Biomimetic apposition compound eye fabricated using microfluidic-assisted 3D printing

Bo Dai, Liang Zhang, Chenglong Zhao, Hunter Bachman, Ryan Becker, John Mai, Ziao Jiao, Wei Li, Lulu Zheng, Xinjun Wan, Tony Jun Huang (), Songlin Zhuang and Dawei Zhang ()
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Bo Dai: University of Shanghai for Science and Technology
Liang Zhang: University of Shanghai for Science and Technology
Chenglong Zhao: University of Dayton
Hunter Bachman: Duke University
Ryan Becker: Duke University
John Mai: University of Southern California
Ziao Jiao: University of Shanghai for Science and Technology
Wei Li: University of Shanghai for Science and Technology
Lulu Zheng: University of Shanghai for Science and Technology
Xinjun Wan: University of Shanghai for Science and Technology
Tony Jun Huang: Duke University
Songlin Zhuang: University of Shanghai for Science and Technology
Dawei Zhang: University of Shanghai for Science and Technology

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract After half a billion years of evolution, arthropods have developed sophisticated compound eyes with extraordinary visual capabilities that have inspired the development of artificial compound eyes. However, the limited 2D nature of most traditional fabrication techniques makes it challenging to directly replicate these natural systems. Here, we present a biomimetic apposition compound eye fabricated using a microfluidic-assisted 3D-printing technique. Each microlens is connected to the bottom planar surface of the eye via intracorporal, zero-crosstalk refractive-index-matched waveguides to mimic the rhabdoms of a natural eye. Full-colour wide-angle panoramic views and position tracking of a point source are realized by placing the fabricated eye directly on top of a commercial imaging sensor. As a biomimetic analogue to naturally occurring compound eyes, the eye’s full-colour 3D to 2D mapping capability has the potential to enable a wide variety of applications from improving endoscopic imaging to enhancing machine vision for facilitating human–robot interactions.

Date: 2021
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DOI: 10.1038/s41467-021-26606-z

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