Nanostructured Manganese Oxide and Composite Electrodes for Electrochemical Supercapacitors
Bibliographic record
Abstract
Electrochemical supercapacitors (ES) are urgently needed as components in many advanced power systems. The development of advanced ES is expected to enable radical innovation in the area of hybrid vehicles and electronic devices. Nanostructured manganese oxides in amorphous or various crystalline forms have been found to be promising electrode materials for ES. The use of composite electrodes of manganese oxide with carbon nanotubes is being proposed to improve the overall electrochemical performance of the ES. Electrodeposition methods have been developed for the fabrication of manganese oxide films with/without carbon nanotubes for applications in ES. Electrolytic deposition of manganese oxides was found to be possible using Mn2+ and Mn7+ species, co-deposition of multi wall carbon nanotubes (MWNT) and manganese oxide using cathodic electrosynthesis was successfully achieved. Novel chemical process has been developed for the synthesis of nano-size manganese oxide particles. Electrophoretic deposition of the nano-size manganese oxide particles was able to be performed in both aqueous and non-aqueous solutions. Electrophoretic co-deposition of the nano-size manganese oxide particles with carbon nanotubes was successfully achieved. The mechanisms and kinetics of all the deposition methods are discussed. Charge storage properties of the films prepared by different deposition methods are investigated and compared.
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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".