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Transparent and attachable ionic communicators based on self-cleanable triboelectric nanogenerators

Author

Listed:
  • Younghoon Lee

    (Seoul National University)

  • Seung Hee Cha

    (Seoul National University)

  • Yong-Woo Kim

    (Seoul National University)

  • Dukhyun Choi

    (Kyung Hee University, Seocheon-dong)

  • Jeong-Yun Sun

    (Seoul National University
    Seoul National University)

Abstract

Human–machine interfaces have benefited from the advent of wireless sensor networks and the internet of things, but rely on wearable/attachable electronics exhibiting stretchability, biocompatibility, and transmittance. Limited by weight and volume, wearable devices should be energy efficient and even self-powered. Here, we report practical approaches for obtaining a stably self-cleanable, transparent and attachable ionic communicator based on triboelectric nanogenerators. The communicator can be easily applied on human skin due to softness and chemically anchored robust layers. It functions as a means of real-time communication between humans and machines. Surface functionalization on the communicator by (heptadecafluoro-1,1,2,2-tetrahydrodecyl)trichlorosilane improves sensitivity and makes the communicator electrically and optically stable due to the self-cleaning effect without sacrificing transmittance. This research may benefit the potential development of attachable ionics, self-powered sensor networks, and monitoring systems for biomechanical motion.

Suggested Citation

  • Younghoon Lee & Seung Hee Cha & Yong-Woo Kim & Dukhyun Choi & Jeong-Yun Sun, 2018. "Transparent and attachable ionic communicators based on self-cleanable triboelectric nanogenerators," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-03954-x
    DOI: 10.1038/s41467-018-03954-x
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    Cited by:

    1. Yijia Lu & Han Tian & Jia Cheng & Fei Zhu & Bin Liu & Shanshan Wei & Linhong Ji & Zhong Lin Wang, 2022. "Decoding lip language using triboelectric sensors with deep learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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