Graphene-nickel cobaltite nanocomposite asymmetrical supercapacitor with commercial level mass loading
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.001 | 0.001 |
| 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 | 0.002 |
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 it