Development of a Molecular Model for Understanding the Polymer-metal Interface in Solid State Pumps

Jaime D. Guevara, Matthew L. Jones, Peter Müllner, and Eric Jankowski

Volume 11, Issue 2 (April 2020), pp. 12–22

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  author={Jaime D. Guevara and Matthew L. Jones and Peter M\"{u}llner and Eric Jankowski},
  title={Development of a Molecular Model for Understanding the Polymer-metal Interface in Solid State Pumps},
  journal={The Journal of Computational Science Education},
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Medical micropumps that utilize Magnetic Shape Memory (MSM) alloys are small, powerful alternatives to conventional pumps because of their unique pumping mechanism. This mechanism—the transfer of fluid through the emulation of peristaltic contractions—is enabled by the magneto-mechanical properties of a shape memory alloy and a sealant material. Because the adhesion between the sealant and the alloy determines the performance of the pump and because the nature of this interface is not well characterized, an understanding of sealant-alloy interactions represents a fundamental component of engineering better solid state micropumps in particular, and metal-polymer interfaces in general. In this work we develop computational modeling techniques for investigating how the properties of sealant materials determine their adhesive properties with alloys. Specifically, we develop a molecular model of the sealant material polydimethylsiloxane (PDMS) and characterize its behavior with a model Ni-Mn-Ga surface. We perform equilibrium molecular dynamics simulations of the PDMS/Ni-Mn-Ga interface to iteratively improve the reliability, numerical stability, and accuracy of our models and the associated data workflow. To this end, we develop the first model for simulating PDMS/Ni-Mn-Ga interfaces by combining the Optimized Potentials for Liquid Simulations (OPLS) [21] force field with the Universal Force Field [5], and show promise for informing the design of more reliable MSM micropumps. We also reflect on the experiences of Blue Waters Supercomputing intern Guevara (the first author) to identify key learning moments during the one-year internship that can help guide future molecular simulation training efforts.