Application of Rhamnolipids as Dispersing Agents for the Fabrication of Composite MnO2-Carbon Nanotube Electrodes for Supercapacitors
Bibliographic record
Abstract
The high theoretical capacitance of MnO2 renders it a promising material for the cathodes of asymmetric supercapacitors. The good dispersion of MnO2 and conductive additives in a nanocomposite electrode is a key factor for efficient electrode performance. This article describes, for the first time, the application of rhamnolipids (RL) as efficient natural biosurfactants for the fabrication of nanocomposite MnO2-carbon nanotube electrodes for supercapacitors. RL act as co-dispersants for MnO2 and carbon nanotubes and facilitate their efficient mixing, which allows for advanced capacitive properties at an active mass of 40 mg cm−2 in Na2SO4 electrolytes. The highest capacitance obtained from the cyclic voltammetry data at a scan rate of 2 mV s−1 is 8.10 F cm−2 (202.6 F g−1). The highest capacitance obtained from the galvanostatic charge–discharge data at a current density of 3 mA cm−2 is 8.65 F cm−2 (216.16 F g−1). The obtained capacitances are higher than the capacitances of MnO2-based electrodes of the same active mass reported in the literature. The approach developed in this investigation is simple compared to other techniques used for the fabrication of electrodes with high active mass. It offers advantages of using a biocompatible RL biosurfactant.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".