Progress and Complexities in Metal–Air Battery Technology
Bibliographic record
Abstract
Metal–air batteries (MABs) offer exceptional energy density, making them attractive for vehicle electrification and storing intermittent renewable energy. However, several challenges persist, including sluggish oxygen reduction and oxygen evolution reactions, interfacial stability issues, and challenges related to current collectors. Herein, the reasons behind MAB's failures are analyzed, considering their thermodynamic aspects (Gibbs free energy, entropy), electrochemical factors (redox potentials, polarization, and ion concentrations), and kinetic properties (mobility of charges). Strategies for mitigating energy barriers of the electrodes are explored, encompassing insights into the initiation process of the oxygen reduction and determinants of oxygen evolution kinetics. The impact of humidity on the electrolyte is assessed, and effective methods for dendrite prevention are elucidated. Additionally, the utilization of 3D electrodes, oxygen‐selective membranes, solid‐state electrolytes, hybrid polymer electrodes, conductive electrocatalysts, and artificial solid‐electrolyte interfaces, and their effects on addressing the challenges faced by MABs are discussed. The study also emphasizes six critical commercialization aspects for the advancement of MABs. Lastly, the potential prospects and challenges in the field of MAB technology are discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".