RESUMO
Atom probe tomography (APT) is commonly used to study solute clustering and precipitation in materials. However, standard techniques used to identify and characterize clusters within atom probe data, such as the density-based spatial clustering applications with noise (DBSCAN), often underperform with respect to small clusters. This is a limitation of density-based cluster identification algorithms, due to their dependence on the parameter Nmin, an arbitrary lower limit placed on detectable cluster sizes. Therefore, this article attempts to consider the characterization of clustering in atom probe data as an outlier detection problem of which k-nearest neighbors local outlier factor and learnable unified neighborhood-based anomaly ranking algorithms were tested against a simulated dataset and compared to the standard method. The decision score output of the algorithms was then auto thresholded by the Karcher mean to remove human bias. Each of the major models tested outperforms DBSCAN for cluster sizes of <25 atoms but underperforms for sizes >30 atoms using simulated data. However, the new combined k-nearest neighbors (k-NN) and DBSCAN method presented was able to perform well at all cluster sizes. The combined k-NN and seven methods are presented as a new approach to identifying clusters in APT.
RESUMO
The functional properties of the high-temperature superconductor Y1Ba2Cu3O7-δ (Y-123) are closely correlated to the exact stoichiometry and oxygen content. Exceeding the critical value of 1 oxygen vacancy for every five unit cells (δ>0.2, which translates to a 1.5 at% deviation from the nominal oxygen stoichiometry of Y7.7Ba15.3Cu23O54-δ ) is sufficient to alter the superconducting properties. Stoichiometry at the nanometer scale, particularly of oxygen and other lighter elements, is extremely difficult to quantify in complex functional ceramics by most currently available analytical techniques. The present study is an analysis and optimization of the experimental conditions required to quantify the local nanoscale stoichiometry of single crystal yttrium barium copper oxide (YBCO) samples in three dimensions by atom probe tomography (APT). APT analysis required systematic exploration of a wide range of data acquisition and processing conditions to calibrate the measurements. Laser pulse energy, ion identification, and the choice of range widths were all found to influence composition measurements. The final composition obtained from melt-grown crystals with optimized superconducting properties was Y7.9Ba10.4Cu24.4O57.2.
RESUMO
The vacancy ordering behavior of an A-site deficient perovskite system, Ca1-xLa2x/3TiO3, was studied using atomic resolution scanning transmission electron microscopy (STEM) in conjunction with electron energy-loss spectroscopy (EELS), with the aim of determining the role of A-site composition changes. At low La content (x = 0.2), adopting Pbnm symmetry, there was no indication of long-range ordering. Domains, with clear boundaries, were observed in bright-field (BF) imaging, but were not immediately visible in the corresponding high-angle annular dark-field (HAADF) image. These boundaries, with the aid of displacement maps from A-site cations in the HAADF signal, are shown to be tilt boundaries. At the La-rich end of the composition (x = 0.9), adopting Cmmm symmetry, long-range ordering of vacancies and La3+ ions was observed, with alternating La-rich and La-poor layers on (001)p planes, creating a double perovskite lattice along the c axis. These highly ordered domains can be found isolated within a random distribution of vacancies/La3+, or within a large population, encompassing a large volume. In regions with a high number density of double perovskite domains, these highly ordered domains were separated by twin boundaries, with 90° or 180° lattice rotations across boundaries. The occurrence and characteristics of these ordered structures are discussed and compared with similar perovskite systems.