Developing Graphene-based Conductive Textiles Using Different Coating Methods

In this research, a series of graphene-based conductive textiles is developed by three different coating methods, including dip-coating (D), airbrushing (A), and filtration (F). The cellulose substrate consists of a blend of cotton and rayon fabric, and the coating formulation is based on a mixture of graphene powder as a conductive filler, polyurethane (PU) as a binder, and poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) as a thermoplastic co-binder. The thermogravimetric analysis (TGA) results are used to prove an enhancement in the thermal stability of the coated fabrics. The graphene content of the coated samples is also estimated from the char residue at 800 degrees C of the TGA profiles. The graphene-based coating converts the water-adsorbing cellulose fabric to a hydrophobic surface as the water contact angle raises from 0 degrees to more than 107 degrees after coating. The mechanical properties of the plain cellulose fabric enhance considerably in terms of tensile strength and tensile modulus, where the highest improvement is seen in the Dip-coating method, with an 89% increase in tensile strength compared to cellulose fabric. The graphene-based coating developed in this work enhances the physical, thermal, mechanical, and conductivity properties of the plain cellulose substrate. The resulting coated fabrics can be potentially used in wearable smart electronic textiles. In this study, graphene-based conductive textiles are developed through three distinct coating methods: dip-coating, airbrushing, and filtration. The cellulose fabric is treated with a formulation comprising graphene, polyurethane, and poly (vinylidene fluoride-co-hexafluoropropylene). These coatings exhibit improved thermal stability, enhanced electrical conductivity, hydrophobic transformation, and substantial mechanical property enhancements. These findings hold promise for the application of these coated fabrics in wearable smart electronic textiles.image