Characterization of Mixtures. Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks.

In our earlier work, we have demonstrated that it is possible to characterize binary mixtures using single component descriptors by applying various mixing rules. We also showed that these methods were successful in building predictive QSPR models to study various mixture properties of interest. Here in, we developed a QSPR model of an excess thermodynamic property of binary mixtures i.e. excess molar volume (V(E) ). In the present study, we use a set of mixture descriptors which we earlier designed to specifically account for intermolecular interactions between the components of a mixture and applied successfully to the prediction of infinite-dilution activity coefficients using neural networks (part 1 of this series). We obtain a significant QSPR model for the prediction of excess molar volume (V(E) ) using consensus neural networks and five mixture descriptors. We find that hydrogen bond and thermodynamic descriptors are the most important in determining excess molar volume (V(E) ), which is in line with the theory of intermolecular forces governing excess mixture properties. The results also suggest that the mixture descriptors utilized herein may be sufficient to model a wide variety of properties of binary and possibly even more complex mixtures.

The effect of aggregation on the electrical conductivity of spin-coated polymer/carbon nanotube composite films.

This paper considers the feasibility of replacing indium tin oxide (ITO) with spin-coated, polymer-based composite films that are filled with multiwalled carbon nanotubes (MWNTs). The coating mixture consists of a solvent with low volatility, a dissolved thermoplastic polymer, and MWNTs. The high aspect ratio of MWNTs and their good electrical conductivity enable electrical percolation at very low concentrations, so that films can be prepared that conduct electricity while retaining good optical transparency. Although the MWNTs are driven to aggregate by Van der Waals interactions, the high viscosity of the polymer/solvent solution enables the preparation of metastable, homogeneous dispersions. However, exposing the mixtures to shear leads to aggregation, the magnitude of which depends on the duration of the shear. This effect could be observed directly in spin-coated films using both optical microscopy and conductivity measurements, with aggregation causing a drop in conductivity at high nanotube loading, and more complex non-monotonic behavior at concentrations approaching the percolation threshold.