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Research Papers

Molecular Dynamics Simulations of Diffusion of O2 and N2 Penetrants in Polydimethylsiloxane-Based Nanocomposites

[+] Author and Article Information
Douglas E. Spearot1

Department of Mechanical Engineering,  University of Arkansas, Fayetteville, AR 72701dspearot@uark.edu

Alex Sudibjo, Varun Ullal, Adam Huang

Department of Mechanical Engineering,  University of Arkansas, Fayetteville, AR 72701

1

Corresponding author.

J. Eng. Mater. Technol 134(2), 021013 (Mar 27, 2012) (8 pages) doi:10.1115/1.4005921 History: Received September 21, 2011; Revised December 27, 2011; Published March 26, 2012; Online March 27, 2012

Recently, metal particle polymer composites have been proposed as sensing materials for micro corrosion sensors. To design the sensors, a detailed understanding of diffusion through metal particle polymer composites is necessary. Accordingly, in this work molecular dynamics (MD) simulations are used to study the diffusion of O2 and N2 penetrants in metal particle polymer nanocomposites composed of an uncross-linked polydimethylsiloxane (PDMS) matrix with Cu nanoparticle inclusions. PDMS is modeled using a hybrid interatomic potential with explicit treatment of Si and O atoms along the chain backbone and coarse-grained methyl side groups. In most models examined in this work, MD simulations show that diffusion coefficients of O2 and N2 molecules in PDMS-based nanocomposites are lower than that in pure PDMS. Nanoparticle inclusions act primarily as geometric obstacles for the diffusion of atmospheric penetrants, reducing the available porosity necessary for diffusion, with instances of O2 and N2 molecule trapping also observed at or near the PDMS/Cu nanoparticle interfaces. In models with the smallest gap between Cu nanoparticles, MD simulations show that O2 and N2 diffusion coefficients are higher than that in pure PDMS at the lowest temperatures studied. This is due to PDMS chain confinement at low temperatures in the presence of the Cu nanoparticles, which induces low-density regions within the PDMS matrix. MD simulations show that the role of temperature on diffusion can be modeled using the Williams–Landel–Ferry equation, with parameters influenced by nanoparticle content and spacing.

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Copyright © 2012 by American Society of Mechanical Engineers
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Figures

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Figure 1

Projection view of the nanocomposite construction procedure.

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Figure 2

(a) Nanocomposite model showing embedded Cu nanoparticles. (b) Detailed image showing that a single PDMS chain interacts with many Cu nanoparticles at the culmination of the equilibration procedure. Isolated spheres represent the randomly distributed penetrant species.

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Figure 3

Averaged mean-squared displacement of 100 O2 penetrants during diffusion through one configuration of a PDMS nanocomposite with (a) 20 chains with 5 vol. % Cu nanoparticles and (b) 80 chains with 5 vol. % Cu nanoparticles. The diffusion coefficient at each temperature is proportional to the least-squared linear fit

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Figure 4

(a) and (b) Temperature dependence of the diffusion coefficient for O2 diffusion through PDMS and PDMS nanocomposites with 5 vol. % Cu nanoparticles and different numbers of PDMS chains. The Williams–Landel–Ferry model captures the role of temperature, whereas an Arrhenius relationship is only valid at temperatures well above Tg .

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Figure 5

Average mean-squared displacement of 100 O2 penetrants during diffusion through one configuration of a PDMS nanocomposite with (a) 20 chains with 2 vol. % Cu nanoparticles and (b) 20 chains with 10 vol. % Cu nanoparticles. The diffusion coefficient at each temperature is proportional to the least-squared linear fit.

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Figure 6

(a) Temperature dependence of the diffusion coefficient for O2 diffusion through PDMS and PDMS nanocomposites as a function of Cu nanoparticle volume fraction. The William–Landel–Ferry model captures the role of temperature. (b) Relationship between the nanoparticle volume fraction and the WLF parameters is extracted from (a).

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Figure 7

Schematic illustration of the variation in PDMS density within the nanocomposite models when PDMS regions strongly influenced by the nanoparticle surfaces overlap

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Figure 8

Comparison between O2 and N2 diffusion in PDMS-based nanocomposites. Comparison is presented only for pure PDMS and configuration 7

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Figure 9

Trajectory of select O2 penetrants during diffusion in a PDMS-based nanocomposite with 20 PDMS chains and 10 vol. % nanoparticle volume fraction for 200 ps at 300 K

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