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Gecko-Inspired Intelligent Adhesive Structures for Rough Surfaces. | LitMetric

Biomimetic dry adhesive structures, inspired by geckos' climbing abilities, have attracted research attention in recent years. However, achieving superior adhesion on a rough surface remains an important challenge, which limits practical applications. Conventional bionic adhesion methods perform well on smooth surfaces, but adhesion strength drastically decreases on rough surfaces due to the reduced contact area. Generally, various adhesive structures have been proposed to increase the contact area without assessing adhesion states, against obtaining good performance on rough surfaces. If an intelligent adhesive approach could be introduced on rough surfaces, it would be beneficial for promoting the development of gecko-inspired adhesives. However, existing adhesive structures with the sensing function usually utilize the adhesive function to support the sensing function, i.e., a sensor with an adhesive function; for other few structures, the sensing function supports adhesion, but they do not focus on improving adhesion performance on rough surfaces. Inspired by the synergistic effect of a kinematic system during the crawling process of geckos, this study proposes an intelligent adhesive structure for rough surfaces. The proposed structure combines a hierarchical bionic dry adhesive structure based on gecko paw microhairs with a flexible capacitive sensor unit. Experimental observations and analytical modeling demonstrate that incorporating mushroom-shaped bionic dry adhesive structures with inclined support micropillars can reduce interface contact stiffness, notably enhancing adhesion on rough surfaces while allowing real-time monitoring of contact states. Moreover, this innovative smart adhesive structure facilitates morphology sensing of contact interfaces, presenting potential advancements in bionic adhesion for morphology sensing applications.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11850978PMC
http://dx.doi.org/10.34133/research.0630DOI Listing

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