Lithium Batteries and the Solid Electrolyte Interphase (SEI)—Progress and Outlook
Bibliographic record
Abstract
Abstract Interfacial dynamics within chemical systems such as electron and ion transport processes have relevance in the rational optimization of electrochemical energy storage materials and devices. Evolving the understanding of fundamental electrochemistry at interfaces would also help in the understanding of relevant phenomena in biological, microbial, pharmaceutical, electronic, and photonic systems. In lithium‐ion batteries, the electrochemical instability of the electrolyte and its ensuing reactive decomposition proceeds at the anode surface within the Helmholtz double layer resulting in a buildup of the reductive products, forming the solid electrolyte interphase (SEI). This review summarizes relevant aspects of the SEI including formation, composition, dynamic structure, and reaction mechanisms, focusing primarily on the graphite anode with insights into the lithium metal anode. Furthermore, the influence of the electrolyte and electrode materials on SEI structure and properties is discussed. An update is also presented on state‐of‐the‐art approaches to quantitatively characterize the structure and changing properties of the SEI. Lastly, a framework evaluating the standing problems and future research directions including feasible computational, machine learning, and experimental approaches are outlined.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".