A Low-Cost and Green Si-Based Anode Material for Lithium-Ion Batteries
Bibliographic record
Abstract
The conception of cheaper and greener electrode materials is critical for Li-ion battery manufacturers. In this study, it is shown that a by-product of the carbothermic reduction of SiO2 to Si, containing Si, SiC and C materials, can be valorized as a low-cost and high-capacity anode material for Li-ion batteries after an appropriate high-energy ball milling treatment. The latter results in the production of a micrometric powder (D50 ~1 mm) in which submicrometric SiC inclusions are embedded in a nanocrystalline/amorphous Si matrix. Such a microstructure prevents the deleterious formation of c-Li15Si4 phase, which is well known to accentuate particle cracking. As a result, the electrode is able to maintain a capacity >1000 mAh g-1 (>3 mAh cm-2) over 100 cycles. Moreover, calendering has no negative impact on the electrode performance. However, a significant and irreversible increase of the electrode mass and thickness was observed over cycling, which is mainly attributed to the accumulation of SEI products. In order to have deeper insights into the microstructural evolution of the electrode during cycling, a focused ion beam (FIB) milled microcavity (45×20×50 µm3) was created in the center of the pristine electrode. This cavity was observed by SEM at different cycling periods of a single electrode (Fig. 1a). This investigation method allows following the same electrode along different steps of its cycling, nearly as for an in-situ method. Additionally, backscattered-electron (BSE) imaging was performed on broad ion beam (BIB) polished cross-section of the electrode after different periods of cycling (Fig. 1b). The morphological change is characterized at the electrode and particle scales by monitoring the thickness, mass, porosity and macrocracking of the electrode, SEI layer thickness and particle morphology. On the basis of these investigations, a more comprehensive view of the degradation phenomena of the electrode is established. 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".