Identification of the Solid Electrolyte Interface on the Si/C Composite Anode with FEC as the Additive
Bibliographic record
Abstract
Silicon-based anodes have the potential to be used in next-generation lithium ion batteries owing to their higher lithium storage capacity. However, the large volume change during the charge/discharge process and the repeated formation of a new solid electrolyte interface (SEI) on the re-exposed Si surface should be overcome to achieve a better electrochemical performance. Fluoroethylene carbonate (FEC) has been widely used as an electrolyte additive for Si-based anodes, but the intrinsical mechanism in performance improvement is not clear yet. Here, we combined solid-state NMR, X-ray photoelectron spectroscopy, and X-ray photoemission electron microscopy to characterize the composition, structure, and inhomogeneity of the SEI on Si/C composite anodes with or without the FEC additive. Similar species are observed with two electrolytes, but a denser SEI formed with FEC, which could prevent the small molecules (i.e., LiPF 6, P–O, and Li–O species) from penetrating to the surface of the Si/C anode. The hydrolysis of LiPF 6 leading to Li x PO y F z and further to Li 3 PO 4 could also be partially suppressed by the denser SEI formed with FEC. In addition, a large amount of LiF could protect the cracking and pulverization of Si particles. This study demonstrates a deeper understanding of the SEI formed with FEC, which could be a guide for optimizing the Si-based anodes for lithium ion batteries.
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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 | 0.001 | 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".