the contact between nanotube complexes, resulting in conductivities in the S m(-1) range with much lower weight loading of SWNTs than their thin film counterparts. The methods reported for the fabrication of conductive nanofibers are simple, inexpensive, and enable SWNT processing in aqueous solutions, and offer great potential for nanofiber use in applications involving flexible electronics, sensing devices, and tissue engineering scaffolds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".