Novel Complex Stacking of Fully-Ordered Transition Metal Layers in Li<sub>4</sub>FeSbO<sub>6</sub> Materials
Bibliographic record
Abstract
As part of a broad project to explore Li 4 MM′O 6 materials (with M and M′ being selected from a wide variety of metals) as positive electrode materials for Li-ion batteries, the structures of Li 4 FeSbO 6 materials with both stoichiometric and slightly deficient lithium contents are studied here. For lithium content varying from 3.8 to 4.0, the color changes from yellow to black and extra superstructure peaks are seen in the XRD patterns. These extra peaks appear as satellites around the four superstructure peaks affected by the stacking of the transition metal atoms. Refinements of both XRD and neutron scattering patterns show a nearly perfect ordering of Li, Fe, and Sb in the transition metal layers of all samples, although these refinements must take the stacking faults into account in order to extract information about the structure of the TM layers. The structure of the most lithium rich sample, where the satellite superstructure peaks are seen, was determined with the help of HRTEM, XRD, and neutron scattering. The satellites arise due to a new stacking sequence where not all transition metal layers are identical but instead two slightly different compositions stack in an AABB sequence giving a unit cell that is four times larger than normal for such monoclinic layered materials. The more lithium deficient samples are found to contain metal site vacancies based on elemental analysis and Mössbauer spectroscopy results. The significant changes in physical properties are attributed to the presence of these vacancies. This study illustrates the great importance of carefully determining the final compositions in these materials, as very small differences in compositions may have large impacts on structures and properties.
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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".