The high pressure behaviors of hydrate Cu-BTC metal-organic framework (MOF) in terms of phase stability, compressibility and reversibility were investigated in situ by synchrotron X-ray powder diffraction as well as vibrational spectroscopy. Two phase transitions, caused by the interaction of water and the sample framework, were revealed by the vibrational spectroscopies. Compressibility of the hydrate Cu-BTC also displays soft and hard regimes, which is the same scenario as non-hydrate Cu-BTC with a pressure transmitting medium. It is further confirmed that the residual water molecules in hydrate Cu-BTC can serve as a pressure transmitting medium with small molecule size under high pressure. Our results not only prove the high stability of Cu-BTC but also provide spectroscopic evidence for the interactions taking place between the guest molecules and the sample framework. Such findings could provide further guidelines for improving Cu-BTC's absorption and storage abilities.

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One chemical reagent, hydrazine hydrate, was discovered to accelerate the growth of semiconductor nanocrystals (cadmium telluride) instead of additional energy, which was applied to the synthesis of high-quality CdTe nanocrystals at room temperature and ambient conditions within several hours. Under this mild condition the mercapto stabilizers were not destroyed, and they guaranteed CdTe nanocrystal particle sizes with narrow and uniform distribution over the largest possible range. The CdTe nanocrystals (photoluminescence emission range of 530-660 nm) synthesized in this way had very good spectral properties; for instance, they showed high photoluminescence quantum yield of up to 60%. Furthermore, we have succeeded in detecting the living Borrelia burgdorferi of Lyme disease by its photoluminescence image using CdTe nanocrystals.

In this paper, a novel multi-physics parametric modeling approach using artificial neural networks (ANNs) for microwave passive components is proposed. In the proposed approach, the ANN is used to learn the nonlinear relationships between electromagnetic (EM) behaviors and multiphysics design variables. The trained model can accurately represent the EM responses of the passive components with respect to the multi-physics input parameters. Therefore, the proposed model can provide accurate and fast prediction of EM responses using low computational cost and little time for multi-physics design. The advantage of the proposed model is demonstrated by two microwave examples: the proposed model can save about 98% computational cost compared with the EM model, and the CPU time of the proposed model is less than 0.1 s while that of the EM model needs many minutes.

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