Modeling the Effect of Lithium Superoxide Solvation and Surface Reduction Kinetics on Discharge Capacity in Lithium–Oxygen Batteries
Bibliographic record
Abstract
Li–O2 batteries could potentially yield significantly higher capacities than Li-ion batteries. Achieving high capacity requires efficient void-filling of the cathode by the insoluble insulating discharge product Li2O2, which forms by two competing mechanistic pathways. One is a surface-mediated pathway that leads to formation of thin films of Li2O2 on the electrode. The other is a solvent-mediated pathway, involving the solvation of a Li+–O2– intermediate that disproportionates and leads to the formation of large toroidal particles. As the solvent pathway produces large particles that are more efficient for void-filling than thin films produced by the surface pathway, there has been an active search for modifications that can promote the solvent pathway. We construct a model that demonstrates how discharge parameters influence each pathway and can be optimized to yield high capacity. We test the model with rotating ring-disk electrode experiments, which allow for the direct measurement of the relative contributions of solution and solvent pathways as a function of rotation rate, water content, voltage, and choice of solvent. We show that favorability of solvation of Li+–O2– has a weak effect on the solvation rate and a large effect on the surface pathway rate. This insight can help guide strategies to optimize capacity in Li–O2 batteries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".