The impact of polypyrrole:carboxymethyl cellulose composite nanostructure on conductivity and capacitance
Bibliographic record
Abstract
Polypyrrole is a popular conjugated polymer that becomes highly conductive in its p-doped state. The intrinsically non-polar nature of the conjugated bond network limits the processing options of such polymers. Surfactants and polyanions help increase dispersity of conductive polymer in suitable solvents for processing. However, while such dispersions can be highly stable, they are formed by complex nanostructures that significantly impact the conducting polymer composite’s bulk properties. Similarly, complex nanostructures can be formed when surfactants or polyelectrolytes serve as templates during the polymerization of the conducting polymer precursor. Following the report of polypyrrole:carboxymethyl cellulose composites in battery electrodes as conductive binders, we are here investigating the role that nanostructure control can have in optimizing their performance. Using methyl orange as a structural template, we can control the composite’s nanoscopic shape between nanospheres and nanofibers. In the bulk material, the latter gives rise to significantly increased electronic conductivity and capacitance in battery use conditions, underlining the opportunities for improved performance of conducting polymer composites by controlling synthesis conditions.
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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) | 0.000 | 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) | 0.002 | 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".