A Baseline Kinetic Study of Co-Free Layered Li<sub>1+x</sub>(Ni<sub>0.5</sub>Mn<sub>0.5</sub>)<sub>1−x</sub>O<sub>2</sub> Positive Electrode Materials for Lithium-Ion Batteries
Bibliographic record
Abstract
Variations of Li chemical diffusion coefficient ( ) with voltage in a series of Co-free Li 1+x (Ni 0.5 Mn 0.5 ) 1−x O 2 , 0 ≤ x ≤ 0.12, materials were systematically investigated using the recently developed "Atlung Method for Intercalant Diffusion". The effects of primary and secondary particle sizes, excess Li content, heating temperature, and synthesis atmospheres on were measured. Li-ion kinetics can be enhanced by an order of magnitude by lowering the amount of Ni atoms in the Li layers (cation mixing) from 10% to 4%. Decreasing cation mixing can be accomplished by either increasing the excess Li content or heating temperature. When cycled to 4.6 V, higher specific capacities were obtained, but with a penalty to due to transition metal migration to the Li layers. The primary particles control the Li diffusion length in these materials, regardless of the secondary particle size indicating that grain boundary diffusion must be very rapid. The general trends observed in this work are of great value for the development of higher Mn-containing, Co-free materials. It should be possible to increase energy/power density by making large secondary particles, composed of small primary particles to minimize the solid-state diffusion length while maximizing grain boundary diffusion.
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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".