Influence of Capping Agents on the Synthesis of Mn<sub>3</sub>O<sub>4</sub> Nanostructures for Supercapacitors
Bibliographic record
Abstract
Nanotechnology offers powerful strategies for the synthesis of advanced materials for supercapacitors. It is hypothesized that the size reduction of Mn 3 O 4 nanoparticles can eliminate time-consuming electrochemical activation and increase the electrochemical pseudocapacitance of this material. Moreover, due to the redox properties and specific features of its chemical synthesis procedure, Mn 3 O 4 can potentially outperform other promising cathode materials for energy storage in supercapacitors. A facile room temperature method to fabricate Mn 3 O 4 nanoparticles is described, which is based on the application of advanced capping agents (CAs) for nanofabrication. Building on the strong adsorption power of the catechol ligand, we utilize tetrahydroxy-1,4-quinone, catechin, and gallocyanine as CAs for the preparation of Mn 3 O 4 . The use of the catecholate molecules as CAs for chemical precipitation facilitates the preparation of Mn 3 O 4 platelet nanoparticles with a typical size of 5 nm. The reduction of the particle size allows the fabrication of advanced Mn 3 O 4 multiwalled carbon nanotube cathodes with 40 mg cm –2 active mass (AM), which show a significant increase in capacitance in a 0.5 M Na 2 SO 4 electrolyte. The highest capacitances of 7.03 F cm –2 (175.8 F g –1 ) at a potentiodynamic sweep rate of 2 mV s –1 and 9.13 F cm –2 (228.3 F g –1 ) at a constant current density of 3 mA cm –2 are obtained at a low impedance using gallocyanine as a CA. Obtained electrodes outperform MnO 2 -based cathodes of similar mass. Another important finding is the possibility to avoid the time-consuming activation process for Mn 3 O 4 -based electrodes. Analysis of the testing results provides evidence of the influence of the CA structure on the electrode performance. The results of this investigation pave the way for the application of Mn 3 O 4 in advanced high-AM supercapacitor cathodes.
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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 | 0.001 |
| 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".