High‐Energy‐Density Solid‐State Metal–Air Batteries: Progress, Challenges, and Perspectives
Bibliographic record
Abstract
Next-generation batteries have long been considered a transition to more sustainable storage technologies. Among them, metal-air batteries (MABs) with low cost, high safety, and environmental friendliness have shown great potential for future large-scale applications. Motivated by the desirable characteristics, significant progress is made in suppressing serious parasitic reactions, improving electrochemical performance, and increasing the energy density in MABs. Compared to the widely reported liquid electrolyte strategy, solid-state electrolytes (SSEs) can thoroughly solve the volatilization challenges of liquid electrolytes and protect the oxygen electrodes without the formation of diffusion-blocking oxide phases. Notably, SSEs for MABs are still in their infancy, and many thorny challenges still need to be solved. In this review, the main electrochemical mechanism, key challenges, and some important progress are sorted out for solid-state MABs, such as lithium-air, zinc-air, aluminum-air, and magnesium-air batteries. Besides their fundamental significance, these configurations are further compared in terms of energy density, cost, carbon footprint, energy consumption, rate performance, cycle performance, safety, and air stability of prevailing electrolytes.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".