Structural properties of Urfa stone doped with erbium oxide

In this study, we investigated the structural properties of Urfa stone (US) doped with erbium oxide (Er₂O₃). Solid US was powdered by using an agate mortar, and its elemental composition was determined using inductive coupling plasma (ICP) methods. Varying amounts of Er₂O₃ (5, 10, 20, 30, and 40%) were added as a dopant to the US powder using mechanical alloying methods. The resultant samples were sintered at 1000 °C for 1 h. The structural properties of the Er₂O₃-doped US samples were subsequently investigated using X-ray diffraction (XRD), Fourier-transform infrared spectrometry (FTIR), and photoluminescence methods. Results from the XRD analysis of the Er₂O₃-doped US powder indicated two crystalline phases: (1) calcium oxide (CaO) or lime and (2) Er₂O₃. After the samples were sintered at 1000 °C, CaO, Er₂O₃, calcium carbonate (CaCO₃), and mixed crystalline phases were observed. Results from the FTIR analysis of the Er₂O₃-doped US samples indicated absorption bands at 711.91, 872.08, and 1396.87 cm^?1 in the spectra. Finally, photoluminescence analysis results indicated a shift in the emission and excitation bands to longer and shorter wavelengths, respectively, in the solid state (non-aqueous media) US-Er complex.     

Investigation of the photoluminescence properties of Urfa stone powder doped with chromium oxide

In this study, the structural properties of Urfa stone (US) doped with chromium oxide (Cr₂O₃) were investigated using X-ray diffraction (XRD) and FT-IR spectroscopy. The photoluminescence properties of US doped with varying amounts of Cr₂O₃ (5, 10, 20, 30, and 40%) were also investigated. US powder was obtained via grinding with an agate mortar, and Cr₂O₃ was then added as a dopant to the US powder. The samples were sintered at 1000 °C for 1 h. The XRD results of the US powder doped with Cr₂O₃ via mechanical alloying showed the presence of some crystalline phases: calcite (CaCO₃) and eskolate (Cr₂O₃). Furthermore, it was found that calcium oxide (CaO) and tongbaite (Cr₃C₂) were also present in the sintered samples. The photoluminescence analysis results indicated that the emission and excitation bands of the US-Cr complex shifted to longer and shorter wavelengths in the solid state (non-aqueous media), respectively.     

Simulation and characterization of multi-class spatial patterns from stochastic point processes of randomness,clustering and regularity

Spatial pattern analysis of data from multiple classes (i.e., multi-class data) has important implications. We investigate the resulting patterns when classes are generated from various spatial point processes. Our null pattern is that the nearest neighbor probabilities being proportional to class frequencies in the multi-class setting. In the two-class case, the deviations are mainly in two opposite directions, namely, segregation and association of the classes. But for three or more classes, the classes might exhibit mixed patterns, in which one pair exhibiting segregation, while another pair exhibiting association or complete spatial randomness independence. To detect deviations from the null case, we employ tests based on nearest neighbor contingency tables (NNCTs), as NNCT methods can provide an omnibus test and post-hoc tests after a significant omnibus test in a multi-class setting. In particular, for analyzing these multi-class patterns (mixed or not), we use an omnibus overall test based on NNCTs. After the overall test, the pairwise interactions are analyzed by the post-hoc cell-specific tests based on NNCTs. We propose various parameterizations of the segregation and association alternatives, list some appealing properties of these patterns, and propose three processes for the two-class association pattern. We also consider various clustering and regularity patterns to determine which one(s) cause segregation from or association with a class from a homogeneous Poisson process and from other processes as well. We perform an extensive Monte Carlo simulation study to investigate the newly proposed association patterns and to understand which stochastic processes might result in segregation or association. The methodology is illustrated on two real life data sets from plant ecology.