Alkaline aqueous electrolytes for secondary zinc-air batteries: an overview
Bibliographic record
Abstract
New applications and emerging markets in electromobility and large-scale stationary energy storage require the development of new electrochemical systems with higher energy density than current batteries. Rechargeable metal–air batteries, mainly lithium–air and zinc–air systems, are considered one of the most promising candidates. In contrast to lithium, zinc is abundant, inexpensive and its electrodeposition in aqueous electrolytes is relatively easy. Unfortunately, achieving a rechargeable zinc–air battery is still hindered by various technical problems related to the reversibility and lifetime of the electrodes. The most widely used electrolyte in zinc–air batteries has been the classical aqueous alkaline. In this context and with the main objective of providing a complete overview, we studied a wide number of articles starting from the beginning of the development of secondary zinc–air batteries (1970–1980s) to more recent works, with the aim of compiling all available information. It is essential to revise older papers to find relevant information that may get otherwise forgotten and not taken into account to develop new solutions. This information could also be applied in other storage systems based on zinc as nickel–zinc, zinc hybrid or zinc-ion. Copyright © 2016 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".