Controlling Electrolyte Properties and Redox Reactions Using Solvation and Implications in Battery Functions: A Mini‐Review
Bibliographic record
Abstract
Abstract Electrolytes will play a central role in the development of next‐generation batteries with increased energy density and cycle life and reduced cost. While molecular designs can enable electrolytes with favorable properties like increased (electro)chemical stability, such properties can be manipulated additionally through the intermolecular interactions among species within the electrolyte. In this mini‐review, a number of intermolecular interactions in the electrolyte that can give rise to significant enhancement in battery functions are highlighted. The critical role of reactant and product solubility is shown in battery reactions, where increasing solubility can enable a dissolution–precipitation reaction pathway, decrease overpotential, and increase capacity. Through the intermolecular interactions among solvent, additives, and ions, the reactivity of electrolyte species can be altered significantly by either enhancing solvent (electro)chemical stability or facilitating water deprotonation in Li–O 2 reactions. It is shown that incorporating redox active species in the electrolyte can reduce the reaction overpotential and enhance cycle life. Moreover, intermolecular interactions that can increase the ionic conductivity and transference number of electrolytes are identified. Finally, future opportunities are highlighted to exploit these intermolecular interactions to gain unprecedented molecular control over the electrolyte and enable next‐generation batteries.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".