<sup>6</sup>Li NMR Studies of Cation Disorder and Transition Metal Ordering in Li[Ni<sub>1/3</sub>Mn<sub>1/3</sub>Co<sub>1/3</sub>]O<sub>2</sub> Using Ultrafast Magic Angle Spinning
Bibliographic record
Abstract
Studies of Li[Ni 1/3 Mn 1/3 Co 1/3 ]O 2 prepared under six different conditions are compared using high-resolution solid-state 6 Li NMR. Differing degrees of cation disorder are established via integration of the NMR resonances, and this quantification of cation disorder is compared with Rietveld refinements of powder X-ray and neutron diffraction studies. Chemical shift trends to high frequency with decreasing degrees of disorder are established among this family of samples and explained according to the orbital overlap experienced by Li nuclei in the two environments: within the lithium layers and exchanged with nickel into the transition metal layers. Finally, an interesting case of local transition metal charge ordering is observed. Three unique environments are described, which can be accounted for based on electroneutrality arguments, and the known clustering of Ni 2+ and Mn 4+ . This effect has not been detected in these materials by other methods including neutron and X-ray diffraction. Thus, the local ordering, which is observed in the dominant NMR resonance of Li in its own layers is thought to be pervasive (affecting the majority of the NMR nuclei), but very local, so as to be seen only by techniques such as NMR which probe immediate neighborhoods.
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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".