Structure and properties of (1-x)Pb(Mg1/2W1/2)O3–xPb(Zr0.5Ti0.5)O3 solid solution ceramics

The widely used piezoelectric Pb(Zr1−x Ti x )O3 ceramics have been known to have Zr4+ and Ti4+ randomly distributed on the B-site lattice in the ABO3 perovskite structure. In this study, we attempted to develop long range 1:1 B-site cation order by forming the solid solution of (1 − x)Pb(Mg1/2W1/2)O3 − xPb(Zr0.5Ti0.5)O3 (x ≥ 0.60). High temperature X-ray diffraction tests indicate that the cation order is embedded in the structural order. The solid solution ceramics appear to have a non-cubic paraelectric phase above their Curie temperatures. The competition between the antiferroelectric order in Pb(Mg1/2W1/2)O3 and the ferroelectric order in Pb(Zr0.5Ti0.5)O3 leads to the relaxor ferroelectric behavior in the solid solution. Since the temperature at dielectric maximum, T m, is significantly above room temperature, regular polarization versus electric field hysteresis loops are recorded in these compositions at room temperature. In addition, these ceramics show very good piezoelectric properties

Influence of long-range cation order on relaxor properties of doped Pb(Mg1/3Nb2/3)O3 ceramics

The 1:1 B-site cation order in Pb(Mg1/3Nb2/3)O3 relaxor ferroelectric ceramics was significantly enhanced by doping of minor amounts of La3+, Sc3+, or W6+ (less than 3 at. %) combined with a slow cooling procedure. Transmission electron microscopy examination confirmed the size increase of the cation-ordered regions embedded in a disordered matrix in the samples that were slowly cooled after sintering. The average cation ordering parameter (S) determined from x-ray diffraction data in these partially ordered samples was about 0.3–0.4. The ferroelectric properties and dielectric relaxation were compared in partially ordered and disordered (S=0) samples with the same composition. It was found that typical relaxor behavior was preserved in partially ordered ceramics. Furthermore, the temperature and diffuseness of the characteristic relaxor permittivity peak and the parameters of dielectric relaxation (in particular, the distribution of relaxation times and the Vogel-Fulcher freezing temperature) were practically independent of S. In contrast, the diffuseness of the phase transition from the ferroelectric phase (induced by external electric field) to the ergodic relaxor phase appeared to be much larger in the disordered samples than in the partially ordered ones (this diffuseness was assessed using pyroelectric current and ferroelectric hysteresis loops). These results suggest that cation ordering did not influence the behavior of polar nanoregions which are responsible for the dielectric response in the ergodic relaxor phase but significantly influenced the ferroelectric phase transition. The results are interpreted in terms of different types of polar regions in the disordered matrix and cation-ordered domains

Rare case of magnetic Ag3+^{3+} ion: double perovskite Cs2_{2}KAgF6_{6}

Normally $4d$ or $5d$ transition metals are in a low-spin state. Here using first-principles calculations, we report on a rare case of a high-spin $S$=1 magnetic state for the Ag$^{3+}$ ion in the double perovskite Cs$_{2}$KAgF$_{6}$. We also explored a possibility of a conventional low-spin $S$=0 ground state and find an associated tetragonal distortion to be 0.29 {\AA}. However, the lattice elastic energy cost and the Hund exchange loss exceed the e$_{g}$ crystal-field energy gain, thus making the low-spin tetragonal structure less favorable than the high-spin cubic structure. We conclude that the compact perovskite structure of Cs$_{2}$KAgF$_{6}$ is an important factor in stabilizing the unusual high-spin ground state of Ag$^{3+}$.Comment: 6 pages, 6 figures, accepted for publication in PR

Modeling Human Visual Search Performance on Realistic Webpages Using Analytical and Deep Learning Methods

Modeling visual search not only offers an opportunity to predict the usability of an interface before actually testing it on real users, but also advances scientific understanding about human behavior. In this work, we first conduct a set of analyses on a large-scale dataset of visual search tasks on realistic webpages. We then present a deep neural network that learns to predict the scannability of webpage content, i.e., how easy it is for a user to find a specific target. Our model leverages both heuristic-based features such as target size and unstructured features such as raw image pixels. This approach allows us to model complex interactions that might be involved in a realistic visual search task, which can not be easily achieved by traditional analytical models. We analyze the model behavior to offer our insights into how the salience map learned by the model aligns with human intuition and how the learned semantic representation of each target type relates to its visual search performance.Comment: the 2020 CHI Conference on Human Factors in Computing System