The Role of Metal Site Vacancies in Promoting Li–Mn–Ni–O Layered Solid Solutions
Bibliographic record
Abstract
The Li–Mn–Ni-O system has received much attention for potential positive electrode materials in lithium-ion batteries. Recent work mapping the phase diagrams of the entire pseudo-ternary system showed that the layered solid-solution region extends to compositions with both less and more lithium than the well-known lithium-rich layered composition line that joins Li 2 MnO 3 to LiNi 0.5 Mn 0.5 O 2 . The part of this solid-solution region that is lithium deficient has a “bump” feature in the single-phase boundary, which could not be explained until now. The current study explores this part of the phase diagram with the use of X-ray diffraction, helium pycnometry measurements, redox titrations, and a Monte Carlo simulation. Results show that metal site vacancies are present in the structures in increasing amounts as the lithium content of the samples decreases. A Ni 2+ ion and a vacancy can replace two Li + ions in Li[Li 1/3 Mn 2/3 ]O 2 to make the solid solution series Li[Li (1/3)– x Ni x /2 □ x /2 Mn 2/3 ]O 2 with 0 < x < 1 / 3 . The most lithium-deficient structures contain sufficient vacancies to allow manganese to form on two-thirds ( 2 / 3 ) of the transition-metal layer, such that the ordering of manganese on two √3 × √3 lattices yields a structure with low internal energy and sharp superlattice peaks in XRD patterns. The material with the maximum theoretical vacancy fraction that still has two-thirds of the transition-metal layer filled with manganese, Li[Ni 1/6 □ 1/6 Mn 2/3 ]O 2, was also synthesized. Both XRD and electrochemical data regarding this new material are presented.
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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 | 0.000 | 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 teacher head, 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".