Composites of Silicon@Li <sub>4</sub> Ti <sub>5</sub> O <sub>12</sub> and Graphite for High-Capacity Lithium-Ion Battery Anode Materials
Bibliographic record
Abstract
Silicon (Si) is a promising anode material for lithium-ion batteries owing to its high theoretical capacity. However, it suffers from poor capacity retention during cycling due to mechanical stresses, pulverization, and an unstable solid electrolyte interface. One practical approach to mitigate the problem is a coating design, where nano-sized silicon is encapsulated within a selected protective layer. In this study, silicon nanoparticles have been coated with a protective layer of Li 4 Ti 5 O 12 (LTO) ceramic and prepared using a water-based sodium alginate binder. It is found that the Si@LTO composites can be combined with graphite to improve battery performance further. The composite electrodes have been tested in half cells at C/10 and 1C rates. The best Si@LTO and graphite composite has an initial high capacity (∼900 mAh g −1 at C/10 and ∼600 mAh g −1 at 1C) and good capacity retention. It is found that this capacity retention is superior to Si@LTO alone and a binary composite of silicon with graphite. These Si@LTO + graphite composites are a promising way to integrate silicon into the development of stable and high-energy-density lithium-ion 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".