Advertisement

Quantum Mechanics and First-Principles Molecular Dynamics Selection of Polymer Sensing Materials

  • Mario Blanco
  • Abhijit V. Shevade
  • Margaret A. Ryan
Chapter
Part of the Integrated Analytical Systems book series (ANASYS)

Abstract

We present two first-principles methods, density functional theory (DFT) and a molecular dynamics (MD) computer simulation protocol, as computational means for the selection of polymer sensing materials. The DFT methods can yield binding energies of polymer moieties to specific vapor bound compounds, quantities that were found useful in materials selection for sensing of organic and inorganic compounds for designing sensors for the electronic nose (ENose) that flew on the International Space Station (ISS) in 2008–2009. Similarly, we present an MD protocol that offers high consistency in the estimation of Hildebrand and Hansen solubility parameters (HSP) for vapor bound compounds and amorphous polymers. HSP are useful for fitting measured polymer sensor responses with physically rooted analytical models. We apply the method to the JPL electronic nose (ENose), an array of sensors with conducting leads connected through thin film polymers loaded with carbon black. Detection relies on a change in electric resistivity of the polymer film as function of the amount of swelling caused by the presence of the analyte chemical compound. The amount of swelling depends upon the chemical composition of the polymer and the analyte molecule. The pattern is unique and it unambiguously identifies the compound. Experimentally determined changes in relative resistivity of fifteen polymer sensor materials upon exposure to ten vapors were modeled with the first-principles HSP model.

Keywords

Quantitative Structure Property Relationship Quantitative Structure Property Relationship Hansen Solubility Parameter Hildebrand Solubility Parameter Polymer Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported in part by the Materials and Process Simulation Center, Beckman Institute at the California Institute of Technology and by a grant from NASA.

