Achieving Ultra‐Fast and Stable Sodium‐Ion Batteries Through Deep Activation of Low‐Spin Iron in Prussian Blue
Bibliographic record
Abstract
Abstract Prussian blue analogs (PBAs) are promising cathode materials for sodium‐ion batteries (SIBs) due to their high theoretical capacity, abundant iron resources, and simple synthesis. However, their practical implementation is limited by [Fe(CN)₆] vacancies and crystal water, which compromise structural stability and hinder the redox activity of low‐spin iron (Fe LS ). Herein, a modulation strategy through activating Fe LS site by introducing Cu 2+ and Zn 2+ in iron‐based PBA is adopted. Na₁.₅₅Cu₀.₀₅₃Zn₀.₀₆₀₈Fe₀.₈₉[Fe(CN)₆]₀.₉₄□₀.₀₆·1.80H₂O (CZ‐FeFe), is successfully synthesized using co‐precipitation. The initial capacity of CZ‐FeFe is dramatically enhanced by activating the Fe LS redox activity (from 0.48 to 0.80 e − ), verified by quasi‐in situ magnetic characterization. Theoretical calculations show improved electron transport and ion diffusion in CZ‐FeFe. Simultaneously, the incorporation of Cu 2+ and Zn 2+ is also beneficial for reducing [Fe(CN)₆] vacancies, minimizing crystal water, and slowing the phase transition between monoclinic and cubic structure, leading to superior long‐cycling stability. As a result, CZ‐FeFe exhibits a high specific capacity of 144.7 mAh g −1 at 1 C, exceptional rate performance, and remarkable long‐term stability (77.21% capacity retention after 2500 cycles at 10 C). The full‐cell performance further confirms the activation of Fe LS (from 0.21 to 0.52 e − ), along with improvements in rate performance and cycling stability.
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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".