Mechanofusion-Derived Si Alloy-Graphite Composite Electrode Material for Li-Ion Battery
Bibliographic record
Abstract
Silicon-graphite composites are extremely promising negative electrode materials for Li-ion batteries. However, simple and effective methods to synthesize silicon-graphite composites with engineered structures are needed to realize this technology in practical applications. Here, we show that the environmentally friendly and low-cost dry mechanofusion (MF) process can effectively synthesize silicon alloy-graphite composites, in which silicon alloy particles can be well dispersed and embedded between flake graphite layers, as shown in Figure 1(a)-(b). This results in increased tap density and reduced surface area. The special structure provides a way to buffer volume expansion and contraction of the silicon alloy during lithiation and delithiation. As a result of this hierarchical arrangement, superior cyclability and rate capability are achieved compared to simple mixtures, with capacities of 950 mAh/g (i.e. 1473 Ah/L) and 900 mAh/g (i.e. 1432 Ah/L) at 2C and 4C, respectively (Figure 1(c)). Moreover, using this process, micron-sized silicon alloy particles can be well embedded within spherical natural graphite particles, as shown in Figure 1(d)-(e). The specially engineered structure created by the MF dry process leaves room for expansion and contraction of the silicon alloy during lithiation and delithiation process. In addition, the silicon alloy particles are protected within the graphite particle, which could be helpful to avoid excessive electrolyte exposure and improve cycle life. Figure 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".