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 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 | 0.000 |
| 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 | 0.000 |
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".