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Oil was employed as an 'entrance door' for loading rubber with carbon-based fillers of different size and dimensionalities: 1D carbon nanotubes (CNTs), 2D graphene nanoplatelets (GNPs), and 3D graphite. This approach was explored, as a proof of concept, in the preparation of tire tread, where oil is commonly used to reduce the viscosity of the composite mixture. Rubber was loaded with carbon black (CB, always used) and one or more of the above fillers to enhance the thermal and mechanical properties of the composite. The CNT-loaded system showed the best enhancement in mechanical properties, followed by the CNT-GNP one. Rubber loaded with both graphite and GNP showed the best enhancement in thermal conductivity (58%). The overall enhancements in both mechanical and thermal properties of the various systems were analyzed through an overall relative efficiency index in which the total filler concentration in the system is also included. According to this index, the CNT-loaded system is the most efficient one. The oil as an 'entrance door' is an easy and effective novel approach for loading fillers that are in the nanoscale and provide high enhancement of properties at low filler concentrations.

McPherson's Blau space and affiliation ecology model is a powerful tool for analyzing the ecological competition among social entities, such as organizations, along a combination of sociodemographic characteristics of their members. In this paper we introduce the R-based Graphical User Interface (GUI) package Blaunet, an integrated set of tools to calculate, visualize, and analyze the statuses of individuals and social entities in Blau space, parameterized by multiple sociodemographic traits as dimensions. The package is able to calculate the Blau statuses at the nodal, dyadic, and meso levels based on three types of information: sociodemographic characteristics, group affiliations (e.g., membership in groups/organizations), and network ties. To facilitate this, Blaunet has the following five main capabilities, it can: 1) identify a list of possible salient dimensions; 2) calculate, plot, and analyze niches for social entities by measuring the social distance along the salient dimensions between individuals affiliated with them; 3) generate Blau bubbles for individuals, thereby allowing the study of interpersonal influence of similar others even with limited or no network information; 4) capture niche dynamics cross-sectionally by calculating the intensity of exploitation from the carrying capacity and the membership rate; and 5) analyze the niche movement longitudinally by estimating the predicted niche movement equations. We illustrate these capabilities of Blaunet with example datasets., Competing Interests: The authors have declared that no competing interests exist.

How does adolescent organizational membership in general, and simultaneous membership in distinct types of organizations in particular, impact drinking behavior? While past studies have focused either on the learning effect of involvement with gangs or on the constraining influence of conventional organizations on adolescent problem behavior, we explore the possibility that conventional school clubs can serve as socializing opportunities for existing gang members to engage in drinking behavior with non-gang club members. Using the Add Health data, we show that gang members drink more often, and engage in more binge drinking, than non-members. More importantly, individuals who are members of both gangs and school clubs drink alcohol at greater levels than those who are solely involved in gangs. In addition, non-gang adolescents who are co-members with gang members in the same school club are more likely to drink alcohol than non-members. This result has important implications for understanding the role of organizations in adolescent behavior and suggests that the study of delinquent behaviors would benefit from devoting more attention to individuals who bridge distinct types of organizations., (Copyright © 2016 Elsevier Inc. All rights reserved.)