Transforming Silicon Slag into High-Capacity Anode Material for Lithium-Ion Batteries
Bibliographic record
Abstract
Silicon is mainly produced by carbothermic reduction of silica. This process generates a by-product called silicon slag, which consists mainly of a mixture of Si, SiC and C. This silicon slag represents considerable energy and material losses. On the other hand, the production of cheaper and more environmentally friendly electrode materials is essential for Li-ion battery manufacturers. In this study, it is demonstrated that this waste containing 65 wt% Si, 31 wt% SiC and 4 wt% C can be valorized as low-cost high-capacity LiB anode material. After 20 h of high-energy ball-milling, C is fully converted to SiC and a micrometric powder is obtained in which submicrometric SiC inclusions are embedded in a nanocrystalline/amorphous Si matrix (Fig. 1a). This material displays a specific discharge capacity ≥1100 mAh g-1 at a current density ≤ 0.9 A g-1 (Fig. 1b) and an areal capacity ≥3.5 mAh cm-2 for at least 100 cycles (Fig. 1c). Moreover, calendering has no negative impact on the electrode performance (Fig 1d). The dQ/dV curves (Fig. 1e) do not shown intense-sharp anodic peak at about 0.45V characteristic of the delithiation of the c-Li15Si4 phase, suggesting that its formation is here prevented. This may be beneficial for the electrode cycle life as the formation of c-Li15Si4 phase is well-known to accentuate the particle cracking. However, a progressive and irreversible increase of the electrode mass and thickness is observed over cycling (reaching 125% and 60% after 200 cycles, respectively) (Fig. 1f), which is mainly attributed to the accumulation of solid electrolyte interphase (SEI) products in the electrode. 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".