Evaluation and Characterization of SEI Composition in Lithium Metal and Anode‐Free Lithium Batteries
Bibliographic record
Abstract
Abstract Interfaces, particularly the solid electrolyte interface (SEI), play a crucial role in the performance and durability of batteries. Peled first proposed the inception of the SEI. The SEI, which is formed by electrolyte decomposition on the electrode surface, affects battery stability, electrochemistry, and cycle life. The structural properties of the SEI are related to lithium stripping and plating efficiency, as well as to the overall battery lifespan. In lithium metal batteries, the SEI must manage the significant volume changes of the electrode and prevent dendrite growth that can lead to short circuits and capacity losses. This challenge is exacerbated in anode‐free lithium batteries, where uncontrolled SEI growth can cause rapid capacity degradation. Improving SEI stability is vital for enhancing battery performance, and researchers are exploring various strategies, such as the use of electrolyte additives and synthetic SEI films. Advanced in situ characterization methods, such as atomic force microscopy and X‐ray photoelectron spectrometry, provide insights into the evolution of SEIs under operating conditions. This review covers recent research on SEI formation in lithium‐metal and anode‐free lithium batteries, emphasizes stabilization strategies, and examines new real‐time characterization methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".