Redox Active Organic-Carbon Composites for Capacitive Electrodes: A Review
Bibliographic record
Abstract
The pressing concerns of environmental sustainability and growing needs of clean energy have raised the demands of carbon and organic based energy storage materials to a higher level. Redox-active organic-carbon composites electrodes are emerging to be enablers for high-performance, high power and long-lasting energy storage solutions, especially for electrochemical capacitors (EC). This review discusses the electrochemical redox active organic compounds and their composites with various carbonaceous materials focusing on capacitive performance. Starting with the most common conducting polymers, we expand the scope to other emerging redox active molecules, compounds and polymers as well as common carbonaceous substrates in composite electrodes, including graphene, carbon nanotube and activated carbon. We then discuss the first-principles computational studies pertaining to the interactions between the components in the composites. The fabrication methodologies for the composites with thin organic coatings are presented with their merits and shortcomings. The capacitive performances and features of the redox active organic-carbon composite electrodes are then summarized. Finally, we offer some perspectives and future directions to achieve a fundamental understanding and to better design organic-carbon composite electrodes for ECs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".