MnO<sub>2</sub>-Carbon Nanotube Electrodes for Supercapacitors with High Active Mass Loadings
Bibliographic record
Abstract
MnO 2 -carbon nanotube electrodes with high active mass loadings for supercapacitors have been fabricated with the goal of achieving a high area normalized capacitance, low impedance and enhanced capacitance retention at high charge-discharge rates. Interface synthesis and liquid-liquid extraction of MnO 2 particles produced non-agglomerated MnO 2 particles which allowed the fabrication of electrodes with good dispersion of carbon nanotubes in the MnO 2 matrix. This strategy was used to fabricate electrodes with active mass loadings in the range of 21–50 mg cm −2 and mass ratios of active material to the nickel foam current collector of 0.33–0.78. The comparison of the experimental data for different extractor molecules provided an insight into the influence of the molecular structure, adsorption mechanism and interface phenomena on particle size and electrode performance. The analysis of capacitance data at different charge-discharge rates and different mass loadings was utilized to optimize electrode performance. The highest capacitance of 7.52 F cm −2 was achieved at a scan rate of 2 mV s −1 and active mass loading of 47 mg cm −2 . Electrodes with mass loading of 35 mg cm −2 showed improved capacitance retention at high scan rates and the highest capacitance of 2.63 F cm −2 at a scan rate of 100 mV s −1 .
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".