Photo-Detachable Self-Cleaning Surfaces Inspired by Gecko Toepads.

Journal: Langmuir : the ACS journal of surfaces and colloids
PMID:

Abstract

Strong, reversible, and self-cleaning adhesion in the toe pads of geckos allow the lizards to climb on a variety of vertical and inverted surfaces, regardless of the surface conditions, whether hydrophobic or hydrophilic, smooth or tough, wet or dry, clean or dirty. Development of synthetic gecko-inspired surfaces has drawn a great attention over the past two decades. Despite many external-stimuli responsive mechanisms (i.e., thermal, electrical, magnetic) have been successfully demonstrated, smart adhesives controlled by light signals still substantially lag behind. Here, in this report, we integrate tetramethylpiperidinyloxyl (TEMPO)-doped polydopamine (PDA), namely, TDPDA, with PDMS micropillars using a template-assisted casting method, to achieve both improved adhesion and self-cleaning performances. To the best of our knowledge, this is the first report on PDA being used as a doping nanoparticle in bioinspired adhesive surfaces to achieve highly efficient self-cleaning controllable by light signals. Notably, the adhesion of the 5% TDPDA-PDMS sample is ∼688.75% higher than that of the pure PDMS at the individual pillar level, which helps to explain the highly efficient self-cleaning mechanism. The sample surfaces (named TDPDA-PDMS) can efficiently absorb 808 nm wavelength of light and heat up from 25 °C to 80.9 °C in 3 min with NIR irradiation. The temperature rise causes significant reduction of adhesion, which results in outstanding self-cleaning rate of up to 55.8% within five steps. The exploration of the photoenabled switching mechanism with outstanding sensitivity may bring the biomimetic smart surfaces into a new dimension, rendering varied applications, e.g., in miniaturized climbing robot, artificial intelligence programmable manipulation/assembly/filtration, active self-cleaning solar panels, including high output sensors and devices in many engineering and biomedical frontiers.

Authors

  • Xiaohang Luo
    State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Beijing), Beijing 102249, China.
  • Xiaoxiao Dong
    College of Mechanical Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, China.
  • Yanguang Hou
    College of Mechanical Transportation Engineering, China University of Petroleum (Beijing), Beijing 102249, China.
  • LiFu Zhang
    Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China.
  • Penghao Zhang
    Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States.
  • Jiaye Cai
    State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Beijing), Beijing 102249, China.
  • Ming Zhao
    School of Computer Science and Engineering, Central South University, Changsha, 410000, China.
  • Melvin A Ramos
    Department of Mechanical Engineering, California State University, Los Angeles, California 90032, United States.
  • Travis Shihao Hu
    Department of Mechanical Engineering, California State University, Los Angeles, California 90032, United States.
  • Hong Zhao
    Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China.
  • Quan Xu
    State Key Laboratory of Stress Cell Biology, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, P.R. China.