Hierarchical Fluorinated Polymer Separator Design Mitigating Bilateral Ionic Crosstalk in Aqueous Batteries
Bibliographic record
Abstract
ABSTRACT Aqueous Zn//MnO 2 batteries hold significant promise for safe and cost‐effective large‐scale energy storage, yet their practical deployment is hindered by rapid performance degradation. Here, we identify bilateral ionic crosstalk, a previously overlooked failure mechanism driven by active ion, as a root cause of electrode degradation. We demonstrate that excess Zn 2+ migrating from the anode induces irreversible phase transitions at the MnO 2 cathode, forming electrochemically inert Zn x (MnO 2 ) y phase (ZMO sclerosis). Concurrently, dissolved Mn 2+ from the cathode exacerbates corrosion and dendrite growth on the Zn anode. To mitigate this crosstalk, we design a hierarchical fluorinated polymer separator (HFPS). Such HFPS enables selective cation coordination and guides ion transport, achieving simultaneous regulation of Zn 2+ and Mn 2+ fluxes. This targeted regulation effectively mitigates ionic crosstalk and stabilizes both electrodes. Batteries employing the HFPS exhibit exceptional cycling stability, retaining 97% capacity after 1,000 cycles at 0.5 A g −1 with stable operation exceeding 500 h. This performance represents a 54% lifespan enhancement over state‐of‐the‐art aqueous counterparts. Our work provides a fundamental mechanistic understanding of active‐ion‐induced failure and establishes ion flux regulation as a universal design strategy for durable aqueous zinc‐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 | 0.000 |
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".