While much work has been reported on the statistical mechanics and molecular simulation of interfaces of planar and spherical geometries, very little has been published on the interfaces of cylindrical geometry. The cylindrical geometry is important for the study of cylindrical micelles and particularly for nano-phases confined within cylindrical pores since the most well-defined porous materials (e.g., carbon and silicon nanotubes, SBA-15 and KIT-6 silicas) that are presently available are of this geometry. In this work, we derive the statistical mechanical equations for the pressure tensor for an interfacial region of cylindrical geometry via the virial route and for the condition of mechanical (hydrostatic) equilibrium. We also report the equation for the surface tension via the mechanical route. Monte Carlo and molecular dynamics simulation results are obtained for two example systems involving a fluid nano-phase of Lennard-Jones argon: a gas-liquid interface of cylindrical geometry and a confined nano-phase within a cylindrical carbon pore. All three diagonal elements of the pressure tensor are reported in each case, the component normal to the interface, = , and the two tangential components = and = , where (, ,) are the usual cylindrical polar coordinates. For the cylindrical pore, the tangential pressures, and , show strong compression in the adsorbed layers, as has been found in slit-shaped and spherical pores.

BACKGROUND: Chemicals in consumer products are a major contributor to human chemical coexposures. Consumers purchase and use a wide variety of products containing potentially thousands of chemicals. There is a need to identify potential real-world chemical coexposures to prioritize in vitro toxicity screening. However, due to the vast number of potential chemical combinations, this identification has been a major challenge. OBJECTIVES: We aimed to develop and implement a data-driven procedure for identifying prevalent chemical combinations to which humans are exposed through purchase and use of consumer products. METHODS: We applied frequent itemset mining to an integrated data set linking consumer product chemical ingredient data with product purchasing data from 60,000 households to identify chemical combinations resulting from co-use of consumer products. RESULTS: We identified co-occurrence patterns of chemicals over all households as well as those specific to demographic groups based on race/ethnicity, income, education, and family composition. We also identified chemicals with the highest potential for aggregate exposure by identifying chemicals occurring in multiple products used by the same household. Last, a case study of chemicals active in estrogen and androgen receptor in silico models revealed priority chemical combinations co-targeting receptors involved in important biological signaling pathways. DISCUSSION: Integration and comprehensive analysis of household purchasing data and product-chemical information provided a means to assess human near-field exposure and inform selection of chemical combinations for high-throughput screening in in vitro assays.

The risk to humans from chemicals in consumer products is a function of both hazard and exposure. There is an ongoing effort to quantify chemical exposure due to household articles such as furniture and building materials. Polymers and plastic materials make up a substantial portion of these articles, which may contain chemical additives such as plasticizers. When these additives are not bound to the polymer matrix, they are free to diffuse throughout it and leach or emit from the surface. We have implemented a methodology to predict plasticizer emission from polyvinyl chloride (PVC) products, based on group contribution methods that consider a free volume effect to estimate activity coefficients for chemicals in polymer-solvent solutions. Using the estimated activity coefficients, we calculate steady-state gas phase concentrations for plasticizers in equilibrium with the polymer surface (y 0 ). The method uses only the structure of the chemical and polymer, the weight fraction, and physical-chemical properties, allowing rapid estimation of y 0 at different weight fractions in PVC. Using the predicted y 0 values and weight fraction data gleaned from public databases, we estimate plasticizer exposures associated with 72 PVC-containing articles using a high-throughput model. We also investigate potential exposures associated with plasticizer substitutions in these products.