Mimicking the mechanical behavior of biological tissues is crucial for tissue engineering, medical implants, and wearable devices. Many biological tissues—such as lung, skin and artery tissues—are supersoft at small deformation (Young’s Modulus <i>E<sub>0</sub></i> &lt; 10 kPa) and stiffen with increasing deformation. This unique combination of mechanical softness and nonlinear elasticity cannot be easily duplicated by conventional synthetic elastomers composed of linear polymers. Graft polymers, consisting of linear backbones densely grafted with short side chains, endow polymeric materials with two unique features owing to their special architecture: (i) the dilution of chain entanglement by side chains, and (ii) the stretching of the backbone due to steric repulsions of side chains. These distinct features pave the way for the design of supersoft elastomers with controllable nonlinear elasticity. Through a combination of theoretical calculations, coarse-grained molecular dynamics simulations and experiments, we have developed a general material design strategy which allows to encode the nonlinear mechanical response of soft materials through architecture of graft polymer networks. Such networks can be prepared either by chemical crosslinking of bottlebrush macromolecules or self-assembly of linear-bottlebrush-linear triblock copolymers. The mechanical properties of the resultant materials is controlled by architectural parameters, including network strand length, side chain length, grafting density, as well as the chain length of blocks in triblock copolymers. By utilizing this design-by-architecture strategy, replicas of jellyfish, artery and skin tissues based on bottlebrush poly(dimethylsiloxane) (bbPDMS) were synthesized to verify the materials design approach.

Guided design of strain-adaptive polymer networks

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.