Nanoengineering of NiO/MnO2/GO Ternary Composite for Use in High-Energy Storage Asymmetric Supercapacitor and Oxygen Evolution Reaction (OER)
Bibliographic record
Abstract
Designing multifunctional nanomaterials for high performing electrochemical energy conversion and storage devices has been very challenging. A number of strategies have been reported to introduce multifunctionality in electrode/catalyst materials including alloying, doping, nanostructuring, compositing, etc. Here, we report the fabrication of a reduced graphene oxide (rGO)-based ternary composite NiO/MnO2/rGO (NMGO) having a range of active sites for enhanced electrochemical activity. The resultant sandwich structure consisted of a mesoporous backbone with NiO and MnO2 nanoparticles encapsulated between successive rGO layers, having different active sites in the form of Ni-, Mn-, and C-based species. The modified structure exhibited high conductivity owing to the presence of rGO, excellent charge storage capacity of 402 F·g−1 at a current density of 1 A·g−1, and stability with a capacitance retention of ~93% after 14,000 cycles. Moreover, the NMGO//MWCNT asymmetric device, assembled with NMGO and multi-wall carbon nanotubes (MWCNTs) as positive and negative electrodes, respectively, exhibited good energy density (28 Wh·kg−1), excellent power density (750 W·kg−1), and capacitance retention (88%) after 6000 cycles. To evaluate the multifunctionality of the modified nanostructure, the NMGO was also tested for its oxygen evolution reaction (OER) activity. The NMGO delivered a current density of 10 mA·cm−2 at the potential of 1.59 V versus RHE. These results clearly demonstrate high activity of the modified electrode with strong future potential.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".