Hybrid 3D Vertical Graphene Nanoflake and Aligned Carbon Nanotube Architectures for High-Energy-Density Lithium-Ion Batteries
Bibliographic record
Abstract
Here, we synthesized a type of three-dimensional (3D) carbon nanostructure through the plasma-enhanced chemical vapor deposition method, which was composed of carbon nanotubes (CNTs) and graphene nanoflakes (GNFs) embedded on the surface of CNTs. The CNTs have a typical hollow structure with an inner diameter of 15 nm, and the CNT@GNF were grown on vermiculite supported with an Fe–Mo catalyst. The diameter of CNTs and the amount of GNFs on the CNT surface can be controlled by adjusting the reaction time, radio frequency (RF) power, and growth temperature. The continuous bombardment of plasma results in a large number of defects on the surface of the CNTs. It was further confirmed that the radio frequency (RF) power played a key role on the generation of GNFs by providing sufficient carbon sources and creating defects as the active sites on surface of the CNTs. Moreover, small amounts (1.2%) of synthesized CNT@GNF material after purification were employed as an efficient conductive agent for the cathode with high contents of LiFePO 4 (LFP) up to 95.8%. As a result, the CNT@GNF-based LFP electrode showed a superior electrochemical performance. After 450 cycles at a current density of 0.5 C, the battery exhibited a specific capacity of 100 mAh g –1, corresponding to a capacity retention rate of 87%. Additionally, a discharge capacity of 61 mAh g –1 can still be achieved at 10 C. The largely improved electrochemical performance should be ascribed to the well-established conductive networks by the CNT@GNF material in the electrode. Overall, we synthesized nanocarbons with a unique structure in a facile way, which is promising for the application in lithium-ion batteries.
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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 | 0.000 |
| 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".