Dispersant Molecules with Functional Catechol Groups for Supercapacitor Fabrication
Bibliographic record
Abstract
Cathodes for supercapacitors with enhanced capacitive performance are prepared using MnO2 as a charge storage material and carbon nanotubes (CNT) as conductive additives. The enhanced capacitive properties are linked to the beneficial effects of catecholate molecules, such as chlorogenic acid and 3,4,5-trihydroxybenzamide, which are used as co-dispersants for MnO2 and CNT. The dispersant interactions with MnO2 and CNT are discussed in relation to the chemical structures of the dispersant molecules and their biomimetic adsorption mechanisms. The dispersant adsorption is a key factor for efficient co-dispersion in ethanol, which facilitated enhanced mixing of the nanostructured components and allowed for improved utilization of charge storage properties of the electrode materials with high active mass of 40 mg cm−2. Structural peculiarities of the dispersant molecules are discussed, which facilitate dispersion and charging. Capacitive properties are analyzed using cyclic voltammetry, chronopotentiometry and impedance spectroscopy. A capacitance of 6.5 F cm−2 is achieved at a low electrical resistance. The advanced capacitive properties of the electrodes are linked to the microstructures of the electrodes prepared in the presence of the dispersants.
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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) | 0.001 | 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".