Predictive modelling-based design and experiments for synthesis and spinning of bioinspired silk fibres
Name
Lin-2015-Predictive modelling.pdf
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Author(s)
Lin, Shangchao
Ryu, Seunghwa
Tokareva, Olena
Gronau, Greta
Jacobsen, Matthew M.
Huang, Wenwen
Rizzo, Daniel J.
Li, David
Staii, Cristian
Pugno, Nicola M.
Date Issued
May 2015
Journal
Nature Communications
Publisher
Nature Publishing Group
Citation
Lin, Shangchao, Seunghwa Ryu, Olena Tokareva, Greta Gronau, Matthew M. Jacobsen, Wenwen Huang, Daniel J. Rizzo, David Li, Cristian Staii, Nicola M. Pugno, Joyce Y. Wong, David L. Kaplan, and Markus J. Buehler. “Predictive Modelling-Based Design and Experiments for Synthesis and Spinning of Bioinspired Silk Fibres.” Nat Comms 6 (May 28, 2015): 6892.
Version
Final published version
Abstract
Scalable computational modelling tools are required to guide the rational design of complex hierarchical materials with predictable functions. Here, we utilize mesoscopic modelling, integrated with genetic block copolymer synthesis and bioinspired spinning process, to demonstrate de novo materials design that incorporates chemistry, processing and material characterization. We find that intermediate hydrophobic/hydrophilic block ratios observed in natural spider silks and longer chain lengths lead to outstanding silk fibre formation. This design by nature is based on the optimal combination of protein solubility, self-assembled aggregate size and polymer network topology. The original homogeneous network structure becomes heterogeneous after spinning, enhancing the anisotropic network connectivity along the shear flow direction. Extending beyond the classical polymer theory, with insights from the percolation network model, we illustrate the direct proportionality between network conductance and fibre Young's modulus. This integrated approach provides a general path towards de novo functional network materials with enhanced mechanical properties and beyond (optical, electrical or thermal) as we have experimentally verified.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Laboratory for Atomistic and Molecular Mechanics
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DOI of Published Version
https://doi.org/10.1038/ncomms7892
