Rapid and Cost-Effective Characterization of Graphene Powders Using Memristive Echo State Networks

Characterising 2D materials is essential for determining their suitability for various applications. However, existing characterisation methods are complex, expensive and not easily incorporated into fabrication processes. In this study, we propose a new instrumentation for measuring the conductivity, I-V curve, and impedance spectrum of powdered graphene samples. We also propose memristive echo-state networks for classifying between pure graphene, graphene oxide and reduced graphene oxide using these electrical measurements. We validate our approach using graphene powders, classifying them between graphene, graphene oxide and reduced graphene oxide. By modelling material characteristics based on electrical properties, we reveal hidden patterns, facilitating more scientific breakthroughs in 2D materials research and expediting their integration into industrial applications.