Strong, Tailored, Biocompatible Shape-Memory Polymer Networks

Authors

  • Christopher M. Yakacki,

    Corresponding author
    1. Research and Development, MedShape Solutions, Inc. Atlanta, GA 30318 (USA)
    2. School of Materials Science and Engineering, The Georgia Institute of Technology, Atlanta, GA 30332 (USA)
    • Research and Development, MedShape Solutions, Inc. Atlanta, GA 30318 (USA).
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  • Robin Shandas,

    1. Department of Mechanical Engineering, University of Colorado Boulder, CO 80309 (USA)
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  • David Safranski,

    1. School of Materials Science and Engineering, The Georgia Institute of Technology, Atlanta, GA 30332 (USA)
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  • Alicia M. Ortega,

    1. Department of Mechanical Engineering, University of Colorado Boulder, CO 80309 (USA)
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  • Katie Sassaman,

    1. Research and Development, MedShape Solutions, Inc. Atlanta, GA 30318 (USA)
    2. School of Materials Science and Engineering, The Georgia Institute of Technology, Atlanta, GA 30332 (USA)
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  • Ken Gall

    1. Research and Development, MedShape Solutions, Inc. Atlanta, GA 30318 (USA)
    2. School of Materials Science and Engineering, The Georgia Institute of Technology, Atlanta, GA 30332 (USA)
    3. Woodruff School of Mechanical Engineering, The Georgia Institute of Technology, Atlanta, GA 30332 (USA)
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  • This work was supported by NIH grants HL 067393 and EB 004481. The authors would like to thank Michael Lyons for his help and contributions to this work. A. M. Ortega is thankful for the financial support by the NIAMS/NIH F31AR053466.

Abstract

Shape-memory polymers are a class of smart materials that have recently been used in intelligent biomedical devices and industrial applications for their ability to change shape under a predetermined stimulus. In this study, photopolymerized thermoset shape-memory networks with tailored thermomechanics are evaluated to link polymer structure to recovery behavior. Methyl methacrylate (MMA) and poly(ethylene glycol) dimethacrylate (PEGDMA) are copolymerized to create networks with independently adjusted glass transition temperatures (Tg) and rubbery modulus values ranging from 56 to 92 °C and 9.3 to 23.0 MPa, respectively. Free-strain recovery under isothermal and transient temperature conditions is highly influenced by the Tg of the networks, while the rubbery moduli of the networks has a negligible effect on this response. The magnitude of stress generation of fixed-strain recovery correlates with network rubbery moduli, while fixed-strain recovery under isothermal conditions shows a complex evolution for varying Tg. The results are intended to help aid in future shape-memory device design and the MMA-co-PEGDMA network is presented as a possible high strength shape-memory biomaterial.

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