Correlating Polymer-Carbon Composite Sensor Response with Molecular Descriptors

We report a quantitative structure-activity relationships (QSAR) study using genetic function approximations to describe the activities of a polymer-carbon composite chemical vapor sensor using a novel approach to selecting a molecular descriptor set. The measured sensor responses are conductivity changes in polymer-carbon composite films upon exposure to target vapors at parts-per-million concentrations. The descriptor set combines the basic analyte descriptor set commonly used in QSAR studies with descriptors for sensing film-analyte interactions. The basic analyte descriptors are obtained using a combination of empirical and semiempirical quantitative structure-property relationships methods. The descriptors for the sensing film-analyte interactions are calculated using molecular modeling and simulation tools. A statistically validated QSAR model was developed for a training data set consisting of 17 analyte molecules. The applicability of this model was also tested by predicting sensor activities for three test analytes not considered in the training set.