A Study of Stacking Faults and Superlattice Ordering in Some Li-Rich Layered Transition Metal Oxide Positive Electrode Materials
Bibliographic record
Abstract
Li-rich layered transition metal oxides such as Li[Li 0.2 Ni 0.2 Mn 0.6 ]O 2 have in-plane ordering between the excess Li atoms and the transition metal (TM) atoms in the transition metal layer. The √3a × √3a superlattice in the TM layer causes superlattice Bragg peaks in their X-ray diffraction patterns. This article describes the relation between the metal composition of the materials, stacking faults and superlattice ordering. The XRD patterns were fitted with a program called FAULTS, which treats the effect of stacking faults on the superlattice peak shapes. The superlattice peak positions of Li[Li 1/3-2x/3 Ni x Mn 2/3-x/3 ]O 2 materials changed monotonically with Ni content (x), as did the positions of the main diffraction peaks of the base structure. This proves that the superlatices peaks originate from the Li[Li 1/3-2x/3 Ni x Mn 2/3-x/3 ]O 2 solid solution and are not caused by any domains of second phase such as Li 2 MnO 3 . Fitting the XRD patterns with FAULTS revealed that the stacking fault probability increased monotonically with Ni content.
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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) | 0.000 | 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; 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".