Three new ferrocene （Fc） based receptors with pyridyl moiety, named methyl-6- ferrocenoylacetyl-2-pyridine carboxylate （FcLl）, 1,1＇-（2,6-bispyridyl）bis-3-ferrocenyl-l,3-propanedione （FcL2）, ferrocenecarboxald...Three new ferrocene （Fc） based receptors with pyridyl moiety, named methyl-6- ferrocenoylacetyl-2-pyridine carboxylate （FcLl）, 1,1＇-（2,6-bispyridyl）bis-3-ferrocenyl-l,3-propanedione （FcL2）, ferrocenecarboxaldehyde-2,6-dipicolinoyhydrazone （FcL3） were synthesized, and further characterized by elemental analysis, IR spectra, UV-Vis spectra, 1H and 13C NMR. The electrochemical properties and ion sensing properties of FcL1, FcL2 and FcL3 were also investigated by means of cyclic voltammetry in ethanol solution with 0.1 mol/L LiC104 as the supporting electrolyte. The E~ values of the receptors increase with the scanning rate increasing at high scanning rate, and Ipa/Ipo approaches unity, indicating that the redox reaction is basically reversible. Their recognition performances to different metal cations such as Cd（II）, Co（II）, Cu（II）, Hg（II）, Mn（II）, Ni（II）, Zn（II） show that the FcL1 is responsive to Cu（II） with the maximum electrochemical shift of the FcL1 for Cu（II）of about 72.0 mV, whereas the FcL2 is responsive to Cu（II） and Mn（II） with shift of 102 mV and 109 mV, respectively, and the FcL3 is responsive to Hg（II） and Mn（II） with the shift of 53.0 mV and 54.0 mV, respectively. All the results show that these receptors may have potential applications in electrochemical sensor technology, material science, and molecular devices.展开更多
Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented ...Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.展开更多
基金Project(21071152)supported by the National Natural Science Foundation of China
文摘Three new ferrocene （Fc） based receptors with pyridyl moiety, named methyl-6- ferrocenoylacetyl-2-pyridine carboxylate （FcLl）, 1,1＇-（2,6-bispyridyl）bis-3-ferrocenyl-l,3-propanedione （FcL2）, ferrocenecarboxaldehyde-2,6-dipicolinoyhydrazone （FcL3） were synthesized, and further characterized by elemental analysis, IR spectra, UV-Vis spectra, 1H and 13C NMR. The electrochemical properties and ion sensing properties of FcL1, FcL2 and FcL3 were also investigated by means of cyclic voltammetry in ethanol solution with 0.1 mol/L LiC104 as the supporting electrolyte. The E~ values of the receptors increase with the scanning rate increasing at high scanning rate, and Ipa/Ipo approaches unity, indicating that the redox reaction is basically reversible. Their recognition performances to different metal cations such as Cd（II）, Co（II）, Cu（II）, Hg（II）, Mn（II）, Ni（II）, Zn（II） show that the FcL1 is responsive to Cu（II） with the maximum electrochemical shift of the FcL1 for Cu（II）of about 72.0 mV, whereas the FcL2 is responsive to Cu（II） and Mn（II） with shift of 102 mV and 109 mV, respectively, and the FcL3 is responsive to Hg（II） and Mn（II） with the shift of 53.0 mV and 54.0 mV, respectively. All the results show that these receptors may have potential applications in electrochemical sensor technology, material science, and molecular devices.
基金Supported by the National Natural Science Foundation of China (No. 30570485)the Shanghai "Chen Guang" Project (No. 09CG69).
文摘Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.
