The synthesis and the electrochemical studies of 1,3-bis(2-cyano-2-R-thienylethenyl)benzene (1), 1,4-bis(2-cyano-2-R-thienylethenyl)benzene (2), and 2,7-bis(2-cyano-2-R-thienylethenyl)biphenylene (3) are reported herein. While compound 2 could be reversibly reduced to form the corresponding dianion, compounds 1 and 3 show irreversible or quasireversible reductions in their cyclic voltammetric studies. We tentatively attribute the high reactivity of the dianions of 1 and 3 to their diradicaloid behavior. This explanation is further supported by PM3/RHF-PM3/UHF calculations. Compounds 1-3 could be irreversibly oxidized at the potential more positive than +1.3 V. Among these compounds, 3 shows the highest reactivity toward oxidative electropolymerization. The resulting polymer film is relatively stable and electroactive. Although polymeric films of compounds 1 and 2 could be formed at higher monomer concentrations, the films are unstable toward electrochemical oxidation. UV-vis analyses of the polymeric films reveal that electropolymerization of 2 is quenched at the early stage of the polymerization, resulting in significant amounts of oligomers in the matrix. However, biphenylene containing monomer 3 could be smoothly converted to highly conjugated polymers under electrochemical oxidation.

[reaction: see text] Ureyleno crown ethers 2 and 3 bind with 2 equiv of Li(+) cooperatively and selectively over other alkali metal ions such as Na(+), K(+), and Cs(+). The binding constant for 3 was found to be 3.0 x 10(7) (L/mol)(2).

Tris(2-((pyrid-2-ylmethyl)uredio)ethyl)amine (2) and its perchlorate salt, 2.HClO(4), bind with Li+ in nitromethane in a 1:1 fashion. The stability constants of K(Li+) and K(H)(Li+) were found to be 112 +/- 25 and 130 +/- 30 M(-)(1) in CD(3)NO(2), respectively. Formation of the 1:1 complexes were further evidenced by electrospray ionization mass spectrometry (ESI-MS). The slight increase, or at least the same order of magnitude, of K(H)(Li+) compared to K(Li+) points to a remarkable preorganization of the protonated podand in 2.HClO(4), that essentially overcomes the increased Columbic repulsion occurring on complexation to Li+.

The objective of this paper is to establish an "Averaged Learning Subspace Method" (ALSM) applicable for classification of multi-spectral MR images. By using the ALSM to process the massive amounts of information in multi-spectral MR images, and classification tissues of brain. The classification result of each tissue has shown by binary image, respectively. The classification results would assist doctor to diagnose more efficiently and more accurately and thus to gain more time for necessary action. In order to further evaluate the performance of ALSM, the high order statistics is adopted assessment and compare with perceptron neural network.