Water-in-Salt Gel Biopolymer Electrolytes for Flexible and Wearable Zn/Alkali Metal Dual-Ion Batteries
Bibliographic record
Abstract
Zn/alkali metal dual-ion batteries (ZM DIBs) with highly concentrated water-in-salt (WiS) electrolytes are promising next-generation energy storage systems. This enhanced design of Zn-ion rechargeable batteries offers intrinsic safety, high operating voltage, satisfactory capacity, and outstanding cyclic stability. Herein, taking the concept of highly concentrated electrolytes one step further, we introduce water-in-salt gel biopolymer electrolytes (WiS-GBEs) by encapsulating Zn/Li or Zn/Na bisalt compositions in a cellulose membrane. WiS-GBEs inherit the electrochemical merits of highly concentrated electrolytes (i.e., wide voltage window, high ionic conductivity, etc.) and excellent durability of gel biopolymer structures. Both types of WiS-GBEs apply to coin- and pouch-cell compartments of ZM DIBs, offering a high plateau voltage (>1.8 V vs. Zn 2+ /Zn), good and reversible capacity (118 and 57 mAh g –1 for Zn/Li and Zn/Na cells, respectively), and outstanding cycling stability (more than 90% after 1,000 cycles). Essentially, the pouch cells with WiS-GBEs present superior durability, flexibility, and capacity endurance under various bending stress conditions (90% capacity retention under 0–180° bending modes), indicating their potential capability to power wearable electronics. The practical powering ability of Li- and Na-based pouch systems is demonstrated by the example of a wearable digital timer.
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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) | 0.000 | 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".