Synthesis and Luminescence Properties of Eu2+-Doped Sr3MgSi2O8 Blue Light-Emitting Phosphor for Application in Near-Ultraviolet Excitable White Light-Emitting Diodes.

In this study, [Sr0.99Eu0.01]3MgSi2O8 phosphors were sintered at 1200-1400 °C for 1-5 h by using the solid-state reaction method. The crystallinity and morphology of these phosphors were characterized through X-ray diffraction analysis and field-emission scanning electron microscopy, respectively, to determine their luminescence. The photoluminescence properties, including the excitation and emission properties, of the prepared phosphors were investigated through fluorescence spectrophotometry. The α-Sr2SiO4, Sr2MgSi2O7, and Sr3MgSi2O8 phases coexisted in the [Sr0.99Eu0.01]3MgSi2O8 phosphors, which were synthesized at low temperatures. The particles of these phosphors had many fine hairs on their surface and resembled Clavularia viridis, which is a coral species. Transmission electron microscopy and energy dispersive X-ray spectroscopy indicated that the fine hairs contained the Sr2SiO4 and Sr2MgSi2O7 phases. However, when the [Sr0.99Eu0.01]3MgSi2O8 phosphors were sintered at 1400 °C, the Sr3MgSi2O8 phase was observed, and the Eu2+-doped Sr3MgSi2O8 phase dominated the only broad emission band, which had a central wavelength of 457 nm (blue light). The emission peaks at this wavelength were attributed to the 4f65d1-4f7 transition at the Sr2+(I) site, where Sr2+ was substituted by Eu2+. The average decay time of the synthesized phosphors was calculated to be 1.197 ms. The aforementioned results indicate that [Sr0.99Eu0.01]3MgSi2O8 can be used as a blue-emitting phosphor in ultraviolet-excited white light-emitting diodes.

Investigations of a Statistical and Analytical Method to Find the Relationship between the Morphological and Optical Properties of ZnO Nanoflower Arrays.

In this study, a sapphire substrate with a patterned concave structure was used to prepare ZnO film/A-B glue, and the ZnO film/A-B glue with a patterned convex matrix was transferred onto a silicon wafer using the lift-off technology as the seed layer. Then, the hydrothermal method with different Zn(CH3COO)2 and C6H12N4 concentrations as precursors was used to synthesize ZnO nanoflower arrays on the patterned convex ZnO seed layer. XRD pattern, FESEM, FIB, and photoluminescence (PL) spectrometry were employed to observe and analyze the properties of the synthesized ZnO nanoflower arrays. When Zn(CH3COO)2 and C6H12N4 concentrations were 0.01, 0.02, 0.03, and 0.04 M, the average heights of the ZnO nanorods in the ZnO nanoflower arrays were 993, 1500, 1550, and 1650 nm, the average diameters of the ZnO nanorods were 50, 90, 105, and 225 nm, and the aspect ratios (H/D) of the ZnO nanorods were 19.9, 16.7, 14.8, and 7.33, respectively. A simple statistical and analytical method was investigated to estimate the densities (number of nanorods) of the ZnO nanoflower arrays in one 1 µm × 1 µm area. The total surface area (S) of the ZnO nanoflower arrays first increased from 5.05 × 106 and then reached a maximum value of 1.20 × 107 nm2 as Zn(CH3COO)2 and C6H12N4 concentrations increased from 0.01 to 0.02 M. For the systhesized ZnO nanoflower arrays, as the Zn(CH3COO)2 and C6H12N4 concentrations increased from 0.01 to 0.04 M, their total volume (V) increased from the 6.23 × 107 to 5.90 × 108 nm3 and the S/V ratio decreased from 8.10 × 10-2 to 1.84 × 10-2. We found that ZnO nanoflower arrays with Zn(CH3COO)2 and C6H12N4 concentrations of 0.2 M presented the maximum PL emission intensities. The calculated S/V ratios and X-ray photoelectron spectroscopy analyses are used to discuss the reasons for these results.

Study on the university students' satisfaction of the wisdom tree massive open online course platform based on parameter optimization intelligent algorithm.

INTRODUCTION: Curriculum learning through the wisdom tree massive open online course platform not only gets rid of the limitations of specialty, school and region, eliminates the limitations of time and space in traditional teaching, but also effectively solves the problem of educational equity. OBJECTIVES: This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. METHODS: This study takes the university students in Fuzhou city information management department as the survey object, and adopts the electronic questionnaire survey method. A total of 1136 formal questionnaires were responded, and 1028 valid questionnaires were obtained after data cleaning and deleting invalid questionnaires (the effective rate was 90.49%). In this paper, the reliability and validity of the questionnaire were tested by IBM SPSS-20.0 software, and six explanatory variables including function, achievement, exercise, quality, richness, and interaction were obtained by principal component analysis. Then, the questionnaire data is converted to CSV (comma separated values) format for analysis. This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. In this paper, the proposed algorithm is compared with decision tree, random forest, k-nearest neighbor, and support vector machine to verify its performance. RESULTS: The experimental results show that training set classification accuracy of decision tree, random forest, k-nearest neighbor, only support vector machine and the proposed algorithm (simulated annealing + support vector machine) are 92.21%, 96.10%, 95.67%, 97.29%, and 99.58%, respectively. CONCLUSION: The proposed algorithm simulated annealing + support vector machine does increase the classification accuracy. At the same time, the 11 decision rules generated by simulated annealing + decision tree can provide useful information for decision makers.

Efficiently solving general weapon-target assignment problem by genetic algorithms with greedy eugenics.

A general weapon-target assignment (WTA) problem is to find a proper assignment of weapons to targets with the objective of minimizing the expected damage of own-force asset. Genetic algorithms (GAs) are widely used for solving complicated optimization problems, such as WTA problems. In this paper, a novel GA with greedy eugenics is proposed. Eugenics is a process of improving the quality of offspring. The proposed algorithm is to enhance the performance of GAs by introducing a greedy reformation scheme so as to have locally optimal offspring. This algorithm is successfully applied to general WTA problems. From our simulations for those tested problems, the proposed algorithm has the best performance when compared to other existing search algorithms.