Influence of Dopants on Performance of Polypyrrole Coated Carbon Nanotube Electrodes and Devices
Bibliographic record
Abstract
Polypyrrole (PPy) and PPy-multiwalled carbon nanotube (MWCNT) composites were prepared for charge storage application in electrodes of electrochemical supercapacitors (ES). Chemical polymerization of PPy was performed using 4-amino-5-hydroxynaphthalene-2,7-disulfonic acid monosodium salt hydrate (AHDA) and diamine green black (DAGB) as new anionic dopants. It was found that DAGB improved colloidal stability of MWCNT dispersions and allowed the fabrication of PPy coated MWCNT. Testing results provided an insight into the influence of the chemical structure of the dopants on the microstructure of the PPy-MWCNT composites and electrochemical performance. The PPy-MWCNT composites showed significant improvement in capacitance, compared to pure PPy electrodes for active mass loading of 10 mg cm −2 . The highest capacitance of 220 F g −1 was achieved at a scan rate of 2 mV s −1 for PPy coated MWCNT, prepared using DAGB. The capacitance retention at 100 mV s −1 was found to be 60.9%. The integral and differential capacitances, calculated from the cyclic voltammetry and impedance spectroscopy data were analyzed at different conditions. The PPy coated MWCNT, prepared using DAGB, were used for the fabrication of symmetric devices, containing two similar PPy-MWCNT electrodes and asymmetric devices containing PPy-MWCNT positive electrode and vanadium nitride (VN)-MWCNT negative electrodes. The asymmetric devices offered advantages of higher capacitance, lower impedance, larger voltage window and improved power-energy characteristics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".