MoO<sub>2</sub> nanoparticles confined in N,P-codoped graphene aerogels with excellent pseudocapacitance performance
Bibliographic record
Abstract
In this work, MoO2@NPGA nanocomposites were successfully prepared via a simple hydrothermal and calcination route. The as-prepared MoO2@NPGA composites exhibit a synergistic effect between MoO2 and N,P-codoped graphene aerogels, which can significantly improve the electrochemical performance of the MoO2@NPGA electrodes. Moreover, the results also proved that the mass loading of MoO2 has a huge effect on the electrochemical properties of MoO2@NPGA composites. With an appropriate amount of MoO2, the MoO2@NPGA composite shows a high specific capacitance (335 F g−1 at 1 A g−1) and excellent cycle stability (capacitance remains at 88% after 6000 cycles). Furthermore, the assembled symmetric supercapacitor displays a high energy density of 23.75 W h kg−1 at a power density of 300 W kg−1 and can maintain an energy density of 17.1 W h kg−1 when the power density reaches up to 6005 W kg−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) | 0.000 | 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) | 0.000 | 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 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".