Reversible Dendritic-Crystal-Reinforced Polymer Gel for Bioinspired Adaptable Adhesive.
Songyan XiFeng TianGumi WeiXian HeYinghui ShangYe JuWenjun LiQinghua LuQigang WangPublished in: Advanced materials (Deerfield Beach, Fla.) (2021)
High-strength and reversible adhesion technology, which is a universal phenomenon in nature but remains challenging for artificial synthesis, is essential for the development of modern science. Existing adhesive designs without interface versatility hinder their application to arbitrary surfaces. Bioinspired by creeper suckers, a crystal-fiber reinforced polymer gel adhesive with ultrastrong adhesion strength and universal interface adaptability is creatively prepared via introducing a room-temperature crystallizable solvent into the polymer network. The gel adhesive formed by hydrogen bonding interaction between crystal fibers and polymer network can successfully realize over 9.82 MPa reversible adhesion strength for rough interface and 406.87 J m-2 peeling toughness for skin tissue. In situ anchoring is achieved for adapting to different geometrical surfaces. The adhesion performance can be significantly improved with the further increase of the interfacial roughness and hydrophilicity, whose dissipation mechanism is simulated by finite element analysis. The melting-crystallization equilibrium of the crystal fibers is proved by synchrotron radiation scattering. Accordingly, reversible phase-transition triggered by light and heat can realize the controlled adhere-detach recycle. Later adjustments to the monomers or crystals are expected to broaden its applications to various fields such as bioelectronics, electronic processing, and machine handling.
Keyphrases
- biofilm formation
- room temperature
- ionic liquid
- finite element analysis
- pseudomonas aeruginosa
- wound healing
- staphylococcus aureus
- candida albicans
- solid state
- escherichia coli
- cell migration
- public health
- high resolution
- molecular dynamics simulations
- hyaluronic acid
- cystic fibrosis
- heat stress
- machine learning
- soft tissue