ABSTRACT
A glass with the mol% composition 17 Y2O3·33 Al2O3·40 SiO2·2 AlF3·3 Na2O·2 CeF3·3 B2O3 is heat treated at 1000 °C for 6-24 h. This results in the surface nucleation and growth of YAG. Nucleation and growth of star-shaped alumina and later of monoclinic ß-Y2Si2O7 and orthorhombic δ-Y2Si2O7 are additionally observed in the bulk. Phase identification and localization are performed by electron backscatter diffraction (EBSD) as well as TEM analysis. The monoclinic ß-Y2Si2O7 observed in the bulk occurs in the form of large, crystal agglomerates which range from 50 to 120 µm in size. The individual crystals are aligned along the c-axis which is the fastest growing axis. Ten probability maxima are observed in the pole-figures illustrating the rotation of orientations around the c-axes indicating a fivefold symmetry. This symmetry is caused by multiple twinning which results in a high probability of specific orientation relationships with rotation angles of ~36°, ~108° (also referred to as the pentagon angle) and ~144° around the c-axis. All these rotation angles are close to the multiples of 36° which are required for an ideal fivefold symmetry. This is the first report of a fivefold symmetry triggered by the presence of barriers hindering crystal growth.
ABSTRACT
A glass with the mol % composition 23.82 Y2O3·39.82 Al2O3·28.50 SiO2·2.91AlF3·4.95 ZrO2 doped with 2 mol % CeF3 is crystallized at 1250 °C for 20 h. The crystallized samples are studied using X-ray diffraction (XRD), the SEM-based methods EBSD and EDX, as well as fluorescence microscopy. Six crystalline phases are detected in the residual glass including alumina, YAG, Y-stabilized zirconia (YSZ), and three different yttrium silicates of the composition Y2Si2O7. Chemistry-assisted indexing (ChI) is successfully applied to separate YAG and YSZ in EBSD-scans. YAG displays polygon as well as dendritic growth. Some crystals show both mechanisms at opposite ends, indicating that the growth mechanism is influenced by the chemistry of the surrounding glass matrix.
ABSTRACT
BACKGROUND: The complexity of reactions and kinetic is the current problem of photodegradation processes. Recently, artificial neural networks have been widely used to solve the problems because of their reliable, robust, and salient characteristics in capturing the non-linear relationships between variables in complex systems. In this study, an artificial neural network was applied for modeling p-cresol photodegradation. To optimize the network, the independent variables including irradiation time, pH, photocatalyst amount and concentration of p-cresol were used as the input parameters, while the photodegradation% was selected as output. The photodegradation% was obtained from the performance of the experimental design of the variables under UV irradiation. The network was trained by Quick propagation (QP) and the other three algorithms as a model. To determine the number of hidden layer nodes in the model, the root mean squared error of testing set was minimized. After minimizing the error, the topologies of the algorithms were compared by coefficient of determination and absolute average deviation. RESULTS: The comparison indicated that the Quick propagation algorithm had minimum root mean squared error, 1.3995, absolute average deviation, 3.0478, and maximum coefficient of determination, 0.9752, for the testing data set. The validation test results of the artificial neural network based on QP indicated that the root mean squared error was 4.11, absolute average deviation was 8.071 and the maximum coefficient of determination was 0.97. CONCLUSION: Artificial neural network based on Quick propagation algorithm with topology 4-10-1 gave the best performance in this study.
