Between scylla and charybdis: Hydrophobic graphene-guided water diffusion on hydrophilic substrates

The structure of water confined in nanometer-sized cavities is important because, at this scale, a large fraction of hydrogen bonds can be perturbed by interaction with the confining walls. Unusual fluidity properties can thus be expected in the narrow pores, leading to new phenomena like the enhanced fluidity reported in carbon nanotubes. Crystalline mica and amorphous silicon dioxide are hydrophilic substrates that strongly adsorb water. Graphene, on the other hand, interacts weakly with water. This presents the question as to what determines the structure and diffusivity of water when intercalated between hydrophilic substrates and hydrophobic graphene. Using atomic force microscopy, we have found that while the hydrophilic substrates determine the structure of water near its surface, graphene guides its diffusion, favouring growth of intercalated water domains along the C-C bond zigzag direction. Molecular dynamics and density functional calculations are provided to help understand the highly anisotropic water stripe patterns observed.

Cognitive Tomography Reveals Complex, Task-Independent Mental Representations

Humans develop rich mental representations that guide their behavior in a variety of everyday tasks. However, it is unknown whether these representations, often formalized as priors in Bayesian inference, are specific for each task or subserve multiple tasks. Current approaches cannot distinguish between these two possibilities because they cannot extract comparable representations across different tasks [1-10]. Here, we develop a novel method, termed cognitive tomography, that can extract complex, multidimensional priors across tasks. We apply this method to human judgments in two qualitatively different tasks, "familiarity" and "odd one out," involving an ecologically relevant set of stimuli, human faces. We show that priors over faces are structurally complex and vary dramatically across subjects, but are invariant across the tasks within each subject. The priors we extract from each task allow us to predict with high precision the behavior of subjects for novel stimuli both in the same task as well as in the other task. Our results provide the first evidence for a single high-dimensional structured representation of a naturalistic stimulus set that guides behavior in multiple tasks. Moreover, the representations estimated by cognitive tomography can provide independent, behavior-based regressors for elucidating the neural correlates of complex naturalistic priors. © 2013 The Authors.

Comparison of moment-curvature models for fiber-reinforced polymer plate-end debonding studies using global energy balance approach

This paper compares a number of different moment-curvature models for cracked concrete sections that contain both steel and external fiber-reinforced polymer (FRP) reinforcement. The question of whether to use a whole-section analysis or one that considers the FRP separately is discussed. Five existing and three new models are compared with test data for moment-curvature or load deflection behavior, and five models are compared with test results for plate-end debonding using a global energy balance approach (GEBA). A proposal is made for the use of one of the simplified models. The availability of a simplified model opens the way to the production of design aids so that the GEBA can be made available to practicing engineers through design guides and parametric studies. Copyright © 2014, American Concrete Institute.