Atomistic Origin of Li-Ion Conductivity Reduction at (Li3xLa2/3-x)TiO3 Grain Boundary.

Lithium lanthanum titanate (LLTO) is one of the excellent candidates for an electrolyte in the all-solid-state Li-ion battery, owing to the high Li-ion conductivity in the bulk. However, the Li-ion conductivity at the grain boundary (GB) is largely reduced, and it is therefore important to reveal the origin of Li-ion conductivity reduction at the GB. Here, by using atomic-resolution scanning transmission electron microscopy combined with atomic force microscopy, we investigate the charge states, Li-ion conductivities, atomic and electronic structures at the LLTO Σ5 and Σ13 GBs. Although the Σ5 GB has no significant influence on Li-ion conductivity, the Σ13 GB shows the evident reduction of Li-ion conductivity. We further elucidate that the Σ13 GB is positively charged by the formation of oxygen vacancies at the GB. Such a positive charge would form the Li-ion depletion layers adjacent to the GB, which causes the significant reduction of Li-ion conductivity.

Total Third-Degree Variation for Noise Reduction in Atomic-Resolution STEM Images.

Scanning Transmission Electron Microscopy (STEM) enables direct determination of atomic arrangements in materials and devices. However, materials such as battery components are weak for electron beam irradiation and low electron doses are required to prevent beam-induced damages. Noise removal is thus essential for precise structural analysis of electron beam sensitive materials at atomic resolution. Total square variation (TSV) regularization is an algorithm that exhibits high noise removal performance. However, the use of the TSV regularization term leads to significant image blurring and intensity reduction. To address these problems, we here propose a new approach adopting L2 norm regularization based on higher-order total variation. An atomic-resolution STEM image can be approximated as a set of smooth curves represented by quadratic functions. Since the third-degree derivative of any quadratic function is 0, total third-degree variation (TTDV) is suitable for a regularization term. The application of TTDV for denoising the atomic-resolution STEM image of CaF2 observed along the [001] zone axis is shown, where we can clearly see the Ca and F atomic columns without compromising image quality.

Atomic-resolution STEM image denoising by total variation regularization.

Atomic-resolution electron microscopy imaging of solid-state material is a powerful method for structural analysis. Scanning transmission electron microscopy (STEM) is one of the actively used techniques to directly observe atoms in materials. However, some materials are easily damaged by the electron beam irradiation, and only noisy images are available when we decrease the electron dose to avoid beam damages. Therefore, a denoising process is necessary for precise structural analysis in low-dose STEM. In this study, we propose total variation (TV) denoising algorithm to remove quantum noise in an STEM image. We defined an entropy of STEM image that corresponds to the image contrast to determine a hyperparameter and we found that there is a hyperparameter that maximizes the entropy. We acquired atomic-resolution STEM image of CaF2 viewed along the [001] direction and executed TV denoising. The atomic columns of Ca and F are clearly visualized by the TV denoising, and atomic positions of Ca and F are determined with the error of ±1 pm and ±4 pm, respectively.