Inhibiting Phase Transitions of Prussian Blue Analogs with High‐Entropy Strategy for Ultralong‐Life Sodium‐Ion Battery Cathodes
Bibliographic record
Abstract
Abstract Prussian blue analogs (PBAs) have garnered considerable attention due to open 3D framework and high theoretical capacity. However, unfavorable phase transitions and inherent low conductivity lead to severe capacity decay and poor rate performance. Herein, the high‐entropy (HE) concept is incorporated into PBAs to improve their electrochemical properties. By introducing four inert elements sharing N coordination site with Fe, the high‐entropy Na x (FeCuNiMgZn)[Fe(CN) 6 ] (HEPBA) is constructed. The developed high‐entropy Prussian blue analog (HEPBA) exhibits high specific capacity with cyclic stability, stable operation 2000 cycles at 1C, and superior rate performance. Experimental results and theoretical calculations demonstrate that this high‐entropy design not only effectively inhibits phase transitions and reinforces structural stability but also activates the redox activity of low‐spin‐state Fe centers. Simultaneously, it can improve sodium‐ion diffusion by optimizing pathways and reducing energy barriers, ultimately enhancing overall performance. This strategy provides an innovative perspective that synergistically optimizes specific capacity, structural stability, and rate performance in PBAs.
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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".