Determination of domain sizes in blends of poly(ethylene) and poly(styrene) formed in the presence of supercritical carbon dioxide

Well-mixed blends of poly(ethylene) and poly(styrene) have been synthesized using supercritical carbon dioxide as a solvent. The morphology of the blends has been conclusively characterized using differential scanning calorimetry (DSC), small-angle X-ray scattering (SAXS), Raman microprobe microscopy, and C-13 solid-state cross-polarization magic angle spinning NMR (C-13 CPMAS NMR). DSC measurements demonstrate that poly(styrene) in the blends resides solely in the amorphous regions of the poly(ethylene) matrix; however, corroborative evidence from the SAXS experiments shows that poly(styrene) resides within the interlamellar spaces. The existence of nanometer-sized domains of poly(styrene) was shown within a blend of poly(styrene) and poly(ethylene) when formed in supercritical carbon dioxide using Raman microprobe microscopy and C-13 CPMAS NMR spectroscopy coupled with a spin diffusion model. This contrasts with blends formed at ambient pressure in the absence of solvent, in which domains of poly(styrene) in the micrometer size range are formed. This apparent improved miscibility of the two components was attributed to better penetration of the monomer prior to polymerization and increased swelling of the polymer substrate by the supercritical carbon dioxide solvent.

Modeling and exploiting behavior patterns in dynamic environments

This paper presents a new approach to improving the effectiveness of autonomous systems that deal with dynamic environments. The basis of the approach is to find repeating patterns of behavior in the dynamic elements of the system, and then to use predictions of the repeating elements to better plan goal directed behavior. It is a layered approach involving classifying, modeling, predicting and exploiting. Classifying involves using observations to place the moving elements into previously defined classes. Modeling involves recording features of the behavior on a coarse grained grid. Exploitation is achieved by integrating predictions from the model into the behavior selection module to improve the utility of the robot's actions. This is in contrast to typical approaches that use the model to select between different strategies or plays. Three methods of adaptation to the dynamic features of the environment are explored. The effectiveness of each method is determined using statistical tests over a number of repeated experiments. The work is presented in the context of predicting opponent behavior in the highly dynamic and multi-agent robot soccer domain (RoboCup)