Boosting Zn Anode Utilization by Trace Iodine Ions in Organic‐Water Hybrid Electrolytes through Formation of Anion‐rich Adsorbing Layers
Bibliographic record
Abstract
Abstract Aqueous Zn batteries are attracting extensive attentions, but their application is still hindered by H 2 O‐induced Zn‐corrosion and hydrogen evolution reactions. Addition of organic solvents into aqueous electrolytes to limit the H 2 O activity is a promising solution, but at the cost of greatly reduced Zn anode kinetics. Here we propose a simple strategy for this challenge by adding 50 mM iodine ions into an organic‐water (1,2‐dimethoxyethane (DME)+water) hybrid electrolyte, which enables the electrolyte simultaneously owns the advantages of low H 2 O activity and accelerated Zn kinetics. We demonstrate that the DME breaks the H 2 O hydrogen‐bond network and exclude H 2 O from Zn 2+ solvation shell. And the I − is firmly adsorbed on the Zn anode, reducing the Zn 2+ de‐solvation barrier from 74.33 kJ mol −1 to 32.26 kJ mol −1 and inducing homogeneous nucleation behavior. With such electrolyte, the Zn//Zn symmetric cell exhibits a record high cycling lifetime (14.5 months) and achieves high Zn anode utilization (75.5 %). In particular, the Zn//VS 2 @SS full cell with the optimized electrolyte stably cycles for 170 cycles at a low N : P ratio (3.64). Even with the cathode mass‐loading of 16.7 mg cm −2 , the full cell maintains the areal capacity of 0.96 mAh cm −2 after 1600 cycles.
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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".