Biological adhesion, in particular the mechanisms by which animals and plants 'stick' to surfaces, has been widely studied in recent years, and some of the structural principles have been successfully applied to bioinspired adhesives. However, modelling of adhesion, such as in single or multiple peeling theories, has in most cases been limited to ideal cases, and due consideration of the role of substrate geometry and mechanical properties has been limited. In this paper, we propose a numerical model to evaluate these effects, including substrate roughness, patterning, curvature, and deformability. The approach is validated by comparing its predictions with classical thin film peeling theoretical results, and is then used to predict the effects of substrate properties. These results can provide deeper insight into experiments, and the developed model can be a useful tool to design and optimize artificial adhesives with tailor-made characteristics.

The influence of substrate roughness, patterning, curvature, and compliance in peeling problems / Brely, L.; Bosia, F.; Pugno, N. M.. - In: BIOINSPIRATION & BIOMIMETICS. - ISSN 1748-3182. - ELETTRONICO. - 13:2(2018), p. 026004. [10.1088/1748-3190/aaa0e5]

The influence of substrate roughness, patterning, curvature, and compliance in peeling problems

Bosia F.;
2018

Abstract

Biological adhesion, in particular the mechanisms by which animals and plants 'stick' to surfaces, has been widely studied in recent years, and some of the structural principles have been successfully applied to bioinspired adhesives. However, modelling of adhesion, such as in single or multiple peeling theories, has in most cases been limited to ideal cases, and due consideration of the role of substrate geometry and mechanical properties has been limited. In this paper, we propose a numerical model to evaluate these effects, including substrate roughness, patterning, curvature, and deformability. The approach is validated by comparing its predictions with classical thin film peeling theoretical results, and is then used to predict the effects of substrate properties. These results can provide deeper insight into experiments, and the developed model can be a useful tool to design and optimize artificial adhesives with tailor-made characteristics.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2772635
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