Remodeling Highly Fluorinated Electrolyte via Shielding Agent Regulation toward Practical Lithium Metal Batteries
Bibliographic record
Abstract
Abstract Highly fluorinated electrolytes have proved effective in improving electrochemical stability of lithium metal batteries. However, excessive fluorination not only detrimentally impacts the electrolyte ionic conductivity, but also inevitably forms the over‐fluorinated interphases with sluggish ion diffusivity. Herein, a strategy on remodeling Li+ solvation structure in highly fluorinated electrolyte aided is proposed by fluorinated amide (FDMA), which denoted as “shielding agent”. Benefitting from FDMA's high donor number (DN) value (22.1), the Li+‐dipole (fluoroethylene carbonate (FEC) or trans‐4,5‐Difluoroethylenecarbonate (DFEC)) interaction is interrupted and the participation of FDMA in primary solvation sheath fructify the solid‐electrolyte interphase without scarifying the privilege of fluorinated electrolyte on interphase chemistry. Eventually, the optimal high‐fluorinated electrolyte (FDMA/DFEC + 1.0 mol L−1 LiTFSI) with this unique shielding effect displays high ionic conductivity and rapid Li+ desolvation behavior, enabling Li||LiNi0.6Co0.2Mn0.2O2 (Li||NCM622) to achieve an ultralong cycle‐life of 2000 cycles at 1C with 84.7% capacity retention. Even under extreme conditions (NCM622: 10 mg cm−2; electrolyte: 20 µL; Li: 50 µm), the Li||NCM622 displays excellent electrochemical performance. Additionally, 447 Wh kg−1 Li||LiNi0.8Co0.1Mn0.1O2 (Li||NCM811) pouch cells have been successfully fabricated and demonstrate an exceptional cycle‐life over 150 cycles. The proposed “shielding” strategy to modulate the solvation structure paves the way for developing practical LMBs with fluorinated electrolytes.
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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 | 0.000 | 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) | 0.001 | 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".