Harnessing Hybrid Aqueous/Organic Electrolytes for High Energy Density Supercapacitors
Bibliographic record
Abstract
Supercapacitors (SCs) emerged as promising energy storage devices to address the energy storage demands of the modern era. The limited energy density of SCs due to a narrow voltage window hinders their competitiveness. The electrodes of SCs drive the mechanism responsible for charge storage. However, electrolytes play a critical role in shaping key parameters that directly affect the operating voltage window, performance, and cost of SCs. This work highlights current research, addressing critical issues and solutions, focusing on advancing hybrid aqueous/organic electrolytes to widen the operating voltage window of SCs. The solvation chemistry and mechanistic studies responsible for widening the voltage window of SCs, especially with water as a primary solvent and organic additive as a co-solvent, have been explored. The tailored coordination of solvents (water and organic co-solvent) with electrolyte ions in hybrid electrolytes reduces the ion size and the availability of free water molecules for undesired hydrogen/oxygen evolution reaction (HER/OER), thereby widening the voltage window in SCs. The challenges in finding a suitable co-solvent and maintaining all essential properties of hybrid electrolytes have been highlighted and discussed. This study is vital to developing high-quality electrolytes for SCs to meet the high energy density demands for practical 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.002 |
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".