Polypyrrole-Carbon Nanotube-FeOOH Composites for Negative Electrodes of Asymmetric Supercapacitors
Bibliographic record
Abstract
For the first time, polypyrrole (PPy) coated carbon nanotubes (CNT) were combined with FeOOH to fabricate negative supercapacitor (SCP) electrodes. The synergistic effects of PPy-CNT and FeOOH resulted in enhanced electrochemical performance at high active mass loading of 37 mg cm −2 in a voltage window of −0.8–+0.1 V versus a saturated calomel electrode. Particle extraction through liquid-liquid interface (PELLI) was utilized for the agglomerate-free processing of FeOOH, which improved FeOOH mixing with PPy-CNT and contributed to the enhanced capacitive behavior of the composite. Tetradecylamine (TA) was found to be an efficient extractor for FeOOH. Cyclic voltammetry and impedance spectroscopy data at different electrode potentials provided an insight into the synergistic effects of PPy-CNT and FeOOH, and influence of PELLI on electrode performance. An areal capacitance of 4.5 F cm −2 and nearly ideal capacitive behavior were achieved at a low electrode impedance. The important finding was that capacitances of the negative PPy-CNT-FeOOH and positive MnO 2 -CNT electrodes can be matched in different voltage windows to fabricate advanced asymmetric devices, which exhibited promising electrochemical performance in a voltage window of 1.6 V at high active mass.
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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) | 0.001 | 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".