Suppressing Chemical and Galvanic Corrosion in Anode‐Free Lithium Metal Batteries Through Electrolyte Design
Bibliographic record
Abstract
Abstract The advancement of anode‐free lithium metal batteries (AFLMBs) is greatly appreciated due to their exceptional energy density. Despite considerable efforts to enhance the cycling performance of AFLMBs, the understanding of lithium corrosion, which leads to substantial capacity loss during the open circuit voltage (OCV), regardless of electrolyte chemistry, remains limited. In particular, the connection between electrolytes and lithium corrosion performance lacks clear understanding, and the impact of solid‐electrolyte interface (SEI) composition on resistance against lithium corrosion remains elusive. This study explores, for the first time, the effects of salt concentration and solvents on the lithium corrosion behavior in AFLMBs, utilizing the lithium bis(fluorosulfonyl)imide (LiFSI)‐1,2 dimethoxyethane (DME)‐1,3 dioxolane (DOL) electrolyte system. The findings reveal that increasing salt concentration leads to the suppression of chemical corrosion but exacerbation of galvanic corrosion. The key to effectively suppressing both chemical and galvanic corrosion lies in promoting the coordination of less‐soluble particles and polymers within the SEI. This conclusion is further validated by a two‐step electrolyte modification measurement. As a result, a new electrolyte formula, 0.5 M LiFSI‐0.5 M LiBF 2 (C 2 O 4 )‐0.5 M LiNO 3 (DME/fluoroethylene carbonate or FEC), is prepared and shown to have improved resistance to both chemical and galvanic corrosion, and the Coulombic efficiency loss from chemical corrosion is reduced to 0.13%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".