Solubility Challenges in Battery Electrolytes
Bibliographic record
Abstract
This review provides a foundational understanding of solubility to support researchers in navigating challenges in battery electrolyte development. We survey recent strategies aimed at controlling, and typically maximizing, solubility in electrochemical systems, with a focus on redox flow and metal-ion batteries. The review begins with an accessible overview of solubility concepts, methods for accurately determining solubility for battery-relevant materials, and solubility prediction. We then discuss how solubility can be tuned by modifying the electrolyte solution structure or by tailoring the molecular structure of the active material itself, and we examine emerging strategies to decouple electrolyte capacity from solubility in flow batteries. In the context of metal and metal-ion batteries, we highlight the role of solvation structures in concentrated electrolytes and their influence on both bulk and interfacial properties. Finally, trade-offs associated with high-concentration formulations, such as increased viscosity and reduced ionic conductivity, are considered in light of their impact on practical deployment. We conclude with a forward-looking perspective on solubility as a central design parameter in battery electrolyte research.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".