A.M. Manfreda

Molecular modeling of polymer composite–analyte interactions in electronic nose sensors

We report a molecular modeling study to investigate the polymer-carbon black (CB) composite-analyte interactions in resistive sensors. These sensors comprise the JPL electronic nose (ENose) sensing array developed for monitoring breathing air in human habitats. The polymer in the composite is modeled based on its stereoisomerism and sequence isomerism, while the CB is modeled as uncharged naphthalene rings with no hydrogens. The Dreiding 2.21 force field is used for the polymer, solvent molecules and graphite parameters are assigned to the carbon black atoms. A combination of molecular mechanics (MM) and molecular dynamics (NPT-MD and NVT-MD) techniques are used to obtain the equilibrium composite structure by inserting naphthalene rings in the polymer matrix. Polymers considered for this work include poly(4-vinylphenol), polyethylene oxide, and ethyl cellulose. Analytes studied are representative of both inorganic and organic compounds. The results are analyzed for the composite microstructure by calculating the radial distribution profiles as well as for the sensor response by predicting the interaction energies of the analytes with the composites.

Toward A Second Generation Electronic Nose at JPL: Sensing Film Optimization Studies

Development of a second generation Electronic Nose at JPL is focusing on optimization of the sensing films to increase sensitivity and optimization of the array. Toward this goal, studies have focused on sources of noise in the films, alternatives to carbon black as conductive medium, measurement techniques, and development of an analytical approach to polymer selection to maximize the abilities of the array to distinguish among compounds. methods of data acquisition, and selection of polymers for films in the sensing array. Sensing film optimization studies to increase sensitivity have been focused primarily on decreasing noise in the response. Studies with this goal have included investigation of the sensor films and of measurement methods. Studies of the films include studies of the polymers used in the films and of the fabrication methods, including consideration of several materials as possible replacements for carbon black as the conductive medium in the film, including noble metals, metal oxides and carbon nanotubes. Studies of measurement techniques have investigated the use of AC methods to follow sensor response. The first JPL ENose used DC measurements to monitor resistance changes in the sensing films. AC measurements of thin films can be more sensitive to changes at the interface of the electrodes and the films, and so AC techniques were considered for data acquisition.

Expanding the Analyte Set of the JPL Electronic Nose to Include Inorganic Species

An array-based sensing system based on 32 polymer/carbon composite conductometric sensors is under development at JPL. Until the present phase of development, the analyte set has focused on organic compounds (common solvents) and a few selected inorganic compounds, notably ammonia and hydrazine. The present phase of JPL ENose development has added two inorganics to the analyte set: mercury and sulfur dioxide. Through models of sensor-analyte response developed under this program coupled with a literature survey, approaches to including these analytes in the ENose target set have been determined.

Temperature effects on polymer-carbon composite sensors: Evaluating the role of polymer molecular weight and carbon loading

We report the effect of temperature coupled with varying polymer molecular weight and carbon loadings on the performance of polymer-carbon black composite films, used as sensing media in the JPL Electronic Nose (ENose). While bulk electrical properties of polymer composites have been studied, with mechanisms of conductivity described by connectivity and tunneling, it is not fully understood how environmental conditions and intrinsic polymer and filler properties affect polymer composite sensor characteristics and responses. Composites of polyethylene oxide (PEO)-carbon black (CB) considered here include PEO polymers with molecular weights of 20K, 600 K and 1M. The effects of polymer molecular weight on the percolation threshold of PEO-carbon composite and incremental sensor temperature effects on PEO-carbon sensor response were investigated. Results show a correlation between the polymer molecular weight and percolation threshold. Changes in sensor properties as a function of temperature are also observed at different carbon loadings; these changes may be explained by a change in conduction mechanism.

Chemical Sensor Array Response Modeling Using Quantitative Structure-Activity Relationships Technique

We have developed a Quantitative Structure-Activity Relationships (QSAR) based approach to correlate the response of chemical sensors in an array with molecular descriptors. A novel molecular descriptor set has been developed; this set combines descriptors of sensing film-analyte interactions, representing sensor response, with a basic analyte descriptor set commonly used in QSAR studies. The descriptors are obtained using a combination of molecular modeling tools and empirical and semi-empirical Quantitative Structure-Property Relationships (QSPR) methods. The sensors under investigation are polymer-carbon sensing films which have been exposed to analyte vapors at parts-per-million (ppm) concentrations; response is measured as change in film resistance. Statistically validated QSAR models have been developed using Genetic Function Approximations (GFA) for a sensor array for a given training data set. The applicability of the sensor response models has been tested by using it to predict the sensor activities for test analytes not considered in the training set for the model development. The validated QSAR sensor response models show good predictive ability. The QSAR approach is a promising computational tool for sensing materials evaluation and selection. It can also be used to predict response of an existing sensing film to new target analytes.

Developing sensor activity relationships for the JPL electronic nose sensors using molecular modeling and QSAR techniques

We report a quantitative structure-activity relationship (QSAR) study using genetic function approximation (GFA) to describe the polymer-carbon composite sensor activities in the JPL electronic nose (ENose), when exposed to chemical vapors at parts-per-million (ppm) concentration levels. A unique QSAR molecular descriptor set developed in this work combines the default analyte property set (thermodynamic, structural etc.) with sensing film-analyte interactions that describes the sensor response. These descriptors are calculated using semi-empirical and molecular modeling tools. The QSAR training data set consists of 15-20 analyte molecules specified by NASA for applications related to life support and habitation in space. The statistically validated QSAR model was also tested independently to predict the sensor activities for test analytes not considered in the training set