Abstract
We present a generic approach for designing sensor arrays for a given chemical sensing task. First, we present a correlation-based metric to systematically assess the analytical information obtained from the conductometric responses of chemiresistive films as a function of their operating temperatures and material composition. We illustrate how this measure can also be used to test the reproducibility of signals obtained from sensors of equal manufacture. Next, complementing the correlation-based analysis, we employ a statistical dimensionality-reduction algorithm to visualize the multivariate sensor response obtained from sensor arrays. We adapt this method to quantify the discriminability of chemical fingerprints. Finally, we show how to determine an optimal set of material compositions to be incorporated within an array for individual species' recognition when practical constraints/tradeoffs on fabrication are also considered. We validate our approach by designing a microsensor array for the task of recognizing a chemical hazard at sub-lethal concentrations in complex environments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mention of these and any other commercial products is strictly for provision of proper experimental definition, and does not constitute an endorsement by the National Institute of Standards and Technology
Hierlemann, A.; Gutierrez-Osuna, R., Higher-order chemical sensing, Chem. Rev. 2008, 108, 563–613
Pearce, T. C.; Sanchez-Montanes, M., Chemical sensor array optimization: geometric and information theoretic approaches, In Handbook of Machine Olfaction: Electronic Nose Technology; Pearce, T. C.; Schiffman, S. S.; Nagle, H. T.; Gardner, J. W., Eds. Wiley-VCH, Weinheim, 2002, 347–376.
Wilson, D.; Garrod, S.; Hoyt, S.; McKennoch, S.; Booksh, K. S., Array optimization and preprocessing techniques for chemical sensing microsystems, Sens. Update 2002, 10, 77–106
Semancik, S.; Cavicchi, R. E.; Wheeler, M. C.; Tiffany, J. E.; Poirier, G. E.; Walton, R. M.; Suehle, J. S.; Panchapakesan, B.; Devoe, D. L., Microhotplate platforms for chemical sensor research, Sens. Actuators B 2001, 77, 579–591
Semancik, S.; Cavicchi, R., Kinetically controlled chemical sensing using micromachined structures, Acc. Chem. Res. 1998, 31, 279–287
Cavicchi, R. E.; Suehle, J. S.; Kreider, K. G.; Gaitan, M.; Chaparala, P., Fast temperature programmed sensing for micro-hotplate gas sensors, IEEE Electron Device Lett. 1995, 16, 286–288
Batzill, M.; Diebold, U., The surface and materials science of tin oxide, Sens. Actuators B 1997, 43, 45–51
Martinez, C. J.; Hockey, B.; Montgomery, C. B.; Semancik, S., Porous tin oxide nanostructured microspherers for sensor applications, Langmuir 2005, 21, 7937–7944
Raman, B.; Hertz, J.; Benkstein, K.; Semancik, S., A bioinspired methodology for artificial olfaction, Anal. Chem. 2008, 80, 8364–8371
Meier, D. C.; Taylor, C. J.; Cavicchi, R. E.; White, E.; Semancik, S.; Ellzy, M. W.; Sumpter, K. B., Chemical warfare agent detection using MEMS-compatible microsensor arrays, IEEE Sens. J. 2005, 5, 712–725
Bârsan, N.; Weimar, U., Understanding the fundamental principles of metal oxide based gas sensors; the example of CO sensing with SnO2 sensors in the presence of humidity, J. Phys. Condens Matter 2003, 15, R813–R819
Tomchenko, A. A.; Harmer, G. P.; Marquis, B. T., Detection of chemical warfare agents using nanostructured metal-oxide sensors, Sens. Actuators B 2005, 108, 41–55
Vaid, T. P.; Burl, M. C.; Lewis, N. S., Comparison of the performance of different discriminant algorithms in analyte discrimination tasks using an array of carbon black-polymer composite vapor detectors, Anal. Chem. 2001, 73, 321–331
Albert, K. J.; Lewis, N.; Schauer, C.; Sotzing, G. A.; Stitzel, S. E.; Vaid, T. P.; Walt, D. R., Cross-reactive chemical sensor arrays, Chem. Rev. 2000, 100, 2595–2626
Ding, J.; McAvoy, T. J.; Cavicchi, R. E.; Semancik, S., Surface state trapping models for SnO2-based microhotplate sensors, Sens. Actuators B 2001, 77, 597–613
Gaggiotti, G.; Galdikas, A.; Kačiulis, S.; Mattogno, G.; Šetkus, A., Temperature dependencies of sensitivity and surface chemical composition of SnO x gas sensors, Sens. Actuators B 1995, 24–25, 516–519
White, N.; Turner, J., Thick-film sensors: Past, present and future, Meas. Sci. Technol. 1997, 8, 1–20
Panchapekesan, B.; Cavicchi, R.; Semancik, S.; DeVoe, D. L., Sensitivity, selectivity and stability of tin oxide nanostructures on large area arrays of microhotplates, Nanotechnology 2006, 17, 415–425
Meier, D. C.; Semancik, S., Effects of Materials Chemistry on Conductometric Sensor Signals. In 2005 Materials Research Society Meeting Boston, 2005
Raman, B.; Meier, D.; Evju, J.; Semancik, S., Designing and optimizing microsensor arrays for recognizing chemical hazards in complex environments, Sens. Actuators B 2009, 137, 617–629
Duda, R. O.; Hart, P. E.; Stork, D. G., Pattern Classification, 2nd edn.; Wiley-Interscience, New York, 2000, 115–121
Acknowledgments
We acknowledge partial financial support of this project by the U.S. Department of Homeland Security, Science and Technology Directorate. BR was supported by a NIH(NIBIB)-NIST Joint Postdoctoral Associateship Award administered through the National Research Council. We thank Kurt Benkstein, Mike Carrier, Steve Fick, Jim Melvin, Wyatt Miller, Chip Montgomery, Casey Mungle, Jim Yost, Blaine Young, and Li Zhang for their valuable contributions to this project. We are grateful to Mark Stopfer for his helpful comments on an earlier version of this manuscript.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Raman, B., Meier, D.C., Semancik, S. (2009). A Statistical Approach to Materials Evaluation and Selection for Chemical Sensor Arrays. 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_10
Download citation
DOI: https://doi.org/10.1007/978-0-387-73715-7_10
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-73714-0
Online ISBN: 978-0-387-73715-7
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)