Controlling Void Space in Crumpled Graphene‐Encapsulated Silicon Anodes using Sacrificial Polystyrene Nanoparticles
Bibliographic record
Abstract
Abstract Silicon anodes have a theoretical capacity of 3590 mAh g−1 (for Li15Si4, at room temperature), which is tenfold higher than the graphite anodes used in current Li‐ion batteries. This, and silicon's natural abundance, makes it one of the most promising materials for next‐generation batteries. Encapsulating silicon nanoparticles (Si NPs) in a crumpled graphene shell by spray drying or spray pyrolysis are promising and scalable methods to produce core–shell structures, which buffer the extreme volume change (>300 vol %) caused by (de)lithiaton of silicon. However, capillary forces cause the graphene‐based materials to tightly wrap around Si NP clusters, and there is little control over the void space required to further improve cycle life. Herein, a simple strategy is developed to engineer void‐space within the core by incorporating varying amounts of similarly sized polystyrene (PS) nanospheres in the spray drier feed mixture. The PS completely decomposes during thermal reduction of the graphene oxide shell and results in Si cores of varying porosity. The best performance is achieved at a 1 : 1 ratio (PS/Si), leading to high capacities of 1638, 1468, and 1179 mAh g−1Si+rGO at 0.1, 1, and 4 A g−1, respectively. Moreover, at 1 A g−1, the capacity retention is 80.6 % after 200 cycles. At a practical active material loading of 2.4 mg cm−2, the electrodes achieve an areal capacity of 2.26 mAh cm−2 at 1 A g−1.
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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".