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Eu3+-site occupation in CaTiO3 perovskite material at low temperature
Fengfeng Chi,Yanguang Qin,Shaoshuai Zhou,Xiantao Wei,Yonghu Chen,Changkui Duan,Min Yin 한국물리학회 2017 Current Applied Physics Vol.17 No.1
In order to clarify the site occupancy of rare-earth ions in rare-earth doped perovskite materials, the undoped pure CaTiO3 and Eu3þ-doped CaTiO3 samples with a series of Ca/Ti ratio were synthesized via high-temperature solid-state reaction method. X-ray diffraction (XRD) powder patterns confirm that the crystal structure keeps invariant at various Ca/Ti ratios. Measurement results of unit-cell parameters and X-ray photoelectron spectroscopy (XPS) indicate that Eu3þ ions enter into the Ca2þ site. The highresolution photoluminescence spectra of Eu3þ ions at 20 K in all samples did not witness a significant change under the excitation at different wavelength, implying that Eu3þ ions occupy only one type of site. Considering the small spectral splitting range of 5D0/7F2 transition and the large intensity ratio of 5D0 / 7F2/5D0 / 7F1, it can be concluded that Eu3þ occupies Ca2þ site with larger coordinate numbers rather than Ti4þ site.
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Fu Xingyu,Zhou Fengfeng,Peddireddy Dheeraj,Kang Zhengyang,Jun Martin Byung-Guk,Aggarwal Vaneet 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.3
In this work, we present a boundary oriented graph embedding (BOGE) approach for the graph neural network to assist in rapid design and digital prototyping. The cantilever beam problem has been solved as an example to validate its potential of providing physical field results and optimized designs using only 10 ms. Providing shortcuts for both boundary elements and local neighbor elements, the BOGE approach can embed unstructured mesh elements into the graph and performs an efficient regression on large-scale triangular-mesh-based finite element analysis (FEA) results, which cannot be realized by other machine-learning-based surrogate methods. It has the potential to serve as a surrogate model for other boundary value problems. Focusing on the cantilever beam problem, the BOGE approach with 3-layer DeepGCN model achieves the regression with mean square error (MSE) of 0.011 706 (2.41% mean absolute percentage error) for stress field prediction and 0.002 735 MSE (with 1.58% elements having error larger than 0.01) for topological optimization. The overall concept of the BOGE approach paves the way for a general and efficient deep-learning-based FEA simulator that will benefit both industry and Computer Aided Design (CAD) design-related areas.
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