Raman Spectroscopy in Lithium–Oxygen Battery Systems
Bibliographic record
Abstract
Abstract Electrochemical processes in lithium–oxygen (Li–O2 or Li–air) batteries are complex, with chemistry depending on cycling conditions, electrode materials and electrolytes. In non‐aqueous Li–O2 cells, reversible lithium peroxide (Li2O2) and irreversible parasitic products (i.e., LiOH, Li2CO3, Li2O) are common. Superoxide intermediates (O2−, LiO2) contribute to the formation of these species and are transiently stable in their own right. While characterization techniques like XRD, XPS and FTIR have been used to observe many Li–O2 species, these methods are poorly suited to superoxide detection. Raman spectroscopy, however, may uniquely identify superoxides from O−O vibrations. The ability to fingerprint Li–O2 products in situ or ex situ, even at very low concentrations, makes Raman an essential tool for the physicochemical characterization of these systems. This review contextualizes the application of Raman spectroscopy and advocates for its wider adoption in the study of 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".