Electrode materials are the most important factors to verify the properties of the electrochemical supercapacitor. In this paper, the storage principles and characteristics of electrode materials, including carbon-based materials, transition metal oxides and conducting polymers for supercapacitors are depicted in detail. Other factors such as electrode separator and electrolyte are briefly investigated. Recently, several works are conducted on application of multiwalled carbon nanotubes (MWCNTs) and MWCNTs-based electrode materials for supercapacitors. MWCNTs serve in experimental supercapacitor electrode materials result in specific capacitance (SC) value as high as 135 Fg-1. Addition of pseudocapacitive materials such as transition metal oxides and conducting polymers in the MWCNTs results in electrochemical performance improvement (higher capacitance and conductivity). The nanocomposites of MWCNTs and pseudocapacitive materials are the most promising electrode materials for supercapacitors because of their good electrical conductivity, low cost and high mass density.

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A composite of two conducting polymers, polyaniline and polypyrrole, could result in much improved physical properties with respect to their applications in pH electrodes and conducting paints/coatings. However, the composite has time dependant properties that made the use of dynamic computational algorithms necessary to control its behaviour. We used a sandwich-modified electrode as a pH-sensing electrode, which was integrated with a pre-trained computer for data processing and knowledge discovery. Conducting polypyrrole/polyaniline colloidal composites have been also prepared by deposition of the mixture solution of polypyrrole and polyaniline at negative potentials. Different substrates were dipped in the solution containing the mixture together with an acrylic latex and after drying the adhesive strength and electroactivity of the paint were studied. Several colloidal composite based coatings were investigated. At last a computer facilitated with powerful artificial ncural network based algorithms classified the paints.

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