Highly Oxidized Graphene Anchored Ni(OH)<sub>2</sub> Nanoflakes as Pseudocapacitor Materials for Ultrahigh Loading Electrode with High Areal Specific Capacitance
Bibliographic record
Abstract
The contact between Ni(OH) 2 and graphene oxide (GO) determines the specific capacitance, high-rate performance, and stability of Ni(OH) 2 -GO composites when they were used as capacitive materials with high/ultrahigh material loading. To improve this contact, the exfoliated GO from Hummers’ method is oxidized twice for anchoring Ni(OH) 2 nanoflakes. The X-ray photoelectron spectroscopy (XPS) results reveal that the further oxidation process increases the carbonyl (C–O) groups and oxygen content on the GO surface. The Ni(OH) 2 -GO composites were obtained through a simple hydrothermal process. Morphology and microstructure characterizations indicate that the further oxidation of GO improves the affinity of Ni(OH) 2 and GO via the increased surface groups on the GO. Due to the high conductivity and suitable structure, the Ni(OH) 2 anchored on the treated GO (Ni(OH) 2 /TGO) exhibits good capacitive performance and high areal specific capacitance. The Ni(OH) 2 /TGO exhibits high specific capacitance of 1236.4 F g –1 at 1.0 mV s –1 and 1374.8 F g –1 at 0.1 A g –1, respectively, which is higher than that of Ni(OH) 2 on the untreated GO. The capacitance retention of Ni(OH) 2 /TGO is 52.2% even at 10 A g –1, which is higher than that of Ni(OH) 2 /GO (48.8%). For the high conductivity, the specific capacitance is still 996.2 F g –1 at 1.0 A g –1 even with ultrahigh material loading of 12.48 mg cm –2, which can be transferred to 12.06 F cm –2 calculated by areal specific capacitance. Furthermore, low deterioration is observed in Ni(OH) 2 /TGO (8.8% loss) after 1000-cycle charge–discharge test at 1.0 A g –1, which is lower than that of Ni(OH) 2 /GO (19.5% loss). The asymmetric supercapacitor, using the Ni(OH) 2 /TGO and activated carbon as the positive material and negative material, respectively, exhibits high energy density of 22.5 Wh kg –1 at 86.3 W kg –1 and 17.8 Wh kg –1 even at 4.05 kW 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".