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Factors that Affect Capacity in the Low Voltage Kinetic Hindrance Region of Ni-Rich Positive Electrode Materials and Diffusion Measurements from a Reinvented Approach

Factors that Affect Capacity in the Low Voltage Kinetic Hindrance Region of Ni-Rich Positive Electrode Materials and Diffusion Measurements from a Reinvented Approach

Liu, A., Phattharasupakun, N., Cormier, M. M. E., Zsoldos, E., Zhang, N., Lyle, E., Arab, P., Sawangphruk, M., & Dahn, J. R. (2021)
Journal of The Electrochemical Society, 168(7), 070503 https://doi.org/10.1149/1945-7111/ac0d69

 

Abstract


With research continuing to push for higher Ni content in positive electrode materials, issues such as the 1st cycle irreversible capacity and kinetic hindrances related to Li diffusion become more significant. This work highlights the impact of various material parameters on electrochemical performances, specifically the kinetic hindrances to Li diffusion in the low voltage region. Increasing the amount of substituents, increasing the secondary particle size and increasing the primary particle size were all variables found to decrease capacity in the ∼3.4–3.6 V region at modest discharge rates and increase the 1st cycle IRC. The capacity in the ∼3.4–3.6 V region can be recovered when cycling at a higher temperature at similar discharge rates or when cycling to a low cut-off voltage of 2 V. Since these processes are related to the diffusion of Li in the positive electrode, analysis of the Li chemical diffusion coefficient, Dc, is presented using a reinvented approach we call the “Atlung Method for Intercalant Diffusion.” The measured Dc for the single crystalline LiNi0.975Mg0.025O2 materials were found to be about 2 orders of magnitude smaller compared to the polycrystalline materials if the secondary particle size was used in the calculation of Dc for the polycrystalline samples. If the primary particle size of the polycrystalline materials was used, then Dc was similar to the single crystal materials. These results demonstrate that lattice diffusion is much slower compared to grain boundary diffusion offering insight for optimizing material morphology for better rate performance.

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