A Search for Low-Irreversible Capacity and High-Reversible Capacity Positive Electrode Materials in the Li–Ni–Mn–Co Pseudoquaternary System
Bibliographic record
Abstract
A comprehensive search for Li-ion battery positive electrode materials that can simultaneously exhibit low irreversible capacity loss (IRC) (∼10% or less) and high reversible capacity (>240 mAh/g) was performed in the Li–Ni–Mn–Co–O pseudoquaternary system. An array of high-capacity Li-rich layered oxides, most of which show an “oxygen release” plateau during the first charge, were synthesized with a wide range of Ni, Mn, and Co compositions, and their first-cycle electrochemical properties were investigated. Low-IRC materials could be synthesized at many Ni–Mn–Co combinations by synthesizing with an amount of lithium lower than that required by site occupation and oxidation state rules. Many of these “Li-deficient” low-IRC materials were found to be single-phase layered materials with inherent metal-site vacancies in their pristine state. For such single-phase materials, the amount of IRC depends on the concentration of metal-site vacancies in their pristine state. Increasing the Li deficiency eventually caused the appearance of the spinel phase, which, when it appears, lowers the IRC, irrespective of the Ni–Mn–Co precursor composition. The number of metal-site vacancies that can be incorporated into the single-phase layered materials depends on the overall metal composition, especially the Co concentration. Low-IRC behavior is correlated to the fraction of metal-site vacancies in the layered phase in both the single-phase and the two-phase materials. 7 Li nuclear magnetic resonance (NMR) studies of low-IRC materials revealed the relative population of Li between the Li and TM layer. Formula unit calculation based on 7 Li NMR results suggests that metal-site vacancies preferably occupy the sites in the Li layer, which could provide room for the intercalation of extra Li into the structure, hence reducing the irreversible capacity.
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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".