We report here the first electrochemical pathway to prepare Au-containing nanocomplexes with the mean diameter of 2 nm in 0.1 N KCl aqueous solutions without addition of any stabilizer. Encouragingly, polypyrrole-(PPy-) coated gold nanocomposites with a core-shell structure and a diameter smaller than 8 nm can be prepared by the formation of self-assembled monolayers and further orderly autopolymerization of pyrrole monomers on these nanocomplexes. The prepared Au/PPy nanocomposites should exhibit extremely high conductivity, as indicated by the analyses of X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and surface-enhanced Raman scattering (SERS). Meanwhile, elemental Au(0) nanoparticles of a mean diameter of 5 nm can be obtained by treating the Au/PPy-nanocomposites-containing solution with sonification.

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By combining techniques of underpotential deposition (UPD) and roughening metal substrates by a triangularwave oxidation-reduction cycle (ORC) which is generally used in surface-enhanced Raman scattering (SERS) studies, and extending applications of self-assembled monolayers (SAMs), copper is underpotentially deposited on electrochemically roughened Au(111) in this study for the first time. The formation of SAMs and further autopolymerization of pyrrole monomers are found on the UPD Cu-modified gold surfaces. The stability of SAMs is significantly improved due to the presence of UPD Cu. Furthermore, electropolymerized polypyrrole (PPy) on this UPD Cu-modified roughened Au demonstrates some distinguishing properties. These include higher conductivities, higher intensity, and better resolution on SERS and improved anti-aging ability in a 50% relative humility, 20% (v/v) O 2 atmosphere at 30 °C for 60 days.

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Three hole-transporting material (HTM)/electron-transporting material (ETM) combinations are chosen to generate blue, green and red exciplexes, allowing us to design precise device architectures for the fabrication of exciplex-based white OLEDs with daylight-like emissions.

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The problem of optimal asymmetric Hopfield-type associative memory (HAM) design based on perceptron-type learning algorithms is considered. It is found that most of the existing methods considered the design problem as either 1) finding optimal hyperplanes according to normal distance from the prototype vectors to the hyperplane surface or 2) obtaining weight matrix W = [w(ij)] by solving a constraint optimization problem. In this paper, we show that since the state space of the HAM consists of only bipolar patterns, i.e., V = (v1, v2, . . ., vN)T E {-1, +1}N, the basins of attraction around each prototype (training) vector should be expanded by using Hamming distance measure. For this reason, in this paper, the design problem is considered from a different point of view. Our idea is to systematically increase the size of the training set according to the desired basin of attraction around each prototype vector. We name this concept the higher order Hamming stability and show that conventional minimum-overlap algorithm can be modified to incorporate this concept. Experimental results show that the recall capability as well as the number of spurious memories are all improved by using the proposed method. Moreover, it is well known that setting all self-connections wiiVi to zero has the effect of reducing the number of spurious memories in state space. From the experimental results, we find that the basin width around each prototype vector can be enlarged by allowing nonzero diagonal elements on learning of the weight matrix W. If the magnitude of w(ii) is small for all i, then the condition w(ii) = OVi can be relaxed without seriously affecting the number of spurious memories in the state space. Therefore, the method proposed in this paper can be used to increase the basin width around each prototype vector with the cost of slightly increasing the number of spurious memories in the state space.