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Issue 3, 2016
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Hierarchical design of synthetic gel composites optimized to mimic the impact energy dissipation response of brain tissue

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Abstract

Synthetic polymer gels that accurately mimic key mechanical properties of brain tissue are valuable tools for evaluating protective equipment and understanding injury mechanisms, for example in response to concentrated mechanical impact events. Here, we employ impact indentation to investigate the response of brain tissue from three species (mice, rats, and pigs), quantified in terms of penetration resistance, energy dissipation capacity, and energy dissipation rate. We identify measurable variations in these three metrics among the different animal models, suggesting that a highly tunable materials system is required to capture the full impact response of specific brain models. To achieve enhanced tunability of energy dissipation, we engineer bilayered polymer composites based on polydimethylsiloxane (PDMS) elastomers and swollen organogels. This bilayer design leverages the key properties of each individual layer to decouple the penetration resistance and energy dissipation characteristics of the composite material. Additionally, we demonstrate that by sequentially tuning the stiffness and thickness of the top layer, all three of these impact response metrics can be optimized to match that of porcine brain tissue. Together, these results suggest that the mechanical behavior of composite gels under impact loading can be modulated to mimic different brain tissues and brain injury models with high fidelity.

Graphical abstract: Hierarchical design of synthetic gel composites optimized to mimic the impact energy dissipation response of brain tissue

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Publication details

The article was received on 14 Jun 2016, accepted on 27 Jul 2016 and first published on 09 Aug 2016


Article type: Paper
DOI: 10.1039/C6ME00051G
Citation: Mol. Syst. Des. Eng., 2016,1, 290-300
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    Hierarchical design of synthetic gel composites optimized to mimic the impact energy dissipation response of brain tissue

    B. Qing and K. J. Van Vliet, Mol. Syst. Des. Eng., 2016, 1, 290
    DOI: 10.1039/C6ME00051G

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