A Dual Effect Additive Modified Electrolyte Strategy to Improve the Electrochemical Performance of Zinc‐Based Prussian Blue Analogs Energy Storage Device
Bibliographic record
Abstract
Abstract Prussian blue analogs (PBA) exhibit excellent potential for energy storage due to their unique three‐dimensional open framework and abundant redox active sites. However, the dissolution of transition metal ions in water can compromise the structural integrity of PBAs, leading to significant issues such as low cycle life and capacity decay. To address these challenges, we proposed a dual‐effect additive‐modified electrolyte method to alleviate such issues, introducing sodium ferrocyanide (Na 4 Fe(CN) 6 ) into aqueous alkaline electrolytes. It could not only capture Zn 2+ dissolved on the surface of Na 1.86 Zn 1.46 [Fe(CN) 6 ] 0.87 (ZnHCF) electrode material during the cycling process but also conduct redox reactions on the electrode surface to provide additional capacitance. Through experiments and molecular simulation calculations, it showed that Na 4 Fe(CN) 6 can restrict the movement of Zn dissolution into the electrolyte on the electrode surface. Based on this, an asymmetric supercapacitor based on ZnHCF//activated carbon was assembled with a modified electrolyte. The assembled supercapacitor displayed a specific capacitance of 1,329.65 mF cm −2 , a power density of 2,900 mW cm −2 , and an energy density of 388.28 mW h cm −2 . This study provides a new idea for the design and construction of stable and efficient PBA energy storage materials by inhibiting the leaching of transition metals in PBA.
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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 | 0.000 |
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".