From Lithium‐Oxygen to Lithium‐Air Batteries: Challenges and Opportunities
Bibliographic record
Abstract
Lithium ‐ air batteries have become a focus of research on future battery technologies. Technical issues associated with lithium‐air batteries, however, are rather complex. Apart from the sluggish oxygen reaction kinetics which demand efficient oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) catalysts, issues are also inherited from the nature of an open battery system and the use of reactive metal lithium as anode. Lithium‐air batteries, which exchange oxygen directly with ambient air, face more challenges due to the additional oxidative agents of moisture, carbon dioxide, etc. which degrade the metal lithium anode, deteriorating the performance of the batteries. In order to improve the cycling performance one must hold a full picture of lithium‐oxygen electrochemistry in the presence of carbon dioxide and/or moisture and fully understand the fundamentals of chemistry reactions therein. Recent advances in the exploration of the effect of moisture and CO 2 contaminants on Li‐O 2 batteries are reviewed, and the mechanistic understanding of discharge/charge process in O 2 at controlled level of moisture and/or CO 2 are illustrated. Prospects for development opportunities of Li‐air batteries, insight into future research directions, and guidelines for the further development of rechargeable Li‐air batteries are also given.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".