Two new copper-based borates with supramolecular cages were synthesized under hydrothermal condition. As new copper complexes, they exhibit photocatalytic activity. The experimental results show borates have potential applications as water reduction catalysts (WRCs).
Two hybrid inorganic–organic CuII-sandwiched POM were synthesized and they exhibit photocatalytic activity. This would guide us to prepared copper-substituted polyoxotungstate and apply them toward renew energy.
Two new Ni12‐substituted sandwiched phosphotungstates organic‐cluster, H2[Ni6(OH)3(H2O)9(PW9O34)(1,4‐NDC)1/2(H2O)1/2]2 ⋅ 14 H2O (1), Ni(H2O)2(en)2[Ni6(OH)3(H2O)6(en)2(PW9O34)(2,6‐NDC)1/2]2 ⋅ 11H2O (2) (en=1,2‐ethylenediamine; 1,4‐NDC=Naphthalene‐1,4‐dicarboxylic acid; 2,6‐NDC=Naphthalene‐2,6‐dicarboxylic acid) which are composed of {Ni6PW9} as second building units (SBU) and organic molecular as the bridging molecule have been synthesized by a hydrothermal method under mild condition. From single‐crystal X‐ray crystallography, we could observe two different configurations between 1 and 2. Two {Ni6PW9} SBUs were connected through 1,4‐H2NDC molecular and 2,6‐H2NDC molecular, respectively. Thought the connection of organic molecular, we successfully increase the nuclear number of transition‐metal‐substituted polyoxometalates. Meanwhile, both of two compounds have ferromagnetic interactions, which guides us to synthesize novel phosphotungstates with ferromagnetic property.
This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signal, impulsive transient signal and frequency variation signal. In this article, the original signals are decomposed into different frequency subbands by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train RBF neural network. The result shows that the RBF neural network is effective in the detection and diagnosis of various urban rail vehicle auxiliary inverter faults.
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