Preparation of Composites of Nano-Silicon and Spherical Natural Graphite for Li-Ion Batteries By a Drop-in Technology
Bibliographic record
Abstract
We will describe a methodology to prepare nano-Si / natural graphite composite using an established transformation manufacturing process without significant changes to the initial process flow, thus minimizing the cost of implementation. First, a baseline was established by investigating half-cells made with anode using a mixture of coated spherical natural graphite, nano-Si and LiPAA binder. Then, a composite of graphite / nano-Si was prepared by carbonizing a dry mixture of spherical graphite, nano-Si and petroleum pitch. The silicon content was below 20 wt.% toward graphite and the pitch-to-nano-Si was optimized. Following these guidelines, a series of Si/graphite composites were prepared by first performing a spheronization process to a dry mixture of natural graphite flakes, nano-Si and carbon precursors to disperse the silicon in the core and on the surface of the graphite matrix. Followed by a carbonization step to form the amorphous carbon coating simultaneously on the graphite and nano-Si surface. The morphology and chemical composition of the composites' surface was investigated using XPS, SEM and HRTEM and the possible formation of silicon oxides or carbides was evaluated by XRD. Tap density, surface area and PSD were performed to assess if the prepared powders reached the battery grade specifications. The composites show very high reversible capacity when evaluated in coin-type, Li-ion half cells.
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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 | 0.001 |
| 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".