A stable TiO$_{2}$–graphene nanocomposite anode with high rate capability for lithium-ion batteries
Bibliographic record
Abstract
A rapid microwave hydrothermal process is adopted for the synthesis of titanium dioxide and reduced graphene oxide nanocomposites as high-performance anode materials for Li-ion batteries. With the assistance of hydrazine hydrate as a reducing agent, graphene oxide was reduced while TiO$_{2}$ nanoparticles were grown in situ on the nanosheets to obtain the nanocomposite material. The morphology of the nanocomposite obtained consisted of TiO$_{2}$ particles with a size of ∼100 nm, uniformly distributed on the reduced graphene oxide nanosheets. The as-prepared TiO$_{2}$–graphene nanocomposite was able to deliver a capacity of 250 mA h g−1 ± 5% at 0.2C for more than 200 cycles with remarkably stable cycle life during the Li+ insertion/extraction process. In terms of high rate capability performance, the nanocomposite delivered discharge capacity of ca. 100 mA h g−1 with >99% coulombic efficiency at C-rates of up to 20C. The enhanced electrochemical performance of the material in terms of high rate capability and cycling stability indicates that the as-developed TiO$_{2}$–rGO nanocomposites are promising electrode materials for future Li-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 | 0.000 | 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".