Ultrasound‐Assisted Deposition of Sepia Melanin and Multiwalled Carbon Nanotubes on Carbon Cloth: Toward Sustainable Surface Engineering for Flexible Supercapacitors
Bibliographic record
Abstract
Abstract The rising global demand for energy requires, among others, sustainable energy storage devices. Biosourced redox‐active molecules are interesting for eco‐designed electrochemical energy storage as they increase the energy density of the electrodes adding the Faradaic (redox) storage mechanism to the electrostatic one. The engineering of the electrode surface and electrode surface/molecule interface is key to optimizing storage. Here, (i) electrodes prepared by ultrasound‐assisted modification of carbon cloth in the presence of Sepia melanin, a quinone macromolecule, and multiwalled carbon nanotubes (MWCNTs) and (ii) their use in flexible symmetric electrochemical capacitors assembled with polyvinyl alcohol (PVA)‐based hydrogel electrolyte is reported. Electrodes exhibit an areal capacitance as high as 274 mF cm−2. Corresponding semi‐solid‐state symmetric supercapacitors feature high energy density of 18 Wh kg−1, power density up to 221 W kg−1 (evaluated at 0.5 A g−1), outstanding cycling stability (100% capacitance retention, and 100% Coulombic efficiency after 10 000 cycles) along with excellent flexibility. This work contributes to the development of sustainable surface engineering approaches for environmentally benign electrochemical energy storage devices.
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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".