Practical Approach to Enhance Compatibility in Silicon/Graphite Composites to Enable High-Capacity Li-Ion Battery Anodes
Bibliographic record
Abstract
There is an urgent need to improve the energy density of Li-ion batteries to enable mass-market penetration of electric vehicles, grid-scale energy storage, and next-generation consumer electronics. Silicon-graphite composites are currently the most plausible anode material to overcome the capacity limit of graphite or poor cycling performance of silicon. One serious and unrecognized limitation to the use of the composite as an anode is the incompatibility of hydrophobic (natural) graphite with the hydrophilic Si, which adversely affects battery performance. Herein, we report a novel, practical approach to modify the graphite resulting in the formation of a hard carbon coating and graphene sheets that give rise to higher compatibility with Si nanoparticles in the composite. Electrochemical and battery testing of the composite (10 wt % Si) anode shows higher reversible capacity (10% at C/12 and 20% at C/2) than the composite with unmodified graphite reaching ∼600 mAh/g with 95% retention after 100 cycles. The enhanced battery performance is explained by the uniform distribution of Si nanoparticles at the modified graphite surface due to the presence of graphene conductive networks and a thin, oxygen-rich, amorphous carbon layer on the surface of graphite particles, as evidenced by transmission electron microscopy (TEM) images and X-ray photoelectron spectra (XPS). This work provides a new approach to prepare graphite compatible materials that can work with hydrophilic components other than silicon for various applications other than batteries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".