Advanced Li-Ion Hybrid Supercapacitors Based on 3D Graphene–Foam Composites
Bibliographic record
Abstract
Li-ion hybrid supercapacitors (LIHSs) have recently attracted increasing attention as a new and promising energy storage device. However, it is still a great challenge to construct novel LIHSs with high-performance due to the majority of battery-type anodes retaining the sluggish kinetics of Li-ion storage and most capacitor-type cathodes with low specific capacitance. To solve this problem, 3D graphene-wrapped MoO 3 nanobelt foam with the unique porous network structure has been designed and prepared as anode material, which delivers high capacity, improved rate performance, and enhanced cycle stability. First-principles calculation reveals that the combination of graphene dramatically reduces the diffusion energy barrier of Li + adsorbed on the surface of MoO 3 nanobelt, thus improving its electrochemical performance. Furthermore, 3D graphene-wrapped polyaniline nanotube foam derived carbon is employed as a new type of capacitor-type cathode, demonstrating high specific capacitance, good rate performance, and long cycle stability. Benefiting from these two graphene foam-enhanced materials, the constructed LIHSs show a wide operating voltage range (3.8 V), a long stable cycle life (90% capacity retention after 3000 cycles), a high energy density (128.3 Wh·kg –1 ), and a high power density (13.5 kW·kg –1 ). These encouraging performances indicate that the obtained LIHSs may have promising prospect as next-generation energy-storage devices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 0.001 |
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".