Magnetism in Lithium–Oxygen Discharge Product
Bibliographic record
Abstract
Nonaqueous lithium-oxygen batteries have a much superior theoretical gravimetric energy density compared to conventional lithium-ion batteries, and thus could render long-range electric vehicles a reality. A molecular-level understanding of the reversible formation of lithium peroxide in these batteries, the properties of major/minor discharge products, and the stability of the nonaqueous electrolytes is required to achieve successful lithium-oxygen batteries. We demonstrate that the major discharge product formed in the lithium-oxygen cell, lithium peroxide, exhibits a magnetic moment. These results are based on dc-magnetization measurements and a lithium-oxygen cell containing an ether-based electrolyte. The results are unexpected because bulk lithium peroxide has a significant band gap. Density functional calculations predict that superoxide-type surface oxygen groups with unpaired electrons exist on stoichiometric lithium peroxide crystalline surfaces and on nanoparticle surfaces; these computational results are consistent with the magnetic measurement of the discharged lithium peroxide product as well as EPR measurements on commercial lithium peroxide. The presence of superoxide-type surface oxygen groups with spin can play a role in the reversible formation and decomposition of lithium peroxide as well as the reversible formation and decomposition of electrolyte molecules.
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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) | 0.002 | 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".