Correlating Polymer-Carbon Composite Sensor Response with Molecular Descriptors

Abstract

We report a quantitative structure-activity relationships ͑QSAR͒ 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 partsper-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.

Operation of Third Generation JPL Electronic Nose on the International Space Station

“…In addition, work in classifying the unknown events by functional group and by applying models of sensor-analyte response [12,13] is underway. …”

Computational Approaches to Design and Evaluation of Chemical Sensing Materials

“…Interaction energies are calculated using quantum mechanics (QM) using the B3LYP and X3LYP flavors of density functional theory (DFT) with basis superposition error (BSSE) corrections. 8 These QM results were used to develop a first principles force field for use in the calculation of interaction energies of SO 2 6,7 The binding energy curves were also fitted by empirical functional form to get a force field to perform large scale polymer simulations.…”

“…Computational methods used in materials simulations have made tremendous strides in the last two decades, including development of approaches that use first principles QM techniques, molecular dynamics (MD) or atomistic techniques, statistical mechanical and multiscale approaches. 1 In addition to purely computational methods, statistical and multivariate methods have been widely used; these approaches include semiempirical and combinatorial methods that use molecular descriptors, as in quantitative structural activity relationships/quantitative structure-property relationships (QSAR/QSPR) 2 and linear solvation energy relationships. 3 On a fundamental level, describing or characterizing the properties of materials depends on understanding chemical interactions, which may take place between the material and other chemical species with which it may come into contact; this understanding, in turn, involves understanding the electronic and atomic level descriptions of materials.…”

Ground Validation of the Third Generation JPL Electronic Nose

“…has shown that the interaction energy of an analyte with the sensor matrix is decreased as water content in the sensor increases; it might therefore be expected that air with lower humidity, 5000 ppm water or RH about 20%, would result in better success rates than air with nominal humidity (about 40% RH) [10,11]. However, the data in Table 4 indicate that this is not the case.…”