Highly Solubilized Urea as Effective Proton Donor‐Acceptors for Durable Zinc‐Ion Storage Beyond Single‐Anion Selection Criteria
Bibliographic record
Abstract
Abstract Urea, as one of the most sustainable organic solutes, denies the high salt consumption in commercial electrolytes with its peculiar solubility in water. The bi‐mixture of urea‐H 2 O shows the eutectic feature for increased attention in aqueous Zn‐ion electrochemical energy storage (AZEES) technologies. While the state‐of‐the‐art aqueous electrolyte recipes are still pursuing the high‐concentrated salt dosage with limited urea adoption and single‐anion selection category. Here, a dual‐anion urea‐based (DAU) electrolyte composed of dual‐Zn salts and urea‐H 2 O‐induced solutions is reported, contributing to a stable electric double‐layer construction and in situ organic/inorganic SEI formation. The optimized ZT 2 S 0.5 ‐20U electrolytes show a high initial Coulombic efficiency of 93.2% and durable Zn‐ion storage ≈4000 h regarding Zn//Cu and Zn//Zn stripping/plating procedures. The assembled Zn//activated carbon full cells maintain ≈100% capacitance over 50 000 cycles at 4 A g –1 in coin cell and ≈98% capacitance over 20 000 cycles at 1 A g –1 in pouch cell setups. A 12 × 12 cm 2 pouch cell assembly illustrates the practicality of AZEES devices by designing the cheap, antifreezing, and nonflammable DAU electrolyte system coupling proton donor‐acceptor molecule and multi‐anion selection criteria, exterminating the critical technical barriers in commercialization.
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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".