3D Network of Sepia Melanin and N‐ and, S‐Doped Graphitic Carbon Quantum Dots for Sustainable Electrochemical Capacitors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Organic electrode materials operating in aqueous electrolytes offer the opportunity to avoid toxic, critical, and expensive materials for electrochemical energy storage. When deposited on carbon current collectors, redox active organic materials add faradaic to electrostatic capacitance contribution to the electrodes. Here, a 3D network electrode material is reported upon, based on sepia melanin, a quinone macromolecule, and nitrogen‐ and sulfur‐doped graphitic carbon quantum dots (N,S GCQDs) designed to achieve good electronic conductivity and electrolyte wettability. The effect of various undoped and doped carbon quantum dots is also investigated, synthesized from acetic acid and sucrose instead of graphite, on the electrochemical performance of sepia melanin. Sepia/N,S GCQD shows optimum areal capacitance (≈180 mF cm −2 ) that is about twice as high as sepia (≈77 mF cm −2 ) with lower charge transfer resistance (1 ohm for sepia/N,S GCQDs compared to 10 ohms for sepia). The sepia/N,S GCQD symmetric supercapacitor in 0.5 m Na 2 SO 4(aq) exhibits promising capacitance retention ≈92% after 10 000 cycles at 5 A g −1 , 100% coulombic efficiency, 11 µW h cm −2 and 102 mW cm −2 maximum energy and power densities. The work paves the way for stable and potentially biodegradable supercapacitor electrode materials for environmentally benign electrochemical energy storage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it