Synergistic effects of low - level magnesium and chromium doping on the electrochemical performance of LiNiO2 cathodes
Bibliographic record
Abstract
Abstract LiNiO 2 cathode materials with magnesium and chromium doping were prepared using a simple low - cost and efficient co - precipitation and lithiation procedure. During this procedure, both magnesium and chromium form a concentrated core particle, unto which nickel hydroxide precipitates. During lithiation, the elements in question will redistribute themselves and form a homogenous mixture. Magnesium - containing materials exhibit an excellent electrochemical performance, due to phase stabilizing effects, while for chromium - containing materials, performance remains poor. Rietveld refinement reveals that there is a possible upper limit for Mg doping (~ 2.5 mol %) as a pilar dopant. Washing of the lithiated materials was explored, and it is proposed that this can improve capacity retention in prolonged cycling. However, the inevitable loss of lithium from the surface layer remains a challenge. Two sources for the chromium facilitated capacity decay are proposed, both owing to the highly irreversible redox reaction of hexavalent chromium possibly blocking lithium pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".