Potential application of carbon activated fibers from pan ( polyacrylonitrile ) textile fibers impregnated with silver for antimicrobial purposes

• Grant number: 16/08828-6
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: October 01, 2016
• End date: June 30, 2018
• Field of knowledge: Biological Sciences - Microbiology - Applied Microbiology
• Principal Investigator: Gabriela de Morais Gouvêa Lima
• Grantee: Gabriela dos Santos Simões
• Host Institution: Instituto de Ciência e Tecnologia (ICT). Universidade Estadual Paulista (UNESP). Campus de São José dos Campos. São José dos Campos , SP, Brazil

Abstract

Activated carbon fibers are active materials that, unlike traditional coals are wires that can be transformed into any textile artifact. They have a diameter between 10 and 15 µm and are resilient, light and have chemical resistance. Through pyrolytic process followed by thermal oxidation, the carbon fibers are produced and converted into the activated carbon fibers (ACF), with a high fraction of microporosity and adsorptive capacity. The FCA impregnated with silver has demonstrated antibacterial power in in vitro assays reported in the literature. Among the species of interest for studies involving these materials are those involved in the contamination in hospitals, where it is very important to control microorganisms. The aim of this study is to evaluate the antimicrobial potential of the activated carbon fiber fabric. The ability of the fabric to inhibit the formation of biofilm microorganisms involved in nosocomial infections will be studied. It will be used fabric with pure carbon fiber and impregnated with silver in concentrations of 24, 12, 6 and 3 g/cm2. Also, the FCA fabric will be tested in a model of chronic wounds (in vitro) and their cytotoxicity is evaluated. Finally, the reuse capacity of the material is evaluated. The results will be tabulated and analyzed later using GraphPad Prim, version 6.0. The statistical method to be used will be chosen after checking the distribution of data using the Kolmogorov-Smirnov test.