Tailoring the structure, composition, optical properties and catalytic activity of Ag-Au nanoparticles by the galvanic replacement reaction

This Letter reports on the synthesis of Ag-Au nanoparticles (NPs) with controlled structures and compositions via a galvanic replacement reaction between Ag NPs and AuCl4(aq)- followed by the investigation of their optical and catalytic properties. Our results showed the formation of porous walls, hollow interiors and increased Au content in the Ag-Au NPs as the volume of AuCl4(aq)- employed in the reaction was increased. These variations led to a red shift and broadening of the SPR peaks and an increase of up to 10.9-folds in the catalytic activity towards the reduction of 4-nitrophenol relative to Ag NPs. (C) 2012 Elsevier B.V. All rights reserved.

Critical behavior in a cross-situational lexicon learning scenario

The associationist account for early word learning is based on the co-occurrence between referents and words. Here we introduce a noisy cross-situational learning scenario in which the referent of the uttered word is eliminated from the context with probability gamma, thus modeling the noise produced by out-of-context words. We examine the performance of a simple associative learning algorithm and find a critical value of the noise parameter gamma(c) above which learning is impossible. We use finite-size scaling to show that the sharpness of the transition persists across a region of order tau(-1/2) about gamma(c), where tau is the number of learning trials, as well as to obtain the learning error (scaling function) in the critical region. In addition, we show that the distribution of durations of periods when the learning error is zero is a power law with exponent -3/2 at the critical point. Copyright (C) EPLA, 2012