Thermal Safety Analysis of Disordered Li-Rich Rock salt Li<sub>1.3</sub>Mn<sub>0.4</sub>Nb<sub>0.3</sub>O<sub>2</sub> Cathode
Bibliographic record
Abstract
The improvement of Li-ion battery energy density greatly depends on the cathode composition/material. Recently, a Li-rich rock salt cathode, Li 1.3 Mn 0.4 Nb 0.3 O 2, has gained significant attention as a promising cathode material because of its ability to extract more than one Li reversibly leading to a high theoretical capacity (>300 mA h g –1 ) and high operating potential (>4 V). However, rapid capacity decay, voltage fade, and increased voltage hysteresis with cycling still need to be addressed despite the intense effort to understand the electrochemical behavior and degradation mechanism. Furthermore, there is little understanding of the thermal properties and their implication on battery safety. Considering this important knowledge gap, we studied the thermal decomposition mechanism in a Li-rich rock salt cathode system under different charged and discharged conditions employing differential scanning calorimetry (DSC). The DSC results infer that Li 1.3 Mn 0.4 Nb 0.3 O 2 under discharged conditions exhibited the least thermal stability as compared to both charged and pristine ones with an exothermic onset temperature of 118 °C. Moreover, the results show that Li 1.3 Mn 0.4 Nb 0.3 O 2 is more likely to cause a thermal runaway as compared to the state-of-the-art of cathode materials. In addition, the temperature-dependent scanning transmission electron microscopy–energy-dispersive X-ray spectrometry mapping and ex situ X-ray diffraction measurements suggest that the thermal stability of Li 1.3 Mn 0.4 Nb 0.3 O 2 is limited by the reaction of the transition metal with electrolyte.
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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".