Water-in-Salt Electrolytes: Advances and Chemistry for Sustainable Aqueous Monovalent-Metal-Ion Batteries
Bibliographic record
Abstract
Electrolytes play a vital role in the performance and safety of electrochemical energy storage devices, such as lithium-ion batteries (LIBs). While traditional LIBs rely on organic electrolytes, these flammable solutions pose safety risks and require expensive, moisture-sensitive manufacturing processes. Aqueous electrolytes offer a safer, more cost-effective alternative, but their narrow electrochemical window, corrosivity to electrodes, and enabling of dendritic growth on metal anodes limit their practical applications. Water-in-salt electrolytes (WiSEs) have emerged as a promising solution to these challenges. By significantly reducing water activity and forming a stable solid–electrolyte interphase (SEI), WiSEs can expand the electrochemical stability window, inhibit material dissolution, and suppress dendritic growth. This unique SEI formation mechanism, which is similar to that observed in organic electrolytes, contributes to the improved performance and stability of WiSE-based batteries. Additionally, the altered solvation structure of WiSEs minimizes the presence of free water molecules, further stabilizing the SEI and reducing water activity. This review comprehensively examines the composition, mechanisms, and characterization of WiSEs and their application in monovalent-metal-ion batteries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".