Synthesis of Co-Free Ni-Rich Single Crystal Positive Electrode Materials for Lithium Ion Batteries: Part I. Two-Step Lithiation Method for Al- or Mg-Doped LiNiO<sub>2</sub>
Bibliographic record
Abstract
Increasing the Ni content of a Ni-rich layered positive electrode material is one common way to improve energy density of Li-ion cells but normally leads to shorter cell lifetimes. Single crystalline materials have been shown to improve the cell lifetime by reducing the degree of material degradation. This first study in a two part series investigates the synthesis of Co-free single crystalline LiNi0.95Al0.05O2 and LiNi0.975Mg0.025O2 via a two-step lithiation method. This method consists of a first step heating of the precursors at high temperatures but with deficient Li to grow crystalline particles and then a second step at lower temperature to fully lithiate the material. The synthesized materials were characterized by scanning electron microscopy and X-ray diffraction to understand the impact of synthesis conditions. Single crystal materials were successfully synthesized, and Mg-containing single crystal materials achieved micron-sized particles with as low as 2% Ni in the Li layer. Al-containing single crystal materials could not avoid the formation of Li5AlO4 impurity for all conditions tested. The presence of Li or Mg and high temperatures were identified as factors that promote crystallite growth. Selected samples were characterized electrochemically and compared to their polycrystalline counterparts. Mg-containing single crystal materials are not yet competitive with their polycrystalline counterparts yet, and further understanding and development is needed.
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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".