Entropy‐Driven Liquid Electrolytes for Lithium Batteries
Bibliographic record
Abstract
Abstract Developing liquid electrolytes with higher kinetics and enhanced interphase stability is one of the key challenges for lithium batteries. However, the poor solubility of lithium salts in solvents sets constraints that compromises the electrolyte properties. Here, it is shown that introducing multiple salts to form a high‐entropy solution, alters the solvation structure, which can be used to raise the solubility of specific salts and stabilize electrode–electrolyte interphases. The prepared high‐entropy electrolytes significantly enhance the cycling and rate performance of lithium batteries. For lithium‐metal anodes the reversibility exceeds 99%, which extends the cycle life of batteries even under aggressive cycling conditions. For commercial batteries, combining a graphite anode with a LiNi 0.8 Co 0.1 Mn 0.1 O 2 cathode, more than 1000 charge–discharge cycles are achieved while maintaining a capacity retention of more than 90%. These performance improvements with respect to regular electrolytes are rationalized by the unique features of the solvation structure in high‐entropy electrolytes. The weaker solvation interaction induced by the higher disorder results in improved lithium‐ion kinetics, and the altered solvation composition leads to stabilized interphases. Finally, the high‐entropy, induced by the presence of multiple salts, enables a decrease in melting temperature of the electrolytes and thus enables lower battery operation temperatures without changing the solvents.
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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 | 0.000 |
| 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".