References

  1. 1.
    Ryan, M. A.; Shevade, A. V.; Zhou, H.; Homer, M. L., Polymer-carbon black composite sensors in an electronic nose for air-quality monitoring, Mrs Bull. 2004, 29, 714–719CrossRefGoogle Scholar
  2. 2.
    Ryan, M. A.; Zhou, H. Y.; Buehler, M. G.; Manatt, K. S.; Mowrey, V. S.; Jackson, S. R.; Kisor, A. K.; Shevade, A. V.; Homer, M. L., Monitoring space shuttle air quality using the jet propulsion laboratory electronic nose, IEEE Sensors J. 2004, 4, 337–347CrossRefGoogle Scholar
  3. 3.
    Zhou, H. Y.; Homer, M. L.; Shevade, A. V.; Ryan, M. A., Nonlinear least-squares based method for identifying and quantifying single and mixed contaminants in air with an electronic nose, Sensors 2006, 6, 1–18CrossRefGoogle Scholar
  4. 4.
    Shevade, A. V.; Homer, M. L.; Taylor, C. J.; Zhou, H. Y.; Jewell, A. D.; Manatt, K. S.; Kisor, A. K.; Yen, S. P. S.; Ryan, M. A., Correlating polymer–carbon composite sensor response with molecular descriptors, J. Electrochem. Soc. 2006, 153, H209–H216CrossRefGoogle Scholar
  5. 5.
    Shevade, A. V.; Ryan, M. A.; Homer, M. L.; Kisor, A. K.; Manatt, K. S.; Lin, B.; Fleurial, J. P.; Manfreda, A. M.; Yen, S. P. S., Calorimetric measurements of heat of sorption in polymer films: A molecular modeling and experimental study, Anal. Chim. Acta 2005, 543, 242–248CrossRefGoogle Scholar
  6. 6.
    Shevade, A. V., Developing sensor activity relationships for the JPL electronic nose sensors using molecular modeling and QSAR techniques, 2005 IEEE Sensors (IEEE Cat. No.05CH37665C) 2005, 4 pp.Google Scholar
  7. 7.
    Cozmuta, I.; Blanco, M.; Goddard, W. A., Gas sorption and barrier properties of polymeric membranes from molecular dynamics and Monte Carlo simulations, J. Phys. Chem. B 2007, 111, 3151–3166CrossRefGoogle Scholar
  8. 8.
    Belmares, M.; Blanco, M.; Goddard, W. A.; Ross, R. B.; Caldwell, G.; Chou, S. H.; Pham, J.; Olofson, P. M.; Thomas, C., Hildebrand and Hansen solubility parameters from molecular dynamics with applications to electronic nose polymer sensors, J. Comput. Chem. 2004, 25, 1814–1826CrossRefGoogle Scholar
  9. 9.
    Lin, S. T.; Blanco, M.; Goddard, W. A., The two-phase model for calculating thermodynamic properties of liquids from molecular dynamics: Validation for the phase diagram of Lennard-Jones fluids, J. Chem. Phys. 2003, 119, 11792–11805CrossRefGoogle Scholar
  10. 10.
    Becke, A. D., Density-functional thermochemistry. 3. The role of exact exchange, J. Chem. Phys. 1993, 98, 5648–5652CrossRefGoogle Scholar
  11. 11.
    Lee, C. T.; Yang, W. T.; Parr, R. G., Development of the Colle–Salvetti correlation-energy formula into a functional of the electron-density, Phys. Rev. B 1988, 37, 785–789CrossRefGoogle Scholar
  12. 12.
    Xu, X.; Goddard, W. A., The X3LYP extended density functional for accurate descriptions of nonbond interactions, spin states, and thermochemical properties, Proc. Natl Acad. Sci. USA 2004, 101, 2673–2677CrossRefGoogle Scholar
  13. 13.
    Zhao, Y., Development and assessment of a new hybrid density functional model for thermochemical kinetics, J. Phys. Chem. A 2004, 108, 2715–2719CrossRefGoogle Scholar
  14. 14.
    Zhao, Y., A density functional that accounts for medium-range correlation energies in organic chemistry, Org. Lett. 2006, 8, 5753–5755CrossRefGoogle Scholar
  15. 15.
    Zhao, Y., Comparative DFT study of van der Waals complexes: Rare-gas dimers, alkaline-earth dimers, zinc dimer, and zinc-rare-gas dimers, J. Phys. Chem. A 2006, 110, 5121–5129CrossRefGoogle Scholar
  16. 16.
    Zhao, Y., Density functionals with broad applicability in chemistry. Acc. Chem. Res. 2008, 41, 157–167CrossRefGoogle Scholar
  17. 17.
    Mayo, S. L.; Olafson, B. D.; Goddard, W. A., Dreiding – A generic force-field for molecular simulations. J. Phys. Chem. 1990, 94, 8897–8909CrossRefGoogle Scholar
  18. 18.
    Blanco, M., Molecular silverware.1. General-solutions to excluded volume constrained problems. J. Comput. Chem. 1991, 12, 237–247CrossRefGoogle Scholar
  19. 19.
    Ryan, M. A.; Shevade, A. V.; Taylor, C. J.; Homer, M. L.; Jewell, A. D.; Kisor, A. K.; Manatt, K. S.; Yen, S. P. S.; Blanco, M.; Goddard III, W. A. In expanding the capabilities of the JPL electronic nose for an international space station technology demonstration, In Proceedings of 36th International Conference on Environmental Systems 2006, 2006–01–2179, Norfolk, Virginia, USAGoogle Scholar
  20. 20.
    Allen, M. P.; Tildesley, D. J., Computer Simulations of Liquids, Oxford University Press, Oxford, 1987 Google Scholar
  21. 21.
    Frenkel, D., Computer Simulation in Chemical Physics, Kjeuver, New York, 1993 Google Scholar
  22. 22.
    van Krevelen, D. W., Properties of Polymers: Their Correlation with Chemical Structure; their Numerical Estimation and Prediction from Group Contributions, Elsevier Science, New York, 1990 Google Scholar
  23. 23.
    Hansen, C. M., The three dimensional solubility parameter – Key to paint component affinities I. – Solvents, plasticizers, polymers, and resins. J. Paint Technol. 1967, 39, 104–117Google Scholar
  24. 24.
    Jaguar, 7.207; Schrodinger, LLC, NY, 2007 Google Scholar
  25. 25.
    Accelrys, I. Cerius2, 4.01, Accelrys, Inc.: San Diego, CA, 2005 Google Scholar
  26. 26.
    Lin, S. T.; Blanco, M. Rotational Isomeric State Table Algorithm, California Institute of Technology, Pasadena, CA, 2003 Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Mario Blanco
    • 1
  • Abhijit V. Shevade
    • 2
  • Margaret A. Ryan
    • 2
  1. 1.Division of Chemistry and Chemical EngineeringCalifornia Institute of TechnologyPasadenaUSA
  2. 2.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA

Personalised recommendations