Synthesis of Co-Free Ni-Rich Single Crystal Positive Electrode Materials for Lithium Ion Batteries: Part II. One-Step Lithiation Method of Mg-Doped LiNiO<sub>2</sub>
Bibliographic record
Abstract
This second study in a two part series investigates the synthesis of Co-free single crystalline Mg-doped LNO via the one-step lithiation method. The synthesized materials were characterized by scanning electron microscopy, X-ray diffraction and particle size analysis to understand the impact of synthesis conditions. Higher heating temperatures promoted grain growth but also increased the Ni content in the Li layer. Increasing the Li/TM ratio does not seem to have an effect on grain growth at lower temperatures but influences the formation of Li2O impurity. The separation of particle aggregates is required to improve the cycling performance of the material. The utilization of a lower temperature step after the calcination step can reduce the Ni content in the Li layer below what would be expected at the calcination temperature, and this can be used to grow larger grains while keeping an acceptable amount of Ni in the Li layer. However, all single crystalline materials are still not yet electrochemically competitive with polycrystalline materials and have lower capacities, higher irreversible capacities and similar cycling fade. The lower capacities of single crystalline materials stem from increased kinetic hindrances to Li diffusion. Cycling single crystalline materials at 55 °C can recover ∼20 mAh g−1 of discharge capacity and yield similar irreversible capacity compared with polycrystalline cells cycled at 30 °C.
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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 | 0.001 |
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".