Localized High‐Concentration Electrolyte with Water‐Miscible Diluent Enables Stable Zinc Deposition and Long‐Life Aqueous Zinc Metal Batteries
Bibliographic record
Abstract
ABSTRACT Electrolyte engineering has emerged as a pivotal strategy to address critical challenges in aqueous zinc metal batteries (AZMBs), including zinc dendrite growth, parasitic side reactions, and anode corrosion. While conventional regular‐concentration electrolytes (≤2 mol L −1 ) fail to mitigate these issues, high‐concentration electrolytes (≥3 mol L −1 ) have demonstrated improved interfacial stability but suffer from high costs, limiting their practical applicability. Inspired by the concept of localized high‐concentration electrolytes (LHCEs) in non‐aqueous systems, we report a novel aqueous LHCE by introducing diisopropyl ether (DIPE) as a cost‐effective, inert diluent. This tailored electrolyte enables the simultaneous formation of robust solid electrolyte interphase and cathode electrolyte interphase on zinc anodes and NaV 3 O₈·1.5H 2 O cathodes, respectively, significantly enhancing interfacial stability and suppressing side reactions. Molecular dynamics simulations and spectroscopic analyses reveal that although DIPE is excluded from the Zn 2 ⁺ solvation sheath, it effectively modulates the solvation environment, suppresses water activity, and directs uniform Zn deposition along the (002) plane. Consequently, the DIPE‐based LHCE delivers a high coulombic efficiency of ∼99.7% over 700 cycles in Zn//Cu half‐cells (1 mA cm − 2 , 1 mAh cm − 2 ), demonstrating outstanding reversibility and long‐term cycling stability. Beyond demonstrating a practical electrolyte formulation, this work establishes a general design principle for aqueous LHCEs, using water‐miscible, weakly coordinating diluents to decouple bulk salt concentration from interfacial coordination, and provides mechanistic guidance for extending LHCE strategies to other zinc‐based and multivalent metal battery 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".