A facile and low-cost Al <sub>2</sub> O <sub>3</sub> coating as an artificial solid electrolyte interphase layer on graphite/silicon composites for lithium-ion batteries
Bibliographic record
Abstract
Abstract Graphite/silicon (G/Si) composites are considered as possible alternative anode materials to commercial graphite anodes. However, the unstable solid electrolyte interphase (SEI) on G/Si particles results in rapid capacity decay, impeding practical applications. Herein, a facile and low-cost Al 2 O 3 coating was developed to fabricate stable artificial SEI layers on G/Si composites. The amorphous Al 2 O 3 coating with a thickness of 10–15 nm was synthesized by a simple sol–gel method followed by high-temperature annealing. The Al 2 O 3 coated G/Si anode delivers an initial discharge capacity of 540 mAh g −1 at 25 °C and has improved Coulombic efficiency and cycling stability. After 100 cycles, the capacity retention is 76.4%, much higher than the 56.4% of the uncoated anode. Furthermore, the Al 2 O 3 coating was found to be more effective at improving the stability of G/Si at a higher temperature (55 °C). This was explained by the Al 2 O 3 coating suppressing the growth of SEI on Si/G and thus reducing the charge transfer resistance at the G/Si–electrolyte interface. It is expected that the Al 2 O 3 coating prepared by the sol–gel process can be applied to other Si-based anodes in the manufacture of practical high-performance 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) | 0.000 | 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".