Ionic liquid electrolytes for sodium-ion batteries to control thermal runaway
Bibliographic record
Abstract
Sodium-ion batteries are expected to be more affordable for stationary applications than lithium-ion batteries, while still offering sufficient energy density and operational capacity to power a significant segment of the battery market. Despite this, thermal runaway explosions associated with organic electrolytes have led to concerns regarding the safety of sodium-ion batteries. Among electrolytes, ionic liquids are promising because they have negligible vapor pressure and show high thermal and electrochemical stability. This review discusses the safety contributions of these electrolyte properties for high-temperature applications. The ionic liquids provide thermal stability while at the same time promoting high-voltage window battery operations. Moreover, apart from cycle stability, there is an additional safety feature attributed to modified ultra-concentrated ionic liquid electrolytes. Concerning these contributions, the following have been discussed, heat sources and thermal runaway mechanisms, thermal stability, the electrochemical decomposition mechanism of stable cations, and the ionic transport mechanism of ultra-concentrated ionic liquid electrolytes. In addition, the contributions of hybrid electrolyte systems consisting of ionic liquids with either organic carbonate or polymers are also discussed. The thermal stability of ionic liquids is found to be the main contributor to cell safety and cycle stability. For high-temperature applications where electrolyte safety, capacity, and cycle stability are important, highly concentrated ionic liquid electrolyte systems are potential solutions for sodium-ion battery 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".