Electrolyte Design for Lithium Metal Anode‐Based Batteries Toward Extreme Temperature Application
Bibliographic record
Abstract
Abstract Lithium anode‐based batteries (LBs) are highly demanded in society owing to the high theoretical capacity and low reduction potential of metallic lithium. They are expected to see increasing deployment in performance critical areas including electric vehicles, grid storage, space, and sea vehicle operations. Unfortunately, competitive performance cannot be achieved when LBs operating under extreme temperature conditions where the lithium‐ion chemistry fail to perform optimally. In this review, a brief overview of the challenges in developing LBs for low temperature (<0 °C) and high temperature (>60 °C) operation are provided followed by electrolyte design strategies involving Li salt modification, solvation structure optimization, additive introduction, and solid‐state electrolyte utilization for LBs are introduced. Specifically, the prospects of using lithium metal batteries (LMBs), lithium sulfur (Li‐S) batteries, and lithium oxygen (Li‐O2) batteries for performance under low and high temperature applications are evaluated. These three chemistries are presented as prototypical examples of how the conventional low temperature charge transfer resistances and high temperature side reactions can be overcome. This review also points out the research direction of extreme temperature electrolyte design toward practical applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".