Fundamental Understanding and Material Challenges in Rechargeable Nonaqueous Li–O<sub>2</sub> Batteries: Recent Progress and Perspective
Bibliographic record
Abstract
Abstract Energy storage challenges have triggered growing interest in various battery technologies and electrocatalysis. As a particularly promising variety, the Li–O2 battery with an extremely high energy density is of great significance, offering tremendous opportunities to improve cell performance via understanding catalytic mechanisms and the exploration of new materials. Furthermore, focus on nonaqueous electrolyte‐based Li–O2 batteries has markedly intensified since there could be a higher probability of commercialization, compared to that of solid‐state or aqueous electrolytes. The recent advancements of the nonaqueous Li–O2 battery in terms of fundamental understanding and material challenges, including electrolyte stability, water effect, and noncarbon cathode materials are summarized in this review. Further, the current status of water impact on discharge products, possible mechanisms, and parasitic reactions in nonaqueous electrolytes are reviewed for the first time. The key challenges of noncarbon oxygen electrode materials, such as noble metals and metal oxides‐based cathodes, transition metals, transition metal compounds (carbides, oxides) based cathodes as well as noncarbon supported catalysts are discussed. This review concludes with a perspective on future research directions for nonaqueous Li–O2 batteries.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".