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

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Computational Methods for Sensor Material Selection

Part of the book series: Integrated Analytical Systems ((ANASYS))

Abstract

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.

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Acknowledgments

This research was funded by the Advanced Environmental Monitoring and Control Program of NASA. This work was carried out at the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration. The authors thank Prof. Anton Hopfinger and Dr. David Rogers for their very valuable suggestions and the Caltech Material and Process Simulation center (MSC) for use of computing facilities.

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Authors and Affiliations

  1. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA, 91109, USA

    Abhijit V. Shevade, Margaret A. Ryan, Margie L. Homer, Hanying Zhou, Allison M. Manfreda, Shiao -Pin S. Yen, Kenneth S. Manatt & Adam K. Kisor

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  1. Abhijit V. Shevade
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  2. Margaret A. Ryan
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  3. Margie L. Homer
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  4. Hanying Zhou
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  5. Allison M. Manfreda
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  6. Liana M. Lara
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  7. Shiao -Pin S. Yen
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  8. April D. Jewell
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  9. Kenneth S. Manatt
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  10. Adam K. Kisor
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Correspondence to Abhijit V. Shevade .

Editor information

Editors and Affiliations

  1. Jet Propulsion Lab., California Institute of Technology, Oak Grove Drive 4800, Pasadena, 91109-8099, U.S.A.

    Margaret A. Ryan

  2. Jet Propulsion Lab., California Institute of Technology, Oak Grove Drive 4800, Pasadena, 91109-8099, U.S.A.

    Abhijit V. Shevade

  3. Pomona College, Seaver Chemistry Laboratory, N. College Avenue 645, Claremont, 91711, U.S.A.

    Charles J. Taylor

  4. Jet Propulsion Lab., California Institute of Technology, Oak Grove Drive 4800, Pasadena, 91109-8099, U.S.A.

    M. L. Homer

  5. Molecular Simulations Center, California Institute of Technology, Pasadena, 91125, U.S.A.

    Mario Blanco

  6. KWJ Engineering, Inc., Central Avenue 8440, Newark, 94560, U.S.A.

    Joseph R. Stetter

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Shevade, A.V. et al. (2009). Chemical Sensor Array Response Modeling Using Quantitative Structure-Activity Relationships Technique. In: Ryan, M., Shevade, A., Taylor, C., Homer, M., Blanco, M., Stetter, J. (eds) Computational Methods for Sensor Material Selection. Integrated Analytical Systems. Springer, New York, NY. https://doi.org/10.1007/978-0-387-73715-7_8

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