Structural, Electrochemical, and Thermal Properties of Nickel-Rich LiNi<sub><i>x</i></sub>Mn<sub><i>y</i></sub>Co<sub><i>z</i></sub>O<sub>2</sub> Materials
Bibliographic record
Abstract
Nickel-rich LiNixMnyCozO2 materials (x + y + z = 1, x ≥ 0.6) (NMC) are one of the most promising positive electrode candidates for lithium-ion cells due to their high specific capacity, ease of production, and moderate cost. Conventional NMC materials such as LiNi0.4Mn0.4Co0.2O2 (NMC442), LiNi0.5Mn0.3Co0.2O2 (NMC532), LiNi0.6Mn0.2Co0.2O2 (NMC622), etc. have 20% of costly Co among the transition metal atoms. To lower the Co content while maintaining good electrochemical performance, three series of materials with different transition metal ratios, LiNi0.6Mn0.4–xCoxO2 (x = 0, 0.1, 0.2), LiNi0.9–xMnxCo0.1O2 (x = 0.1, 0.2, 0.25), and LiNi0.8Mn0.2–xCoxO2 (x = 0, 0.1, 0.2), were studied. The materials were synthesized via a coprecipitation/solid state sintering method. Powder X-ray diffraction and electrochemical measurements using coin-type cells were made to characterize the materials. Accelerating rate calorimetry was used to study the reactivity of charged NMC positive electrode materials in the presence of electrolyte at elevated temperatures. NMC721, NMC631, and NMC6.5:2.5:1, which have 50% less Co content than current commercialized NMC622, exhibited excellent specific capacity and thermal stability and therefore deserve careful consideration as next generation materials.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".