Nanowires with Conductivities Comparable to Their Bulk Films from an Electrospun Self-Doped Water-Soluble Conductive Polymer
Bibliographic record
Abstract
Electrospinning of conducting polymer blends, such as PEDOT:PSS, on a flexible substrate such as PDMS has been a practical approach to obtaining stretchable substrates that are conductive. However, the intrinsic conductivity of the doped polymer is often not preserved when it is electrospun as nanofibers. Knowing the PSS dopant leads to insulating domains in the nanofibers, this study aimed to eliminate this conductivity-limiting external dopant. A fully water-soluble, self-doped conductive polymer ( p(PDS) ), not requiring an external dopant, served to prepare nanofibers by electrospinning. This was to replace PEDOT:PSS in electrospinning nanofibers, whose conductivity could be on par with its corresponding bulk conductivity in thin films. Toward this goal, the effects of carrier polymer content, organic cosolvent, and pH on both the morphology and conductivity of the nanofibers were assessed. The sheet resistance of nanofibers electrospun from p(PDS) on PDMS tape improved >100-fold (4.5 × 10 4 Ω/sq) by adjusting the electrospinning solution to pH < 2 along with adding DMF as a cosolvent. Postelectrospinning dopant exchange treatment was not required for this improvement. The nanofibers electrospun on an elastomeric tape maintained a threshold conductivity when stretching the substrate, upward of 100%. Also, the original sheet resistance was restored upon releasing the applied stress/strain. The effects of electrospinning solution composition on the morphology of the conductive nanofibers provide key knowledge that can be used for preparing conductive stretchable substrates that potentially meet the mechanical and electrical requirements for their use in wearable electronics.
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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) | 0.000 | 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 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".