Machines of the Future will synergize biomimetic mechanics with artificial intelligence [1]. The ideal actuator should mimic muscle by being passively elastic while also efficiently converting potential energy into mechanical strokes [2]. The ideal body material should mimic skin by being simultaneously compliant and strong to accommodate actuator motion [3]. Further, both body and actuator should adaptively increase stiffness under stress to maintain performance at peak loads. One drawback of biological tissues, however, is that their softness requires water, which is an unreliable engineering material. To overcome this challenge, we have developed a materials design platform that controls mechanical properties of elastomers by engineering brush-like polymer networks [4]. Adaptable to any chemistry, the platform harnesses architectural parameters to encode tissue-mimetic softness, strain-stiffening, and extensibility within <i>single-chemical-component</i> systems. Applying this platform to archetypal poly(dimethylsiloxane) (PDMS), aka <i>silicone</i>, we have precise replicated the mechanical response of biological tissues including ultra-soft adipose, brain, and fetal membrane tissues without using solvent as a material “softener”.

Encoding mechanics of ultra-soft tissues in silicone

2019 Spring National Meeting

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