Sticking to rough surfaces using functionally graded bio-inspired microfibres

Serdar Gorumlu, Burak Aksak

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Synthetic fibrillar adhesives inspired by nature, most commonly by the gecko lizard, have been shown to strongly and repeatedly attach to smooth surfaces. These adhesives, mostly of monolithic construction, perform on par with their natural analogues on smooth surfaces but exhibit far inferior adhesive performance on rough surfaces. In this paper, we report on the adhesive performance of functionally graded microfibrillar adhesives based on a microfibre with a divergent end and a thin soft distal layer on rough surfaces. Monolithic and functionally graded fibre arrays were fabricated from polyurethanes and their adhesive performance on surfaces of varying roughness were quantified from force–distance data obtained using a custom adhesion measurement system. Average pull-off stress declined significantly with increasing roughness for the monolithic fibre array, dropping from 77 kPa on the smoothest (54 nm RMS roughness) to 19 kPa on the roughest (408 nm RMS roughness) testing surface. In comparison, pull-off stresses of 81 kPa and 63 kPa were obtained on the same respective smooth and rough surfaces with a functionally graded fibre array, which represents a more than threefold increase in adhesion to the roughest adhering surface. These results show that functionally graded fibrillar adhesives perform similar on all the testing surfaces unlike monolithic arrays and show potential as repeatable and reusable rough surface adhesives.

Original languageEnglish
Article number161105
JournalRoyal Society Open Science
Volume4
Issue number6
DOIs
StatePublished - Jun 2017

Keywords

  • Adhesion
  • Bioinspired adhesives
  • Functionally graded microfibres
  • Gecko
  • Pull-off
  • Roughness

Fingerprint Dive into the research topics of 'Sticking to rough surfaces using functionally graded bio-inspired microfibres'. Together they form a unique fingerprint.

Cite this