journal article·2020-06

Mussel-mimetic polymer underwater adhesives with l-Dopa functionality: influencing adhesion properties and simplified operation procedures

Xiaoyong Zhang, Huihui Liu, Lipei Yue, Yongping Bai, Jinmei He

Journal of Materials Science· Volume 55· Issue 18
Publisher page

Abstract

When it comes to bonding in an aqueous environment, intertidal marine organisms such as mussels, sandcastle worms, barnacles and polychaetes are providing inspiration for the assembly of synthetic molecules into the polymeric adhesives. Although intense demands in various life or industrial applications and many mussel-inspired systems have been shown to develop coatings and hydrogels, exploring the mimetic mussel adhesive system through the control of composition and polymer molecular weight is demonstrated less often. In this report, we showed a simple approach to synthesize mussel-inspired adhesive, P(N-butyl acrylate-co-l-Dopa-methacrylate anhydride), which possesses characteristics of simple synthesis, easy-using, no extra-crosslinker need, and excellent adhesion behaviour on diverse substrates. Adhesion behaviour was examined using low- and high-surface-energy substrates (e.g. PTFE and steel, respectively). Furthermore, the fabricated adhesives exhibited excellent underwater adhesive behaviour for steel in various aqueous solutions, including ion ((IO4)− and Mn3+), acid and alkali solutions (pH = 4, 6.7 and 8, respectively). The outstanding fabricated adhesives take advantage of the catechol functionality and wettability of the resulting polymer. These insights should help us to design next-generation underwater adhesive systems as well as open up a number of future research designs for mussel-inspired adhesives with performance not accessed previously.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Polymer Surface Interaction Studies · Marine Biology and Environmental Chemistry · Advanced Polymer Synthesis and Characterization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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