Synthesis, Characterization, and Thermal Stability of LiNi<sub>1/3</sub>Mn<sub>1/3</sub>Co<sub>1/3−<i>z</i></sub>Mg<sub><i>z</i></sub>O<sub>2</sub>, LiNi<sub>1/3−<i>z</i></sub>Mn<sub>1/3</sub>Co<sub>1/3</sub>Mg<sub><i>z</i></sub>O<sub>2</sub>, and LiNi<sub>1/3</sub>Mn<sub>1/3−<i>z</i></sub>Co<sub>1/3</sub>Mg<sub><i>z</i></sub>O<sub>2</sub>
Bibliographic record
Abstract
LiNi 1/3 Mn 1/3 Co 1/3− z Mg z O 2, LiNi 1/3− z Mn 1/3 Co 1/3 Mg z O 2, and LiNi 1/3 Mn 1/3− z Co 1/3 Mg z O 2 (0≤ z ≤ 1/3) were prepared from hydroxide precursors. The hydroxide precursors were heated with Li 2 CO 3 at 900 °C to prepare the oxides. Rietveld refinements of XRD data show that Mg substitution for Co, Ni and Mn results in different degrees of cation mixing in the Li layer with very little cation mixing in LiNi 1/3 Mn 1/3− z Co 1/3 Mg z O 2 and the most cation mixing in LiNi 1/3 Mn 1/3 Co 1/3− z Mg z O 2 . Electrochemical studies of the LiNi 1/3 Mn 1/3 Co 1/3− z Mg z O 2, LiNi 1/3− z Mn 1/3 Co 1/3 Mg z O 2, and LiNi 1/3 Mn 1/3− z Co 1/3 Mg z O 2 (0 ≤ z ≤ 1/3) samples were used to measure the rate of capacity reduction with Mg content, found to be about −389 (mAh/g)/( z = 1) independent of which cation was substituted by Mg. The impact of Mg substitution on the thermal stability of NMC samples was studied via accelerating rate calorimetry and compared with Al-substituted NMC samples. The substitution of Mg did not improve the thermal stability of the samples, independent of which cation was substituted and independent of the amount of Mg added, in contrast to the effect of Al, which dramatically improves thermal stability.
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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 | 0.033 | 0.016 |
| Meta-epidemiology (narrow) | 0.042 | 0.051 |
| Meta-epidemiology (broad) | 0.043 | 0.015 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.025 | 0.016 |
| Research integrity | 0.029 | 0.026 |
| Insufficient payload (model declined to judge) | 0.001 | 0.010 |
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".