Novel conductive hydrogels incorporating conjugated polymer-single-walled carbon nanotube complexes as network reinforcer and conductive filler
Bibliographic record
Abstract
Conductive hydrogels incorporating single-walled carbon nanotubes (SWNTs) as conductive fillers are rarely reported, as their poor solution processability poses a major challenge for uniform integration into hydrogel networks. Dispersing SWNTs with conjugated polymers (CPs) improves their solution processability but typically yields dilute dispersions in organic solvents that are difficult to convert into hydrogels. Additionally, the insulating sidechains on CPs often diminish the conductivity of the final material. To address these limitations, we employed a CP featuring self-immolative linkers (SILs) in its sidechains to disperse SWNTs in N, N-dimethylformamide. This dispersion was then combined with a concentrated polyvinyl alcohol solution in dimethyl sulfoxide to form a uniform composite. After concentration via centrifugation and solvent exchange into water, a stable hydrogel was formed through multiple freeze–thaw cycles. The SILs enabled rapid and clean removal of the polymer sidechains upon treatment with tetra- n-butylammonium fluoride (TBAF), resulting in an approximately threefold enhancement in hydrogel conductivity. Moreover, the sidechain cleavage products contributed to hydrogen bonding within the hydrogel network, counteracting the chaotropic effects of TBAF and reinforcing the hydrogel mechanically. This innovative strategy provides a versatile platform for fabricating conductive hydrogels from organic solvents and is readily adaptable to a wide range of polymeric systems, opening new avenues in functional hydrogel design.
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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 | 0.001 |
| 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".