Cation disorder dominates the defect chemistry of high-voltage LiMn1.5Ni0.5O4 (LMNO) spinel cathodes

High-voltage spinel LiMn1.5Ni0.5O4 (LMNO) can exist in a Mn/Ni ordered P4332 or disordered Fd[3 with combining macron]m arrangement with a majority of literature studies reporting improved electrochemical performance for the disordered phase. Through modifying synthesis conditions, the Mn/Ni ordering can be tuned, however oxygen and Mn3+ stoichiometries are also affected, making it difficult to decouple these responses and optimise performance. Here, we investigate all intrinsic defects in P4332 LMNO under various growth conditions, using density functional theory (DFT) calculations. We find that the majority of defects are deep and associated with small polarons (Mn3+, Mn2+ and Ni3+) formation. The tendency for cation disorder can be explained by the low formation energy of the antisite defects and their stoichiometric complexes. The intrinsic Fermi level of LMNO varies from moderately n-type under oxygen-poor conditions to weakly p-type under oxygen-rich conditions. Our work explains experimental observations (e.g. the Mn/Ni disorder) and provides guidelines for defect-controlled synthesis

Kinisi:Bayesian analysis of mass transport from molecular dynamics simulations

kinisi is a Python package for estimating transport coefficients—e.g., self-diffusion coefficients, ∗—and their corresponding uncertainties from molecular dynamics simulation data. It includes an implementation of the approximate Bayesian regression scheme described in McCluskey etal. (2023), wherein the mean-squared displacement (MSD) of mobile atoms is modelled as a multivariate normal distribution that is parametrised from the input simulation data. kinisi uses Markov-chain Monte Carlo (Foreman-Mackey et al., 2019; Goodman &amp; Weare, 2010) to sample this model multivariate normal distribution to give a posterior distribution of linear model ensemble MSDs that are compatible with the observed simulation data. For each linear ensemble MSD, x(), a corresponding estimate of the diffusion coefficient, ̂∗ is given via the Einstein relation, ̂∗ =1d x() / 6 d where is time. The posterior distribution of compatible model ensemble MSDs calculated by kinisi gives a point estimate for the most probable value of ∗ , given the observed simulation data, and an estimate of the corresponding uncertainty in ̂∗. kinisi also provides equivalent functionality for estimating collective transport coefficients, i.e., jump-diffusion coefficients and ionic conductivities<br/

Isgur-Wise Function for Heavy Light Mesons in D dimensional Potential Model

We report results of a potential model for mesons in D space-time dimension developed by considering the quark-antiquark potential of Nambu-Goto strings. With this wave function, we have studied Isgur-Wise function for heavy-light mesons and its derivatives like slope and curvature. The dimensional dependence of our results and a comparative study with the results of 3+1 dimensional QCD are also reported.Comment: 11 pages, 4 figure