Modification of <scp>VPO<sub>4</sub></scp> with carbon and <scp>3DG</scp> for high performance lithium‐ion battery anode
Bibliographic record
Abstract
Abstract Lithium‐ion batteries (LiBs) are one of the most promising energy storage devices. However, the large‐scale application of LiBs is limited by their electrochemical properties. In this study, we built a three‐dimensional (3D) conductive network structure with carbon and 3DG coating VPO4 (VPO4@C@3DG) via a one‐pot hydrothermal method with subsequent high‐temperature annealing. The effects of the content of three‐dimensional porous graphene (3DG) on the crystal structure, morphology, and electrochemical properties of VPO4/C are investigated using characterization and electrochemical test techniques. The SEM images show that the size of sphere‐like particles of VPO4@C@3DG composite with 20 wt.% of 3DG (VPO4@C@3DG‐20) is the smallest in all samples. In addition, the electrochemical experimental results reveal that VPO4@C@3DG‐20 exhibits the best cycling and rate performance compared to other VPO4@C@3DG composites. Specifically, VPO4@C@3DG‐20 achieves an initial charge capacity of 601.2 mAh g−1 at 0.2 C (110 mA g−1) and keeps at 354 mAh g−1 at the 100th cycle. This is because the introduction of 20 wt.% 3DG graphene inhibits the growth and aggregation of the particles, thus shortening the diffusion path of Li+. In addition, the 3D conducting network structure boosts the conductivity of the materials and buffers the volume variation resulting from the charging/discharging process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".