SiO<sub><i>x</i></sub>@ZrO<sub>2</sub>@C Nanospheres as a High-Capacity and Stable Anode Material for Lithium-Ion Batteries
Bibliographic record
Abstract
Silicon oxide (SiO x ) has been widely studied due to its ultrahigh theoretical specific capacity for lithium storage. However, its inherent low electronic conductivity and large volume change before and after lithium insertion have limited its commercialization. To address this issue, we report the synthesis of SiO x @ZrO 2 @C ternary composite nanospheres by convenient and scalable wet chemistry and sintering processes. In the unique structure of 3D porous SiO x @ZrO 2 @C nanospheres, zirconium oxide (ZrO 2 ) supplies high structural stability, while the amorphous carbon (C) layer helps to form a stable solid–electrolyte interface, suppress volume expansion, and greatly improve the conductivity. Electrochemical measurements demonstrate that the SiO x @ZrO 2 @C nanospheres exhibit a capacity retention of 72.2% after 500 cycles at 500 mA g –1 . At a high current density of 800 mA g –1, the SiO x @ZrO 2 @C nanospheres can still deliver a capacity of 529.1 mAh g –1 . This work provides a promising mass-scale synthesis method for producing SiO x composites and introduces a high-performance anode material for 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) | 0.000 | 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".