Programming gel automata shapes using DNA instructions
Ruohong Shi, Kuan-Lin Chen, Joshua Fern, Siming Deng, Yixin Liu, Dominic Scalise, Qi Huang, Noah J. Cowan, David H. Gracias, Rebecca Schulman
- 发表年份
- 2024
- 引用次数
- 8
- 访问权限
- 开放获取
摘要
The ability to transform matter between numerous physical states or shapes without wires or external devices is a major challenge for robotics and materials design. Organisms can transform their shapes using biomolecules carrying specific information and localize at sites where transitions occur. Here, we introduce gel automata, which likewise can transform between a large number of prescribed shapes in response to a combinatorial library of biomolecular instructions. Gel automata are centimeter-scale materials consisting of multiple micro-segments. A library of DNA activator sequences can each reversibly grow or shrink different micro-segments by polymerizing or depolymerizing within them. We develop DNA activator designs that maximize the extent of growth and shrinking, and a photolithography process for precisely fabricating gel automata with elaborate segmentation patterns. Guided by simulations of shape change and neural networks that evaluate gel automata designs, we create gel automata that reversibly transform between multiple, wholly distinct shapes: four different letters and every even or every odd numeral. The sequential and repeated metamorphosis of gel automata demonstrates how soft materials and robots can be digitally programmed and reprogrammed with information-bearing chemical signals. Shape transformation without the need for external devices or wires is desirable for soft robotics, but challenging to achieve. Here, the authors report the development of actuators capable of shape changes by polymerization and depolymerization of DNA activator sequences.
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