3D Graphene Nanoflake/Vertically Aligned Carbon Nanotube/CoAl Layered Double Oxide Composites for High-Performance Lithium-Ion Batteries
Bibliographic record
Abstract
Using a urea-assisted precipitation method, we synthesized CoAl-layered double hydroxide (LDH) nanosheets that were uniformly aligned perpendicular to the surface of the silicon wafer. Then, a carbon nanocomposite consisting of vertically aligned carbon nanotubes (VACNTs) and graphene nanoflakes (GNFs) was prepared by plasma-enhanced chemical vapor deposition (PECVD) using LDH as the catalyst precursor. After heat treatment, LDH formed a layered double oxide (LDO). The VACNTs were attached to both sides of the LDO nanosheets, while GNFs were uniformly distributed on the VACNTs’ surface. Next, the three-dimensional (3D) GNF/VACNT-LDO material was used as a conductive agent for the LiFePO 4 cathode with a practical commercialized state-of-the-art cathode recipe of lithium-ion batteries. The results showed that the cathode had a high specific capacity and excellent cycling stability. The discharge specific capacity was as high as 168.6 mAh g –1 at a current rate of 0.2 C. Amazingly, when the current rate was increased to 10 C, the discharge capacity reached 105.3 mAh g –1, which was much higher than that with the conventional conductive agent Super P (65.1 mAh g –1 ). After 500 cycles at 0.5 C current density, the discharge specific capacity was still 118.2 mAh g –1, with a capacity retention rate of 72.7% and an average capacity loss of only 0.089 mAh g –1 per cycle. The excellent rate performance and cycling stability of the LFP cathode are largely attributed to the GNF/VACNT-LDO. The unique 3D conductive network constructed by GNF/VACNT-LDO can greatly increase the electron transport rate and accelerate the shuttling of Li + between the electrolyte and the electrode material.
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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 | 0.001 |
| 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) | 0.000 | 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 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".