Optimizing electrolyte systems for stable and low-temperature zinc-ion batteries via efficient coordinator
Bibliographic record
Abstract
Aqueous zinc-ion battery suffers poor cycling stability due to uneven Zn2+ deposition and serious dendrite growth. To effectively protect the Zn metal anode, diglyme (G2) is added as the co-solvent. Adding G2 can reconstruct the Zn2+ solvation structure and reduce the water molecule content of [Zn(H2O)6]2+ sheath. Theoretical calculations confirm that by adding G2, the triflate group OTF− ion shows greater interaction with Zn, and the water molecules from the [Zn(H2O)6]2+ solvation structure are replaced by OTF− ions. Benefitted from this, an organic-inorganic SEI layer is formed on the Zn anode, which isolates the Zn anode from the bulk electrolyte, and allows/suppresses Zn2+ diffusion. As well, Zn corrosion and side reactions are inhibited in this system. This co-solvent electrolyte system has a high Coulombic efficiency of 99.7%. A long cycling life of 1000 cycles with capacity retention of 80% is demonstrated by Zn//iodine (I2) - Activated Carbon (AC) full cell at room temperature. Even under a low-temperature condition (−18 °C), Zn//I2 - AC full cell shows a long lifespan of 1000 cycles with capacity retention of 65%. This work demonstrates a new direction and unparalleled insight into electrolyte engineering with solvation structure regeneration and SEI player formation for aqueous zinc-ion batteries.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".