Correlating Cation Mixing with Li Kinetics: Electrochemical and Li Diffusion Measurements on Li-Deficient LiNiO<sub>2</sub> and Li-Excess LiNi<sub>0.5</sub>Mn<sub>0.5</sub>O<sub>2</sub>
Bibliographic record
Abstract
Cation mixing in Li-based layered positive electrode materials has been reported to negatively affect the electrochemical performance and transport properties of intercalated Li. However, no previous reports have systematically correlated the impact of cation mixing (Ni atoms in the Li layer) on the electrochemical properties and Li transport. Herein, a series of Li-deficient LNO (Li 1−x Ni 1+x O 2 ) materials with different amounts of Ni in the Li layers ranging from ca. 1.5%–6.0% was intentionally prepared by varying the Li/Ni ratio during synthesis. An order of magnitude decrease in the Li chemical diffusion coefficient was found between samples with 1.5% and 6% Ni in the Li layer. A similar dependence of the diffusion constant on the amount of Ni in the Li layer was also observed in the Li-excess materials Li 1 + x Ni 0.5 Mn 0.5 1 − x O 2 for x = 0, 0.04, 0.08, 0.12, suggesting that, in general, larger amounts of Ni in the Li layer will lead to worse kinetics. This work quantitatively demonstrates that the amount of Ni in the Li layer needs to be carefully considered for the development of high-energy Ni-containing layered positive electrode materials as it directly affects overall electrochemical performance, phase transitions, and Li diffusion, leading to worse kinetics and seriously hindering rate capability.
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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 | 0.001 | 0.001 |
| 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".