Conducting Polymers Doped with Bifunctional Copolymers for Improved Organic Batteries
Bibliographic record
Abstract
We propose a simple yet very versatile method to functionalize conducting polymers by the use of a bifunctional copolymer that can act as a redox-active dopant. A copolymer composed of 4-vinylcatechol and styrenesulfonic acid moieties was used as both the source of ions and the dopant for poly(3,4-ethylenedioxythiophene) (PEDOT) electropolymerization. The composite polymer shows an improvement in capacity which originates from the catechol faradaic reaction (52 mAh g –1 vs 18 mAh g –1 ) compared to PEDOT:poly(styrenesulfonate) (PSS). The active material utilization in the composite polymer was further investigated by using HClO 4 as a secondary dopant and by increasing the ratio of neutral 4-vinylcatechol in the bifunctional copolymer to obtain a higher energy density electrode. Characterization by X-ray diffraction and atomic force microscopy hints at phase separation between PEDOT and the doping copolymer. Consequently, 4-vinylcatechol electronic connection to PEDOT is weakened at the microscale which prevents its complete utilization. These findings show the complex interaction between a conducting polymer and its dopant. The possibility to further tune the bifunctional copolymer composition, structure, and polymerization strategy should lead to improved energy storage performances and other new functional materials that explore properties imbedded in molecular units.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".