Investigating the Effects of Magnesium Doping in Various Ni-Rich Positive Electrode Materials for Lithium Ion Batteries
Bibliographic record
Abstract
This work studies the effect of Mg doping in LiMO 2 (M = Ni, Ni+Al, Ni+Co+Al, Ni content >0.8) at a dopant level of less than 5%. Synthesized materials were all single phase and contained no appreciable amount of surface or bulk impurities. Structural and electrochemical characterization of the materials was carried out to understand how Mg affects the materials and whether the presence of Al or Co influences the dopant effects of Mg. All synthesized materials, even those without Co, were found to contain a small amount of Ni in the Li layer. Increases in the Mg content of the material reduced the initial capacity of the materials but improved the capacity retention. The capacity reduction was related to the amount of Li trapped by electrochemically inactive Al and Mg. The capacity retention was related either to the number of Ni ions substituted by electrochemically inactive dopants or to the amount of Li trapped by those dopants. Mg helped improve capacity retention by reducing the growth of polarization during cycling. No synergistic effects were found to exist from co-doping materials containing Co and/or Al with Mg.
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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) | 0.000 | 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 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".