Abstract
Setae, fibrils located on a gecko's feet, have been an inspiration of synthetic dry microfibrillar adhesives in the last two decades for a wide range of applications due to unique properties: residue-free, repeatable, tunable, controllable and silent adhesion; self-cleaning; and breathability. However, designing dry fibrillar adhesives is limited by a template-based-design-approach using a pre-determined bioinspired T- or wedge-shaped mushroom tip. Here, a machine learning-based computational approach to optimize designs of adhesive fibrils is shown, exploring a much broader design space. A combination of Bayesian optimization and finite element methods creates novel optimal designs of adhesive fibrils, which are fabricated by two-photon-polymerization-based 3D microprinting and double-molding-based replication out of polydimethylsiloxane. Such optimal elastomeric fibril designs outperform previously proposed designs by maximum 77% in the experiments of dry adhesion performance on smooth surfaces. Furthermore, finite-element-analyses reveal that the adhesion of the fibrils is sensitive to the 3D fibril stem shape, tensile deformation, and fibril microfabrication limits, which contrast with the previous assumptions that mostly neglect the deformation of the fibril tip and stem, and focus only on the fibril tip geometry. The proposed computational fibril design could help design future optimal fibrils with less help from human intuition.
Highlights
This page was generated automatically upon download from the ETH Zurich Research Collection
Finite-element-analyses reveal that the adhesion of the fibrils is sensitive to the 3D fibril stem shape, tensile deformation, and fibril microfabrication limits, which contrast with the previous assumptions that mostly neglect the deformation of the fibril tip and stem, and focus only on the fibril tip geometry
The aspect ratio of the fibril is set to be 1:1 in order to compare the fibril with other fibril designs at the same aspect ratio, which removes the effect of having different aspect ratios on adhesion tests.[34]
Summary
The proposed computational single fibril design framework has two components: FEM-based adhesion mechanics simulation and Bayesian optimization[55,56,57,58,59,60] (Figure 1a). This approach would not be accurate when the fibril is deformed due to the elasticity of the material, where the stress profile drastically changes Soft elastomers, such as polydimethylsiloxane (PDMS) used in our experimental fibrils and in all previous T- and wedge-shaped mushroom fibril studies, do stretch significantly during the pull-off process resulting in the hyperelastic regime.[19] In contrast, our method estimates adhesion directly right before the detachment considering the hyperelastic body deformation of the fibrils. Once the optimal fibril design is obtained, such fibril is fabricated using two-photon polymerization (2PP) process and a subsequent double-molding-based replication technique (Figure 1c).[19] This fabrication method is versatile to create various shapes as well as allows the use of various elastomeric materials. Our simulation results show comparative adhesion tests among these two types, showing that adhesion energy optimized fibrils show higher adhesion energy while demonstrating compromised maximum adhesion forces
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
