Implications of Metal-Site Vacancies on Li-Ni-Mn-Co Based Positive Electrode Materials
Bibliographic record
Abstract
One of the key issues with Li-excess positive electrode materials is their high irreversible capacity loss (IRC), which is usually ~ 20 % of their first charge capacity. Li-excess materials with IRC as low as 4% have been recently reported by us1. Those materials were intentionally synthesized with less Li than the stoichiometric amount, based on oxidation state rules, and as a result, metal-site vacancies were found in their single-phase, layered, pristine structures. With metal-site vacancies, the structure can be written as Li[ΔqM(1-q)]O2, where Δ is a metal site vacancy and the transition metal layer has no Li atoms. An elemental analysis of metals only on such materials would conclude they were Li rich because when q > 0, the number of moles of Li is greater than the number of moles of transition metal atoms, even though there are no Li atoms in the transition metal layer. The small IRC was found to be related to the presence of metal site vacancies. Following that work, a comprehensive search for materials that contain metal-site vacancies was performed in the Li-Ni-Mn-Co pseudo-ternary system. An array of materials with deliberate Li-deficiency and a wide-range of Ni, Mn, and Co compositions was synthesized and their properties were investigated. It was found that, in the Li-Ni-Mn-Co pseudo-ternary system, materials with metal-site vacancies can be synthesized at many Ni-Mn-Co combinations by forcing Li deficiency. Most of the materials were layered single-phase materials but increasing Li deficiency eventually caused the evolution of a spinel phase. The presence of metal-site vacancies were verified by density measurements made with a He-pycnometer. Figure 1 shows the XRD patterns of several single-phase materials (a to f), which have considerable amount of metal-site vacancies, in the range of 20° to 34° representing superlattice ordering between TM ions and vacancies in the TM layer similar to that reported by McCalla et al2. The relative intensity of superlattice peaks varied with overall metal composition (not shown here) suggesting different degrees of ordering. For example, the absence of superlattice peaks in samples e and f suggests that only a negligible amount of vacancies reside in the TM layer whereas the prominent superlattice peaks in samples a and b suggests a significant amount of vacancies in the TM layer. Thus the nature and the relative intensity of the superlattice peaks can be used as a first approximation to predict the location and distribution of vacancies between TM and Li layers. Detailed results on the implications of metal-site vacancies on the properties of Li-Ni-Mn-Co based positive electrode materials will be presented. References: Shunmugasundaram, R.; Senthil Arumugam, R.; Dahn, J. R. Chem. Mater. 2015,27, 757–767 McCalla, E.; Rowe, A. W.; Camardese, J.; Dahn, J. R. Chem. Mater. 2013, 25, 2716–2721 Figure 1
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".