Facet‐dependent Thermal and Electrochemical Degradation of Lithium‐rich Layered Oxides
Bibliographic record
Abstract
Lithium-rich layered oxides (LLOs) are promising candidate cathode materials for safe and inexpensive high-energy-density Li-ion batteries. However, oxygen dimers are formed from the cathode material with oxygen activity, which can result in morphological changes and structural transitions that result in performance deterioration and safety concerns. Herein, we prepare a flake-like LLO and using aberration-corrected scanning transmission electron microscopy (STEM), In-situ high-temperature XRD (HT-XRD) and soft X-ray absorption spectrum (sXAS) to explore its crystal-facet degradation behavior in both thermal and electrochemical processes. We find that degradation of the cathode material is strongly facet-dependent and, more importantly, depends on oxygen activity at high voltage. Particle degradation originates from side facets, such as the (010) facet, and oxygen loss, while the close (003) facet is stable. These results are further understood through ab initio molecular dynamics calculations, which show that oxygen atoms are lost from the {010} facets. The study reveals important aspects of the mechanism responsible for the degradation of LLO cathode materials with oxygen anionic activity for use in lithium-ion batteries. Importantly, this study provides insight that enables precise and efficient measures to be taken to improve the thermal and electrochemical stability of an LLO.
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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) | 0.000 | 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 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".