Regulating the Li<sup>+</sup>‐Solvation Structure of Ester Electrolyte for High‐Energy‐Density Lithium Metal Batteries
Bibliographic record
Abstract
Abstract The development of high‐energy‐density Li metal batteries are hindered by electrolyte consumption and uneven lithium deposition due to the unstable lithium‐electrolyte interface (SEI). In this work, tetraglyme is introduced into ester electrolyte to regulate the Li+‐solvation structures for stable SEI while remaining appropriate voltage window for high‐voltage cathodes. In the modified solvation structures, an enhanced lowest unoccupied molecular orbital energy level occurs, resulting in relieved electrolyte degradation. In addition, the modified solvation structures can facilitate adequate LiNO3 dissolution in the ester electrolyte (denoted as E‐LiNO3), contributing to constant supplement of constructing highly conductive LiNxOy‐containing SEI for dendrite‐free Li deposition under high capacity condition. As a result, the Li||Cu cell‐based on this electrolyte exhibits high Li plating/stripping Coulombic efficiency of 98.2% over 350 cycles. Furthermore, when paired with high‐voltage LiNi0.5Co0.2Mn0.3O2 cathodes, the E‐LiNO3 enables a stable cycling with a high‐energy‐density of 296 Wh kg−1 based on the full cell under realistic testing conditions (lean electrolyte of 3 g Ah−1, limited Li excess of 2.45‐fold, and high areal capacity of 4 mAh cm−2).
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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".