Papers by Author: Yi Min Dai

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Authors: Yun Lin Dai, Zhong Cao, Yi Min Dai, Ju Lan Zeng, Wei Gang Huang, Jing Ling Hu, De Liang He, Katsuyuki Aoki
Abstract: Coating with a calixarene derivative on gold surface of AT-cut quartz crystal, a piezoelectric quartz crystal (PQC) sensing device was successfully fabricated in this paper. Among four calixarene materials, the compound of MRCT was the most efficient actively coating material for recognizing ethanol molecule based on a host-guest recognition mechanism with C—H•••π interaction. In comparison with gas-chromatography (GC) method, the calixarene based PQC device can be well used for on-line detection of the ethanol vapor in the range of 0 ~ 3000 ppm around our environment with a recovery of 92.33~105.76 %. The detection limit can be evaluated to be 3.53 ppm. Furthermore, the proposed TSM sensor possessed good selectivity, reproducibility, reversibility and high stability for practical purpose.
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Authors: Xi Xi Huang, Zhong Cao, Yong Le Liu, Yi Min Dai, Ju Lan Zeng, Rong Hua Yang, Hiroyuki Takei
Abstract: An novel optical nano biosensor based on gold capped nano-particles for detecting binding events between ligands and receptor molecules as well as interactions among proteins without use of labels has been presented in this paper. The optical properties of nano-sized gold particles exhibiting pronounced adsorption in the visible region which called as localized surface plasmon resonance (LSPR) have been exploited, whose peak wavelengths depended exquisitely on the refractive index of the surrounding. In comparison with surface plasmon resonance (SPR) technology, the optical nano biosensor possessed high sensitivity, surprisingly low “bulk effect”, ease of preparation, and low-cost polymer based fabrication, which opened a promising bioanalytical application in practice.
1554
Authors: Zhong Cao, Ju Lan Zeng, Yi Min Dai, Xun Li, Dong Mei Luo
Abstract: The frequency domain power spectra of acoustic emission (AE) signals from different metal-acid reaction processes such as 6111 Al-alloy-hydrochloric acid (HCl) and 7070 Al-alloy-HCl for evolving hydrogen gases were obtained by fast Fourier transform (FFT) program and used for chemical analysis of different metal materials. Averaged power spectra from these processes and their corresponding characteristics were extracted. The characteristic AE frequency signals could be used for chemical pattern recognition of different metal materials like 6111 and 7050 aluminum alloys from the metal-acid reaction processes, that the principal component analysis (PCA) with appropriate frequency selection procedure gave a satisfactory classification with a correct rate of 78.1%. The back-propagation (BP) algorithm of artificial neural network (ANN) could give better recognition of AE signals for 6111 and 7050 alloys with a correct rate of 100%. Moreover, the AE energetic parameters are linearly correlated with the pH value of the acidic buffer solution, which opens a new possibility for quantitatively analytical application of AE signals on metal materials.
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