Combinatorial Investigation of the Impact of Systematic Al Substitution into LiNi<sub>1–<i>x</i>–<i>y</i></sub>Mn<sub><i>x</i></sub>Co<sub><i>y</i></sub>O<sub>2</sub> Materials
Bibliographic record
Abstract
State-of-the-art Li-ion batteries in long-range electric vehicles increasingly rely on LiNi 1– x – y Mn x Co y O 2 (NMC) cathodes with a trend toward higher Ni content. While high-Ni compositions can increase capacity, they also decrease the material’s stability and therefore battery lifetime. Though aluminum has been substituted into a few NMC compositions, to date, no systematic study has been performed. In this study, the impact of different levels of Al substitution was explored across the full range of NMC compositions by preparing a total of 320 materials with varying aluminum content from 0 to 15%. Both X-ray diffraction (XRD) and cyclic voltammetry were performed on all samples. The single-phase layered oxide region is quite large when no Al is present and decreases slightly in size as aluminum content increases. The nickel-rich region (1 – x – y > 0.8) was entirely single phase for 0 and 5% Al levels, while at higher Al content small amounts of Li–Al–O phases were found as contaminants detrimental to the electrochemistry, indicating an optimal Al level in high-Ni materials. Key battery metrics including discharge capacity, average voltage, and diffusion coefficients were obtained for all materials and correlated to structural changes seen in the XRD. It was found that Al suppressed the detrimental hexagonal phase transitions at high voltage (∼4.3 V) in Ni-rich compositions. Increased Al content also improved the diffusion coefficients in the Ni-rich materials. Overall, this study provides the first generalized view of the effects of Al substitution on NMC cathode materials. This work can serve as a base for further exploration into Al substitution on less-studied NMC compositions.
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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".