Oxygen Anion Redox Chemistry Correlated with Spin State in Ni‐Rich Layered Cathodes
Bibliographic record
Abstract
Abstract Despite the low cost and high capacity of Ni‐rich layered oxides (NRLOs), their widespread implementation in electric vehicles is hindered by capacity decay and O release. These issues originate from chemo‐mechanical heterogeneity, which is mainly related to oxygen anion redox (OAR). However, what to tune regarding OAR in NRLOs and how to tune it remains unknown. In this study, a close correlation between the OAR chemistry and Li/Ni antisite defects is revealed. Experiments and calculations show the opposite effects of aggregative and dispersive Li/Ni antisite defects on the NiO 6 configuration and Ni spin state in NRLOs. The resulting broad or narrow spans for the energy bands caused by spin states lead to different OAR chemistries. By tuning the Li/Ni antisite defects to be dispersive rather than aggregative, the threshold voltage for triggering OAR is obviously elevated, and the generation of bulk‐O 2 ‐like species and O 2 release at phase transition nodes is fundamentally restrained. The OAR is regulated from irreversible to reversible, fundamentally addressing structural degradation and heterogeneity. This study reveals the interaction of the Li/Ni antisite defect/OAR chemistry/chemo‐mechanical heterogeneity and presents some insights into the design of high‐performance NRLO cathodes.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".