Gel Polymer Electrolytes for Zinc-Air Batteries Operating at Low Temperatures
Bibliographic record
Abstract
Zinc-air batteries (ZABs) have gained much attention from researchers in recent years, partly due to their high theoretical energy density as well as improved safety and the abundance of zinc relative to lithium in lithium-ion batteries (LIBs). Gel polymer electrolytes (GPEs) in ZABs can act as a separator and, therefore, can reduce the effect of dendrite formation at the zinc electrode. The temperature of many areas in Canada can be extremely low (less than -30oC) in the winter and many batteries do not work well at such low temperatures, mainly due to reduced rates for the electrochemical reactions in the battery. The objective of this work is to develop a GPE for ZABs using in-situ fabrication. The use of in-situ gelation can reduce the contact resistance between the GPE and the electrodes, thereby improving ion transport between the electrolyte and electrodes. The GPEs in this work are fabricated using poly(acrylic acid), crosslinked with KOH and a final immersion step in a mixture of additives. The additives are utilized to improve the performance of ZABs at low temperatures. As a redox mediator, KI can change the traditional oxygen evolution reaction in ZABs to a more thermodynamically favored reaction. ZnO is used to improve the cyclability of ZABs, whereas ethylene glycol is used to reduce the effect of hydrogel evolution at the zinc electrode.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".