Graphene-nickel cobaltite nanocomposite asymmetrical supercapacitor with commercial level mass loading
Bibliographic record
Abstract
A high performance asymmetric electrochemical supercapacitor with a mass loading of 10 mg·cm−2 on each planar electrode has been fabricated by using a graphene-nickel cobaltite nanocomposite (GNCC) as a positive electrode and commercial activated carbon (AC) as a negative electrode. Due to the rich number of faradaic reactions on the nickel cobaltite, the GNCC positive electrode shows significantly higher capacitance (618 F·g−1) than graphene-Co3O4 (340 F·g−1) and graphene-NiO (375 F·g−1) nanocomposites synthesized under identical conditions. More importantly, graphene greatly enhances the conductivity of nickel cobaltite and allows the positive electrode to charge/discharge at scan rates similar to commercial AC negative electrodes. This improves both the energy density and power density of the asymmetric cell. The asymmetric cell composed of 10 mg GNCC and 30 mg AC displayed an energy density in the range of 19.5 Wh·kg−1 with an operational voltage of 1.4 V. At high sweep rate, the system is capable of delivering an energy density of 7.6 Wh·kg−1 at a power density of about 5600 W·kg−1. Cycling results demonstrate that the capacitance of the cell increases to 116% of the original value after the first 1600 cycles due to a progressive activation of the electrode, and maintains 102% of the initial value after 10000 cycles.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".