Bioinspired Tough Solid‐State Electrolyte for Flexible Ultralong‐Life Zinc–Air Battery
Bibliographic record
Abstract
Abstract Manufacturing advanced solid‐state electrolytes (SSEs) for flexible rechargeable batteries becomes increasingly important but remains grand challenge. The sophisticated structure of robust animal dermis and good water‐retention of plant cell in nature grant germane inspirations for designing high‐performance SSEs. Herein, tough bioinspired SSEs with intrinsic hydroxide ion (OH−) conduction are constructed by in situ formation of OH− conductive ionomer network within a hollow‐polymeric‐microcapsule‐decorated hydrogel polymer network. By virtue of the bioinspired design and dynamic dual‐penetrating network structure, the bioinspired SSEs simultaneously obtain mechanical robustness with 1800% stretchability, good water uptake of 107 g g−1 and water retention, and superhigh ion conductivity of 215 mS cm−1. The nanostructure of bioinspired SSE and related ion‐conduction mechanism are revealed and visualized by molecular dynamics simulation, where plenty of compact and superfast ion‐transport channels are constructed, contributing to superhigh ion conductivity. As a result, the flexible solid‐state zinc–air batteries assembled with bioinspired SSEs witness high power density of 148 mW cm−2, specific capacity of 758 mAh g−1 and ultralong cycling stability of 320 h as well as outstanding flexibility. The bioinspired methodology and deep insight of ion‐conduction mechanism will shed light on the design of advanced SSEs for flexible energy conversion and storage systems.
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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".