High Rate and Long Cycle Life of a CNT/rGO/Si Nanoparticle Composite Anode for Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract Si nanoparticle (Si‐NP) composite anode with high rate and long cycle life is an attractive anode material for lithium‐ion battery (LIB) in hybrid electric vehicle (HEV)/pure electric vehicle (PEV). In this work, a carbon nanotube (CNT)/reduced graphene oxide (rGO)/Si nanoparticle composite with alternated structure as Li‐ion battery anode is prepared. In this structure, rGO completely wraps the entire Si/CNT networks by different layers and CNT networks provide fast electron transport pathways with reduced solid‐state diffusion, so that the stable solid‐electrolyte interphase layer can form on the whole surface of the matrix instead of on single Si nanoparticle, which ensure the high cycle stability to achieve the excellent cycle performance. As a result, the CNT/rGO/Si‐NP anode exhibits high performances with long cycle life (≈455 mAh g−1 at 15 A g−1 after 2000 cycles), high specific charge capacity (≈2250 mAh g−1 at 0.2 A g−1, ≈650 mAh g−1 at 15 A g−1), and fast charge/discharge rates (up to 16 A g−1). This nanostructure anode with facile and low‐cost synthesis method, as well as excellent electrochemical performances, makes it attractive for the long life cycles at high rate of the next generation LIB applications in HEV/PEV.
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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) | 0.000 | 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 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".