Non‐Aqueous and Hybrid Li‐O<sub>2</sub> Batteries
Bibliographic record
Abstract
Abstract With the increasing importance of electrified transport, the need for high energy density storage is also increasing. Possible candidates include Li‐O2 batteries, which are the subject of rapidly increasing focus worldwide despite being in their infancy of understanding. This excitement owes to the high energy density of Li‐O2 (up to 2‐3 kWh kg−1), theoretically much higher compared to that of other rechargeable systems, and the open “semi‐fuel” cell battery configuration that uses oxygen as the positive electrode material. To bring Li‐O2 batteries closer to reality as viable energy storage devices, and to attain suitable power delivery, understanding of the underlying chemistry is essential. Several concepts have been proposed in the last year to account for the function and target future design of Li‐O2 batteries and these are reviewed. An overview is given of the efforts to understand oxygen reduction/evolution and capacity limitations in these systems, and of electrode and electrolyte materials that are suitable for non‐aqueous and hybrid (nonaqueous/aqueous) cells.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".