A low-cost and high performance ball-milled Si-based negative electrode for high-energy Li-ion batteries
Bibliographic record
Abstract
A Si-based anode with improved performance can be achieved using high-energy ball-milling as a cheap and easy process to produce Si powders prepared from a coarse-grained material. Ball-milled powders present all the advantages of nanometric Si powders, but not the drawbacks. Milled powders are nanostructured with micrometric agglomerates (median size ∼10 μm), made of submicrometric cold-welded particles with a crystallite size of ∼10 nm. The micrometric particle size provides handling and non-toxicity advantages compared to nanometric powders, as well as four times higher tap density. The nanostructuration is assumed to provide a shortened Li+ diffusion path, a fast Li+ diffusion path along grain boundaries and a smoother phase transition upon cycling. Compared to non-milled 1–5 μm powders, the improved performance of nanostructured milled Si powders is linked to a strong lowering of particle disconnection at each charge, while the irreversibility due to SEI formation remains unchanged. An electrode prepared in acidic conditions with the CMC binder achieves 600 cycles at more than 1170 mA h per gram of the milled Si-based electrode, in an electrolyte containing FEC/VC SEI-forming additives, with a coulombic efficiency above 99%, compared to less than 100 cycles at the same capacity for an electrode containing nanometric Si powder.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".