Giant piezoresponse in nanoporous (Ba,Ca)(Ti,Zr)O3 thin film.

Lattice strain effects on the piezoelectric properties of crystalline ferroelectrics have been extensively studied for decades; however, the strain dependence of the piezoelectric properties at nano-level has yet to be investigated. Herein, a new overview of the super-strain of nanoporous polycrystalline ferroelectrics is reported for the first time using a nanoengineered barium calcium zirconium titanate composition (Ba0.85Ca0.15)(Ti0.9Zr0.1)O3 (BCZT). Atomic-level investigations show that the controlled pore wall thickness contributes to highly strained lattice structures that also retain the crystal size at the optimal value (<30 nm), which is the primary contributor to high piezoelectricity. The strain field derived from geometric phase analysis at the atomic level and aberration-corrected high-resolution scanning transmission electron microscopy (STEM) yields of over 30% clearly show theoretical agreement with high piezoelectric properties. The uniqueness of this work is the simplicity of the synthesis; moreover the piezoresponse d 33 becomes giant, at around 7500 pm V-1. This response is an order of magnitude greater than that of lead zirconate titanate (PZT), which is known to be the most successful ferroelectric over the past 50 years. This concept utilizing nanoporous BCZT will be highly useful for a promising high-density electrolyte-free dielectric capacitor and generator for energy harvesting in the future.

Prediction of nanocomposite properties and process optimization using persistent homology and machine learning.

Physical property prediction and synthesis process optimization are key targets in material informatics. In this study, we propose a machine learning approach that utilizes ridge regression to predict the oxygen permeability at fuel cell electrode surfaces and determine the optimal process temperature. These predictions are based on a persistence diagram derived from tomographic images captured using transmission electron microscopy (TEM). Through machine learning analysis of the complex structures present in the Pt/CeO2 nanocomposites, we discovered that l2 regularization considering diverse structural elements is more appropriate than l1 regularization (sparse modeling). Notably, our model successfully captured the activation energy of oxygen permeability, a phenomenon that could not be solely explained by the geometric feature of the Betti numbers, as demonstrated in a previous study. The correspondence between the ridge regression coefficient and persistence diagram revealed the formation process of the local and three-dimensional structures of CeO2 and their contributions to pre-exponential factor and activation energies. This analysis facilitated the determination of the annealing temperature required to achieve the optimal structure and accurately predict the physical properties.

Strain visualization using large-angle convergent-beam electron diffraction.

In this study, we report a strain visualization method using large-angle convergent-beam electron diffraction (LACBED).1 We compare the proposed method with the strain maps acquired via STEM-NBD, a combination of scanning transmission electron microscopy (STEM) and nanobeam electron diffraction (NBD). Although STEM-NBD can precisely measure the lattice parameters, it requires a large amount of data and personal computer (PC) resources to obtain a two-dimensional strain map. Deficiency lines in the transmitted disk of LACBED reflect the crystalline structure information and move, curve, or disappear in the deformed area. Properly setting the optical conditions makes it possible to acquire real-space images over a broad area in conjunction with deficiency lines on the transmitted disk. The proposed method acquires images by changing the relative position between the specimen and the deficiency line and can grasp the strain information with a small number of images. In addition, the proposed method does not require high-resolution images. It can reduce the required PC memory or storage consumption in comparison with that of STEM-NBD, which requires a high-resolution diffraction pattern (DP) from each point of the region of interest. Compared with the two-dimensional maps of LACBED and NBD, NBD could detect large distortions in the area where the deficiency line curved, moved, or disappeared. The curving or moving direction of the deficiency line is qualitatively consistent with the NBD results. If quantitative strain values are not essential, strain visualization using LACBED can be considered an effective technique. We believe that the strain information of a sample can be obtained effectively using both methods.

Unsupervised machine learning combined with 4D scanning transmission electron microscopy for bimodal nanostructural analysis.

Unsupervised machine learning techniques have been combined with scanning transmission electron microscopy (STEM) to enable comprehensive crystal structure analysis with nanometer spatial resolution. In this study, we investigated large-scale data obtained by four-dimensional (4D) STEM using dimensionality reduction techniques such as non-negative matrix factorization (NMF) and hierarchical clustering with various optimization methods. We developed software scripts incorporating knowledge of electron diffraction and STEM imaging for data preprocessing, NMF, and hierarchical clustering. Hierarchical clustering was performed using cross-correlation instead of conventional Euclidean distances, resulting in rotation-corrected diffractions and shift-corrected maps of major components. An experimental analysis was conducted on a high-pressure-annealed metallic glass, Zr-Cu-Al, revealing an amorphous matrix and crystalline precipitates with an average diameter of approximately 7 nm, which were challenging to detect using conventional STEM techniques. Combining 4D-STEM and optimized unsupervised machine learning enables comprehensive bimodal (i.e., spatial and reciprocal) analyses of material nanostructures.