Cation-self-shielding strategy promises high-voltage all-Prussian-blue-based aqueous K-ion batteries
Bibliographic record
Abstract
Prussian blue analogues (PBAs) are promising electrode candidates for aqueous batteries because the inevitable interstitial water is generally thought to have little impact on battery performance. Currently, mounting researches have focused on optimizing PBA properties by varying transition metal composition, but less attention has been paid to interstitial water, especially in alkali metal-ion deficient PBAs with large cavities. Here, we employ the water-rich K0.01Mn[Cr(CN)6]0.74·4.75H2O as the negative electrode to study the effect of interstitial water. It is found that during de-potassiation, the electrode undergoes dehydration, which negatively impacts kinetics, distorts structure, and raises charging potential. A cation-self-shielding strategy involving Dihydroxyacetone (DHA) in the electrolyte to secure the water-rich state is then proposed. The built 1.82 V all-Prussian blue aqueous K-ion battery delivers a high practical specific energy of ~76 Wh kg−1 over 1.5 V (based on the total mass of active materials in both electrodes). This study reveals the significance of interstitial water on the kinetics of PBA negative electrodes and promotes the exploration of water-containing electrodes to develop high-voltage aqueous rechargeable batteries for energy storage applications. Prussian blue analogues are promising for aqueous batteries but interstitial water effects remain underexplored. Here, authors develop a cation-self-shielding strategy with dihydroxyacetone to stabilize water-rich Prussian blue analogues for application in K-ion 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".