Tracking the Fate of Excess Li in the Synthesis of Various Liy[Ni<sub>1−x</sub>Mn<sub>x</sub>]O<sub>2</sub> Positive Electrode Materials Under Different Atmospheres
Bibliographic record
Abstract
Various Ni-rich Liy[Ni1−xMnx]O2 (x = ∼0.08, 0.2, 0.5) materials were synthesized with excess Li precursor in oxygen, dry air or air to understand what happens to the excess Li during synthesis. The Li[Ni1−xMnx]O2 components of the synthesized materials were single phase and synthesis in oxygen produced materials with less Ni in the Li layer. Inductively coupled plasma optical emission spectrometry (ICP-OES) and titration experiments on as-prepared samples and samples that were rinsed with water are useful in the determination of the amount of Li lost during heating, the amount of Li taken in by the material during synthesis and the amount of residual Li present in the samples as impurity phases. Materials synthesized in oxygen and dry air lost a similar amount of Li during heating but synthesis in air resulted in more Li loss. Synthesis in oxygen increased the lithium content, y, in the Liy[Ni1−xMnx]O2 materials. Materials with a higher Mn content can take in more Li to form Li-rich materials with larger values of y. From these experiments, the fate of Li can be tracked to heating loss, residual Li as impurity phases or uptake into the material as a function of Mn content and synthesis atmosphere.
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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".