Environmental In Situ X-ray Absorption Spectroscopy Evaluation of Electrode Materials for Rechargeable Lithium–Oxygen Batteries
Bibliographic record
Abstract
Lithium–oxygen batteries have attracted much recent attention due their high theoretical capacities, which exceeds that of Li-ion batteries. Among all the metal oxides that have been investigated in oxygen cathodes, α-MnO 2 materials have shown unique electrochemical properties in rechargeable lithium oxygen batteries. Although extensive research has been performed to investigate the structure of α-MnO 2 upon lithium intercalation, its behavior upon reacting with lithium under an oxygen environment remains to be fully explored. Here, we performed a systematic study on the behavior of two forms of α-MnO 2 nanowires (i.e., potassium and ammonia versions) together with bulk α-MnO 2 in oxygen cathodes through environmental in situ X-ray absorption spectroscopy. The results show that the α-MnO 2 materials undergo lithium insertion/removal and lithium peroxide formation/decomposition simultaneously. The former causes a self-switching of the oxidation state of Mn during cycling. Additionally, we found that potassium-containing α-MnO 2 nanowires exhibit a suppression of Mn reduction until late in cell discharge under oxygen, retaining a higher degree of Mn 4+ character for enhanced oxygen reduction activity than other, similar α-MnO 2 materials. During cell recharge along with oxygen evolution, the materials were found to return to their initial states at low overpotential.
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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".