Dual‐Solvent Li‐Ion Solvation Enables High‐Performance Li‐Metal Batteries
Bibliographic record
Abstract
Abstract Novel electrolyte designs to further enhance the lithium (Li) metal battery cyclability are highly desirable. Here, fluorinated 1,6‐dimethoxyhexane (FDMH) is designed and synthesized as the solvent molecule to promote electrolyte stability with its prolonged –CF 2 – backbone. Meanwhile, 1,2‐dimethoxyethane is used as a co‐solvent to enable higher ionic conductivity and much reduced interfacial resistance. Combining the dual‐solvent system with 1 m lithium bis(fluorosulfonyl)imide (LiFSI), high Li‐metal Coulombic efficiency (99.5%) and oxidative stability (6 V) are achieved. Using this electrolyte, 20 µ m Li||NMC batteries are able to retain ≈ 80% capacity after 250 cycles and Cu||NMC anode‐free pouch cells last 120 cycles with 75% capacity retention under ≈ 2.1 µ L mAh −1 lean electrolyte conditions. Such high performances are attributed to the anion‐derived solid‐electrolyte interphase, originating from the coordination of Li‐ions to the highly stable FDMH and multiple anions in their solvation environments. This work demonstrates a new electrolyte design strategy that enables high‐performance Li‐metal batteries with multisolvent Li‐ion solvation with rationally optimized molecular structure and ratio.
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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".