Optimal Structural Design to Improve Cycling Stability of SiO <sub> <i>x</i> </sub> -Spherical Porous CNTs Composite Anode for Lithium-Ion Battery
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Silicon suboxide (SiO x ) has emerged as a viable anode material for lithium-ion batteries (LIBs) because of its high theoretical specific capacity and structural stability. However, its practical application is restricted by inadequate cycling stability and poor electrical conductivity. Herein, plasma-enhanced chemical vapor deposition (PE-CVD) was utilized to synthesize SiO x -SPC composites, in which the optimized spherical porous CNTs (SPC) provided a well-defined porous framework that facilitated uniform SiO x deposition. The resulting SiO x -SPC composite (SSC) exhibits high electrical conductivity, Li-ion diffusivity, and mechanical stability, which remarkably enhance the cyclic stability and rate capability. As a result, the SSC electrode exhibits a high initial specific capacity of 1032.26 mAh g –1 and achieves exceptional cycling performance, considerably surpassing microsized SiO x (MSiO x ) particles (∼102% vs ∼41% retention after 100 cycles at 0.5C). Moreover, SSC shows enhanced Li-ion diffusion (4.66 × 10 –10 cm 2 s –1 ) as evaluated by cyclic voltammetry. This work demonstrates the essential role of the SiO x coating on optimized SPC via PE-CVD and enables the development of long-lasting, high-capacity anode materials for advanced lithium-ion battery technologies.
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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".