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.
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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.
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Blanco, M., Shevade, A.V., Ryan, M.A. (2009). Quantum Mechanics and First-Principles Molecular Dynamics Selection of Polymer Sensing Materials. 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_3
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