Diketonylpyridinium cations as a support of new ionic liquid crystals and ion-conductive materials: analysis of counter-ion effects

Ionic liquid crystals (ILCs) allow the combination of the high ionic conductivity of ionic liquids (ILs) with the supramolecular organization of liquid crystals (LCs). ILCs salts were obtained by the assembly of long-chained diketonylpyridinium cations of the type [HOO^(R(n)pyH)] + and BF_(4)^(-) , ReO_(4)^(-), NO_(3)^(-), CF_(3)SO_(3)^(-), CuCl_(4)^(2-) counter-ions. We have studied the thermal behavior of five series of compounds by differential scanning calorimetry (DSC) and hot stage polarized light optical microscopy (POM). All materials show thermotropic mesomorphism as well as crystalline polymorphism. X-ray diffraction of the [HOO^(R(12)pyH)][ReO_(4)] crystal reveals a layered structure with alternating polar and apolar sublayers. The mesophases also exhibit a lamellar arrangement detected by variable temperature powder X-ray diffraction. The CuCl_(4)^(2-) salts exhibit the best LC properties followed by the ReO_(4)^(-) ones due to low melting temperature and wide range of existence. The conductivity was probed for the mesophases in one species each from the ReO_(4)^(-) , and CuCl_(4)^(2-) families, and for the solid phase in one of the non-mesomorphic Cl^(-) salts. The highest ionic conductivity was found for the smectic mesophase of the ReO_(4)^(-) containing salt, whereas the solid phases of all salts were dominated by electronic contributions. The ionic conductivity may be favored by the mesophase lamellar structure.

The difference model with guessing explains interval bias in two-alternative forced- chocice detection procedures

The standard difference model of two-alternative forced-choice (2AFC) tasks implies that performance should be the same when the target is presented in the first or the second interval. Empirical data often show “interval bias” in that percentage correct differs significantly when the signal is presented in the first or the second interval. We present an extension of the standard difference model that accounts for interval bias by incorporating an indifference zone around the null value of the decision variable. Analytical predictions are derived which reveal how interval bias may occur when data generated by the guessing model are analyzed as prescribed by the standard difference model. Parameter estimation methods and goodness-of-fit testing approaches for the guessing model are also developed and presented. A simulation study is included whose results show that the parameters of the guessing model can be estimated accurately. Finally, the guessing model is tested empirically in a 2AFC detection procedure in which guesses were explicitly recorded. The results support the guessing model and indicate that interval bias is not observed when guesses are separated out.