Facile Synthesis of Graphite-SiO<sub><i>x</i></sub>/C Core–Shell Composite Anode for High Stable Lithium-Ion Batteries
Bibliographic record
Abstract
Graphite-silicon composite anodes have been regarded as some of the most practical next-generation anode materials for commercialization. However, poor interfacial contact between Si and graphite and serious volume expansion of Si always lead to even worse electrochemical performances than the pure graphite anode. Herein, we report a stable graphite-SiO x /C composite anode (Gr@SiO x /C) with a homogeneous SiO x /C coating layer on the surface of graphite via a facile sol–gel process and subsequent pyrolysis. SiO x /C can enhance the overall capacity of the composite anode while possessing a low volume expansion, which is beneficial to maintaining structural stability. Furthermore, the homogeneous distribution of SiO x and C frameworks also enables rapid and stable Li + /electron transport toward the graphite inner core. As a result, the as-prepared Gr@SiO x /C composite anode exhibits excellent cycling stability and rate capability with more than twice the capacity of graphite at 1 A g –1 . A full cell assembled with NCM811 cathode delivers a high stable cycling performance with a capacity retention exceeding 90% after 300 cycles and an average Coulomb efficiency of 99.24%. This work is expected to provide a reference for the rational design of graphite-silicon composite anodes 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) | 0.001 | 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".