Co-precipitation of Mg-doped Ni<sub>0.8</sub>Co<sub>0.1</sub>Mn<sub>0.1</sub>(OH)<sub>2</sub>: effect of magnesium doping and washing on the battery cell performance
Bibliographic record
Abstract
(NCM811) and Mg-doped (0.25 wt% and 0.5 wt%) NCM811 precursors is carried out from concentrated metal sulphate solutions. In this paper, the aim is to study the role of magnesium dopant in the co-precipitation step of NCM811, the cathode active material and further the Li-ion battery cell performance. Based on the results, magnesium was fully co-precipitated in the NCM811 precursors, as expected from thermodynamic calculations. The presence of magnesium in these precursors was also confirmed by several characterization methods and magnesium was evenly distributed in the sample. It was observed that tapped density decreased and surface area increased with an expected increase in Mg content. Surprisingly, Mg doping did not improve the cyclability of coin cells, due to the stable crystal structure of NCM811. However, a slight improvement in cyclability was seen in pouch cells after 1000 cycles. A washing effect was clearly seen in lattice parameters and washing also decreased the capacity retention after 62 cycles for all samples.
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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".