Polymer Hydrogel Electrolytes for Flexible and Multifunctional Zinc‐Ion Batteries and Capacitors
Bibliographic record
Abstract
Flexibility and multifunctionality are now becoming inevitable worldwide tendencies for electronic devices to meet modern life's convenience, efficiency, and quality demand. To that end, developing flexible and wearable energy storage devices is a must. Recently, aqueous zinc‐ion batteries (ZIBs) and zinc‐ion capacitors (ZICs) stand out as two of the most potent candidates for wearable electronics due to their excellent electrochemical performance, intrinsic safety, low cost, and functional controllability. Simultaneously, polymer electrolytes' introduction and rational design, especially various hydrogels, have endowed conventional ZIBs and ZICs with colorful functions, which has been regarded as a perfect answer for energy suppliers integrated into those advanced wearable electronic devices. This review focuses on the functional hydrogel electrolytes (HEs) and their application for ZIBs and ZICs. Previously reported HEs for ZIBs and ZICs were classified and analyzed, from the flexibility to mechanical endurance, temperature adaptability, electrochemical stability, and finally cell‐level ZIBs and ZICs based on multifunctional HEs. Besides introducing the diverse and exciting functions of HEs, working principles were also analyzed. Ultimately, all the details of these examples were summarized, and the related challenges, constructive solutions, and futural prospects of functional ZIBs and ZICs were also dedicatedly evaluated.
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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 | 0.001 |
| 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".