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Self-Healing Textile: Enzyme Encapsulated Layer-by-Layer Structural Proteins.
Gaddes, David; Jung, Huihun; Pena-Francesch, Abdon; Dion, Genevieve; Tadigadapa, Srinivas; Dressick, Walter J; Demirel, Melik C.
Affiliation
  • Dion G; Westphal College of Media Arts and Design, Shima Seiki Haute Tech Lab at ExCITe, Drexel University , Philadelphia, Pennsylvania 19104, United States.
  • Dressick WJ; U.S. Naval Research Laboratory, Code 6910, 4555 Overlook Avenue, S.W., Washington, D.C. 20375, United States.
ACS Appl Mater Interfaces ; 8(31): 20371-8, 2016 Aug 10.
Article in En | MEDLINE | ID: mdl-27419265
ABSTRACT
Self-healing materials, which enable an autonomous repair response to damage, are highly desirable for the long-term reliability of woven or nonwoven textiles. Polyelectrolyte layer-by-layer (LbL) films are of considerable interest as self-healing coatings due to the mobility of the components comprising the film. In this work mechanically stable self-healing films were fabricated through construction of a polyelectrolyte LbL film containing squid ring teeth (SRT) proteins. SRTs are structural proteins with unique self-healing properties and high elastic modulus in both dry and wet conditions (>2 GPa) due to their semicrystalline architecture. We demonstrate LbL construction of multilayers containing native and recombinant SRT proteins capable of self-healing defects. Additionally, we show these films are capable of utilizing functional biomolecules by incorporating an enzyme into the SRT multilayer. Urease was chosen as a model enzyme of interest to test its activity via fluorescence assay. Successful construction of the SRT films demonstrates the use of mechanically stable self-healing coatings, which can incorporate biomolecules for more complex protective functionalities for advanced functional fabrics.
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Full text: 1 Database: MEDLINE Main subject: Textiles Type of study: Prognostic_studies Language: En Year: 2016 Type: Article

Full text: 1 Database: MEDLINE Main subject: Textiles Type of study: Prognostic_studies Language: En Year: 2016 Type: Article