Investigating the Effects of Magnesium Doping in Various Ni-Rich Materials
Bibliographic record
Abstract
As lithium ion battery technology expands into more demanding applications such as electric vehicles, attention has shifted towards nickel-rich positive electrode materials, namely LiNi 1-x-y Mn x Co y O 2 (NMC) and LiNi 1-x-y Co x Al y O 2 (NCA). 1 Aims to improve energy density and reduce costs of NMC and NCA can be achieved by increasing the Ni content of the material, but at the cost of shorter cell lifetimes. Besides Al, Co and Mn, research has tried substituting Ni with many different metals. 1 Mg has repeatedly come up as a beneficial dopant from positive electrode material doping studies that try to improve material cycling performances. Mg has been shown to improve cycling performances of various positive electrode materials including LiCoO 2 (LCO), NMC, and NCA when doped into the material in small amounts. 1–4 Doping the material with Mg slightly reduces the specific capacity of the material since Mg is an inactive constituent, but it has been shown that a Mg content of as low as 1 mol% can improve cycling. 3 LiNiO 2 (LNO) and some other Ni-rich materials undergo phase transitions as Li gets removed during charging. In particular, when the material transitions from the H2 phase to the H3 phase at low Li, it experiences a large volume change. It is believed that this contributes to a poor lifetime of the material. Mg doping has been shown to suppress these phase transitions and related volume changes. 2,4 Mg has also been shown to reduce the amount of Ni migration to the Li layer. 1 As research continues to increase the Ni content of NMC and NCA, the compositions will invariable converge towards LNO. Additionally, efforts are being made to reduce the Co content of Ni-rich materials due to cost and sourcing issues. 4 Co is believed to reduce the amount of Ni in the Li layer and also to stabilize cycling performance, which are similar benefits that Mg imparts. 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 mol%. 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. Figure 1 shows the initial half-cell voltage vs capacity and differential capacity vs voltage curves for a series of LiNi 0.88-x Co 0.09 Al 0.03 Mg x O 2 (x = 0, 0.01, 0.02, 0.04) materials, showing a trend of how Mg affects the electrochemical performance of the materials. (1) Kim, J.; Lee, H.; Cha, H.; Yoon, M.; Park, M.; Cho, J. Adv. Energy Mater. 2018 , 8 , 1–25. (2) Sasaki, T.; Godbole, V.; Takeuchi, Y.; Ukyo, Y.; Novák, P. J. Electrochem. Soc. 2011 , 158 , A1214–A1219. (3) Huang, B.; Li, X.; Wang, Z.; Guo, H.; Xiong, X. Ceram. Int. 2014 , 40 , 13223–13230. (4) Li, H.; Cormier, M.; Zhang, N.; Inglis, J.; Li, J.; Dahn, J. R. J. Electrochem. Soc. 2019 , 166 , A429–A439. Figure 1
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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 | 0.001 | 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".