Interfacial Super‐Assembled Porous CeO<sub>2</sub>/C Frameworks Featuring Efficient and Sensitive Decomposing Li<sub>2</sub>O<sub>2</sub> for Smart Li–O<sub>2</sub> Batteries
Bibliographic record
Abstract
Abstract The Li–O2 battery (LOB) represents a promising candidate for future electric vehicles owing to its outstanding energy density. However, the practical application of LOB cells is largely blocked by the poor cycling performance of cathode materials. Herein, an ultralong 440‐cycle life of an LOB cell is achieved using CeO2 nanocubes super‐assembled on an inverse opal carbon matrix as the cathode material without any additives. CeO2 is proved to be effective for the complete and sensitive decomposition of loosely stacked Li2O2 films during the oxygen evolution reaction process and full accommodation of volume changes caused by the fast growth of Li2O2 films during the oxygen reduction reaction process. The super‐assembled porous CeO2/C frameworks satisfy critical requirements including controlled size, morphology, high Ce3+/Ce4+ ratio, and efficient volume change accommodation, which dramatically increase the cycle life of LOB cell to 440 cycles. This study reveals the design strategy for high performance CeO2 catalyst cathodes for LOB cells and the generation mechanisms of Li2O2 films during the discharge process by using density functional theory calculations, showing new avenues for improving the future smart design of CeO2‐based cathode catalysts for 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".