A Simple Trick to Increase the Areal Specific Capacity of Polypyrrole Membrane: The Superposition Effect of Methyl Orange and Acid Treatment
Bibliographic record
Abstract
Polypyrrole (PPy) is one of the attractive conducting polymers that have been investigated as energy storage materials in devices like supercapacitors. Previously, we have reported a free-standing soft PPy membrane synthesized through interfacial polymerization in which methyl orange (MO) and ferric chloride were used as nano template and oxidant. In this work, we report that the presence of MO and the treatment of the PPy–MO membrane with sulfuric acid can dramatically increase the specific capacitance of the membrane. The properties of the membranes were evaluated using scanning electron microscope (SEM) for morphology, Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) for chemistry, thermogravimetric analysis (TGA) for thermal stability, and cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) for electrochemical activity. It was found that the areal specific capacitance of the PPy membrane increased from 2226 mF/cm2 to 6417 mF/cm2 and the charge transfer resistivity decreased from about 17 Ω to 3 Ω between 10,000 and 0.1 Hz due to the presence of MO and the acid treatment. It is likely that the superposition effect of MO and acid treatment helped the charge transfer process and consequently enhanced the charge storage performance and specific capacitance of the PPy membrane.
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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 | 0.001 |
| 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".