In memory of Bruno Scrosati: Metal salts for rechargeable Batteries: Past, present, and future
Bibliographic record
Abstract
Ionic transport mechanisms within the electrolyte play a crucial role in governing the electrode processes and charge transfer kinetics in rechargeable battery systems. This has particularly garnered widespread attention in recent years, as high voltage for metal ion batteries (MIBs) and solid-state batteries (SSMBs) are key areas of growing interest, driven by their potential future applications. Despite their importance, the electrolytes in MIBs and SSMBs lead to multiple bottleneck challenges. SSMBs based on polymer-state electrolytes (SPEs) are relatively new research hotspots that exhibit higher ionic transport and greater energy density while addressing issues like flammability and low energy density often encountered with liquid electrolytes (LEs) in lithium-ion (LIBs) and sodium-ion (SIBs) batteries. However, owing to their high crystallinity, poly (ethylene oxide) — PEO, SPEs suffer from reduced ionic conductivity at room temperature. A possible remedy is provided by gel polymer electrolytes (GPEs), which, decrease crystallinity, thus improving the ionic conductivity as well as improving the electrode-electrolyte contact. This review covers the synthesis processes and characterization of lithium and sodium salts, which are key to electrolyte design. It then summarizes the recent progress in electrolyte formulations for high-voltage MIBs and SSMBs. Finally, the critical contribution of understanding ionic conductivity the stability of electrolytes-electrodes interfaces and electrolyte structures is highlighted, as these factors directly impact the performance and lifespan of these battery systems.
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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) | 0.000 | 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) | 0.006 | 0.003 |
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".