Dry self-cleaning surfaces inspired by Gecko has been widely applied in climbing robots, micromanipulator, astronaut gloves and surgical knives, etc. Most of the artificial biomimicry surfaces face the challenges of local structure failure and deformation, which limit their potential industrial applications. Gecko, however, has remarkable long fatigue life (up to 3 months) and dry self-cleaning property. Further study revealed the adhesive seta of the gecko toe feature pronounced gradients in the material composition and properties along their length. The Young’s modulus ranges from the lowest at the tips and the highest at the bottom and the maximum difference can be as large as 100 times. These gradients likely represent an evolutionary optimization, performance of the adhesive system by enabling effective adaptation to rough surfaces while simultaneously preventing lateral collapse of the setae. In this study, the objective was to further quantitatively analyze the gradient distribution characteristics of single gecko seta and initial mechanism. In addition, gradient gecko inspired surfaces was designed and synthesized via controlling the locations of magnetic nanoparticle inside the polymer bulking surface using external magnetic field and then reveal its controllable adhesion and tribological characteristics. The prepared gradient gecko inspired surfaces could be used to manipulator artificial micro particles and so on.
基于壁虎脚掌多级刚毛结构的仿壁虎干黏附表面设计,已在爬墙机器人,夹持器,宇航员手套及医疗手术刀等领域展现出价值。现有机械夹持器对微颗粒操控难以突破颗粒尺寸限制,且仿生表面在服役过程中极易产生局部断裂,变形导致黏附失效,严重影响使用寿命,限制了其在机械工程领域应用。本项目预研发现,壁虎脚掌刚毛可逆黏附与自清洁性质可长达三个月之久。究其原因,在于单根刚毛具备与现有仿生表面截然不同的杨氏模量分布,其单根刚毛根部到尖部杨氏模量最高相差100倍,这种刚度梯度分布可增强壁虎刚毛韧性,降低断裂及弯曲可能性,从而提高壁虎长效黏附寿命。本研究拟在此预研基础上,深入研究单根壁虎刚毛,刚毛尖端铲状触须梯度分布特性,构建理论模型揭示其内在耦合机理。并通过磁性纳米粒子结合高分子的方式,借助磁场调控粒子空间分布,设计制备出具有刚度梯度分布的仿生表面,揭示其可控黏附与摩擦学特性,并进一步探讨其在异型颗粒操控中的应用。
在基于刚度梯度分布的仿壁虎刚毛表面制备与异性颗粒操控研究项目的资助下,对壁虎刚毛进行了深度的研究,发现壁虎刚毛β-角蛋白的梯度分布规律并进行机理解释并制备出具有光热性能的仿生壁虎可控干黏附表面,并且实现了通过外界环境的改变对仿生表面进行“非接触式”黏附/脱附的可逆控制。此外,还研究了贻贝足丝高黏附性的原因,首次揭示了贻贝足丝角质层内部左旋多巴胺与铁离子动态敖合新机理及其角质层中铁离子存在多种价态与梯度分布特性,并引起了广泛关注。
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数据更新时间:2023-05-31
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