Ultra‐stable Zinc Metal Anodes at −20 °C through Eutectic Solvation Sheath in Chlorine‐functionalized Eutectic Electrolytes with 1,3‐Dioxolane
Bibliographic record
Abstract
Abstract The brain‐storm of designing low‐cost and commercialized eutectic electrolytes for zinc (Zn)‐based electrochemical energy storage (ZEES) remains unresolved and attractive, especially when implementing it at low temperatures. Here, we report an appealing layout of advancing chlorine‐functionalized eutectic (Cl‐FE) electrolytes via exploiting Cl anion‐induced eutectic interaction with Zn acetate solutions. This novel eutectic liquid shows high affinity to collaborate with 1,3‐dioxolane (DOL) and is prone to constitute Cl‐FE/DOL‐based electrolytes with a unique inner/outer eutectic solvation sheath for the better regulation of Zn‐solvating neighboring and reconstruction of H‐bonding. The side reactions are effectively restricted on Zn anodes and a high Coulombic efficiency of 99.5 % can be achieved over 1000 cycles at −20 °C with Zn//Cu setups. By prototyping scale‐up Zn‐ion pouch cells using the optimal eutectic liquid of 3ZnOAc 1.2 Cl 1.8 ‐DOL, we obtain improved electrochemical properties at −20 °C with a high capacitance of 203.9 F g −1 at 0.02 A g −1 in a range of 0.20–1.90 V and long‐term cycling ability with 95.3 % capacitance retention at 0.2 A g −1 over 3,000 cycles. Overall, the proposal of ideal Cl‐FE/DOL‐based electrolytes guides the design of sub‐zero and endurable aqueous ZEES devices and beyond.
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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".