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Influence of 3d Transition Metal Doping on Lithium Stabilized Na-β″-Alumina Solid Electrolytes.

ABSTRACT: Na-β″-alumina is the commercially most successful solid electrolyte due to its application in ZEBRA and NAS^® batteries. In this work, Li-stabilized Na-β″-alumina electrolytes were doped with 3d transition metal oxides, namely TiO₂, Mn₃O₄, and NiO, in order to improve their ionic conductivity and fracture strength. Due to XRD and EDX measurements, it was concluded that Mn- and Ni-ions are incorporated into the crystal lattice of Na-β″-alumina. In contrast, TiO₂ doping results in the formation of secondary phases that enable liquid-assisted sintering at temperatures as low as 1500 °C. All dopants increased the characteristic fracture strength of the electrolytes; 1.5 wt% of NiO doping proved to be most efficient and led to a maximal characteristic fracture strength of 296 MPa. Regarding the ionic conductivity, TiO₂ doping showed the uppermost value of up to 0.30 S cm^-1 at 300 °C. In contrast to the other dopants, TiO₂ doping lowered the sintering temperature needed to obtain a dense, stable, and highly conductive Na-β″-alumina electrolyte suitable for applications in Na based batteries.

SUBMITTER: Dirksen CL

PROVIDER: S-EPMC8466469 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12?000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining the propensity of dendrite initiation are usually obtained from computationally expensive first-principles methods. In order to obtain a large data set for screening, we use machine-learning models to predict the mechanical properties of several new solid electrolytes. The machine-learning models are trained on purely structural features of the material, which do not require any first-principles calculations. We train a graph convolutional neural network on the shear and bulk moduli because of the availability of a large training data set with low noise due to low uncertainty in their first-principles-calculated values. We use gradient boosting regressor and kernel ridge regression to train the elastic constants, where the choice of the model depends on the size of the training data and the noise that it can handle. The material stiffness is found to increase with an increase in mass density and ratio of Li and sublattice bond ionicity, and decrease with increase in volume per atom and sublattice electronegativity. Cross-validation/test performance suggests our models generalize well. We predict over 20 mechanically anisotropic interfaces between Li metal and four solid electrolytes which can be used to suppress dendrite growth. Our screened candidates are generally soft and highly anisotropic, and present opportunities for simultaneously obtaining dendrite suppression and high ionic conductivity in solid electrolytes.

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Influence of 3d Transition Metal Doping on Lithium Stabilized Na-β″-Alumina Solid Electrolytes.

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