Freezing of Aqueous Electrolytes in Zinc–Air Batteries: Effect of Composition and Nanoscale Confinement
Bibliographic record
Abstract
Abstract Zinc–air batteries, which typically employ aqueous electrolytes, have attracted much attention owing to their high energy density, low cost, and environmental friendliness. While the zinc–air battery is a promising solution for energy grid applications, freezing of the electrolyte is an important problem for operation in cold climates. The freezing point of the electrolyte can be affected not only by chemical composition but also by micro/nanoscale confinement in porous electrodes or separators, and this is the focus of our work. First, we find osmotic virial coefficients by fitting experimental freezing point data for various electrolytes that are used in zinc–air batteries. Second, we show how additives that improve the performance of the batteries may also lower the freezing point of the electrolyte system. Third, we show how the nanoscale confinement inside zinc–air batteries further decreases the freezing point; a 10 nm diameter capillary pore can suppress the local freezing point of the electrolyte by ∼10 °C. Finally, we map out the equilibrium mol% ice as a function of temperature, concentration, and confinement. This study provides insight that can be used to design specialized electrolytes for low temperature applications.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".