Visualization of Thermal‐Induced Degradation Pathways of High‐Ni Cathode: a Comparative Study in Solid Chloride and Liquid Electrolytes
Bibliographic record
Abstract
Abstract Nickel‐rich‐layered oxide cathodes are promising candidates for enhancing the energy density of lithium‐ion batteries. Higher energy density leads to severe oxygen release, poor thermal stability, and safety risks, as exothermic side reactions induce complex structural and chemical transformations at elevated temperatures. Herein, in‐situ heating scanning transmission X‐ray microscopy (STXM)‐ptychography to directly investigate the thermal degradation pathways of charged LiNi 0.8 Co 0.1 Mn 0.1 O 2 in both solid chloride and liquid electrolytes is employed. A key finding is the opposite spatial degradation behavior: in solid electrolytes, oxygen loss and Ni reduction occur from the core to surface, while in liquid electrolytes, the degradation proceeds from surface to core. These observations are closely linked to the local structural disorder around Ni atoms, as oxygen loss directly weakens the Ni─O bonding environment, promoting the reduction of Ni and accelerating lattice instability at high temperatures. Additionally, the extent of degradation is found to correlate with particle size, with solid electrolytes effectively stabilizing smaller particles. These results reveal strong spatial heterogeneity in thermal degradation and highlight the critical role of electrolyte chemistry in dictating thermal stability. Our study provides new insights into the structural and chemical evolution of Ni‐rich cathodes under thermal stress, offering valuable guidance for the design of safer, high‐performance lithium‐ion 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 | 0.000 | 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) | 0.001 | 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".