The Improved Electrochemistry of Single-Phase Layered Li-Mn-Ni-O Materials over That of Layered-Layered Nano-Composites
Bibliographic record
Abstract
The Li-Mn-Ni-O system has received much attention for potential positive electrode materials in lithium ion batteries. In recent work [1-4], the entire phase diagram has been mapped out. Using the phase diagram as a guide, it is possible to select compositions near the boundary of the layered region. These materials can either be single phase layered if prepared by quenching from high temperature or layered-layered nano-composites if cooled more slowly. Work presented here will compare the electrochemistry of materials made under various synthesis conditions at such compositions. Here, two compositions near LiNi0.5Mn0.5O2are studied under various oxygen partial pressures and cooling rates. Figure 1 (top) shows the phase diagram with the two compositions studied, A and B (B is slightly lithium rich compared to A). XRD patterns and peak width analysis will be used to show that the materials made at B are single-phase while the only material made at A that was single-phase was made in 2% oxygen and quenched. The samples made in air showed peak broadening attributed to phase separation and the non-quenched sample in 2% oxygen showed the smallest signs of phase separation (high angle peak broadening only). Figure 1 (bottom) shows that the capacities of the single-phase materials are all higher than those that show phase separation and the cycling stability is comparable. The poor performance of the layered-layered composites is attributed to the compositions of the two phases: they are not Li2MnO3 and LiNi0.5Mn0.5O2 as promoted in the literature [5]. Instead, the end-members both contain some nickel and one contains a high proportion of nickel on the lithium layer (as high as 30% depending on synthesis conditions). Interestingly, the sample that showed the smallest sign of phase separation in the XRD (2% O2, RC) had the largest drop in capacity (140 to 100 mAh/g) compared to the quenched sample. The dramatic decrease in capacity in the sample showing the first signs of forming a layered-layered nano-composite suggests that layered-layered nano-composities should be avoided in the Li-Mn-Ni-O system. The approach often used in research experiments of adding a small amount of excess lithium then serves to help keep the material single phase which improves the electrochemistry. Figure 1: Top: a partial Li-Mn-Ni-O phase diagram showing how the upper layered boundary moves with temperature, synthesis condition (Q is quench, RC is regular cooling at a rate of 5°C/min) and atmosphere (air versus 2% O2). For all conditions, the lower layered boundary is the curved solid line joining Li2MnO3 to LiNiO2. The red lines indicate the a lattice parameter contour plots (the corresponding cones will be shown also) while the blue dotted line is a rocksalt to layered phase transition. Bottom: capacity vs. cycle number for materials made at compositions A and B in the top panel. References: [1] E. McCalla and J.R. Dahn, Solid State Ionics 242, 1 (2013). [2] E. McCalla, A.W. Rowe, R. Shunmugasundaram, and J.R. Dahn, Chem. Mater. 25, 989 (2013). [3] E. McCalla, A.W. Rowe, C.R. Brown, L.R.P. Hacquebard and J.R. Dahn, J. Electrochem. Soc. 160, A1134 (2013). [4] E. McCalla, A.W. Rowe, J. Camardese and J.R. Dahn, Chem. Mater. 25, 2716 (2013). [5] M. M. Thackeray, C. S. Johnson, J. T. Vaughey and S. A. Hackney, J. Mater. Chem. 15, 2257 (2005).
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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 | 0.001 |
| 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".