Springtail-inspired superomniphobic surface with extreme pressure resistance

Geun Tae Yun, Woo Bin Jung, Myung Seok Oh, Gyu Min Jang, Jieung Baek, Nam I. Kim, Sung Gap Im, Hee Tae Jung

Research output: Contribution to journalArticlepeer-review

114 Scopus citations


Both high static repellency and pressure resistance are critical to achieving a high-performance omniphobic surface. The cuticles of springtails have both of these features, which result from their hierarchical structure composed of primary doubly reentrant nanostructures on secondary microgrooves. Despite intensive efforts, none of the previous studies that were inspired by the springtail were able to simultaneously achieve both high static repellency and pressure resistance because of a general trade-off between these characteristics. We demonstrate for the first time a springtail-inspired superomniphobic surface displaying both features by fabricating a hierarchical system consisting of serif-T–shaped nanostructures on microscale wrinkles, overcoming previous limitations. Our biomimetic strategy yielded a surface showing high repellency to diverse liquids, from water to ethanol, with a contact angle above 150°. Simultaneously, the surface was able to endure extreme pressure resulting from the impacts of drops of water and of ethylene glycol with We > 200, and of ethanol with We ~ 53, which is the highest pressure resistance ever reported. Overall, the omniphobicity of our springtail-inspired fabricated system was found to be superior to that of the natural springtail cuticle itself.

Original languageEnglish
Article numbereaat4978
JournalScience Advances
Issue number8
StatePublished - 24 Aug 2018

Bibliographical note

Funding Information:
We acknowledge the support from KAIST Analysis Center for Research Advancement (KARA) This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT and Future Planning, Korea (NRF-2018R1A2B3008658).

Funding Information:
Acknowledgments: We acknowledge the support from KAIST Analysis Center for Research

Publisher Copyright:
Copyright © 2018 The Authors.


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