Condensation of all-cis-tetraphenylcyclotetrasiloxanetetraol in ammonia: new method for preparation of ladder-like polyphenylsilsesquioxanes

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Stability of O3 + N2, O3 + O2 and O3 + CO2 double hydrates: DFT study

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Existence and Stability for Spherical Crystals Growing in a Supersaturated Solution

We consider the growth of a spherical crystal in a supersaturatedsolution. In the first part, existence and uniqueness resultsfor radially symmetric growth are obtained, provided that thesupersaturation is not too large; conversely, when the far-fieldsupersaturation exceeds a critical value, it is shown that theradially symmetric solution ceases to exist in finite time.In the second part, we examine the linear stability of a radiallysymmetric similarity solution (in which the radius grows ast?) to shape perturbations. The results are compared with previousquasi-static analyses, and, in particular, the critical radiusat which the crystal becomes unstable is found to be largerfor small supersaturations, but smaller for large supersaturations,than those predicted by the quasi-static analysis     

New ligands for the Fe(III)‐mediated reverse atom transfer radical polymerization of methyl methacrylate

A series of (di)picolinic acids and their derivates are investigated as novel complexing tridentate or bidentate ligands in the iron‐mediated reverse atom transfer radical polymerization of methyl methacrylate in N,N‐dimethylformamide at 100 °C with 2,2′‐azobisisobutyrontrile as an initiator. The polymerization rates and polydispersity indices (1.32–1.8) of the resulting polymers are dependent on the structures of the ligands employed. Different iron complexes may be involved in iron‐mediated reverse atom transfer radical polymerization, depending on the type of acid used. ¹H NMR spectroscopy has been used to study the structure of the resulting polymers. Chain‐extension reactions have been performed to further confirm the living nature of this catalytic system. © 2006 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 44: 2912–2921, 2006     

Nanostructured thermoset epoxy resin templated by an amphiphilic poly(ethylene oxide)‐block‐poly(dimethylsiloxane) diblock copolymer

An amphiphilic poly(ethylene oxide)‐block‐poly(dimethylsiloxane) (PEO–PDMS) diblock copolymer was used to template a bisphenol A type epoxy resin (ER); nanostructured thermoset blends of ER and PEO–PDMS were prepared with 4,4′‐methylenedianiline (MDA) as the curing agent. The phase behavior, crystallization, hydrogen‐bonding interactions, and nanoscale structures were investigated with differential scanning calorimetry, Fourier transform infrared spectroscopy, transmission electron microscopy, and small‐angle X‐ray scattering. The uncured ER was miscible with the poly(ethylene oxide) block of PEO–PDMS, and the uncured blends were not macroscopically phase‐separated. Macroscopic phase separation took place in the MDA‐cured ER/PEO–PDMS blends containing 60–80 wt % PEO–PDMS diblock copolymer. However, the composition‐dependent nanostructures were formed in the cured blends with 10–50 wt % PEO–PDMS, which did not show macroscopic phase separation. The poly(dimethylsiloxane) microdomains with sizes of 10–20 nm were dispersed in a continuous ER‐rich phase; the average distance between the neighboring microdomains was in the range of 20–50 nm. The miscibility between the cured ER and the poly(ethylene oxide) block of PEO–PDMS was ascribed to the favorable hydrogen‐bonding interaction. © 2006 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 44: 3042–3052, 2006     

Error analysis: a tool for selecting travel demand models

In developing travel demand models it is generally assumed that the base-year data used in developing the parameters, as well as the forecasted data to be used as independent variables for the design year, are of acceptable quality. The purpose of this paper is to present the application of error propagation theory in assesing the predictive quality of one type of travel demand forecasting model (multinomial logit models) and to demonstrate how error considerations can be used as a tool for identifying the optimal model. The general conclusions of this study are that: (1) it is indeed possible to quantify errors in dependent variables in logit models as a consequence of errors in independent variables; and (2) error consideration can be used as a tool for identifying the optimal model from a set of candidate models. Further research is recommended to develop better insights into the phenomenon of error propagation so that the consideration of errors can be a factor in decisions on model selection.