Impedance Hyperbolicity in Inkjet‐Printed Graphene Nanocomposites: Tunable Capacitors for Advanced Devices. (11th January 2016) - Chiolerio, Alessandro; Porro, Samuele; Bocchini, Sergio
Abstract : An easy method for the fabrication of a completely tunable capacitor based on inkjet‐printed hybrid organic systems is here reported. The quantum relativistic properties of graphene‐induce electronic resonances between the polyaniline polymeric matrix and the graphene filler, with extremely long transfer rates. These events induce peculiar physical phenomena due to impedance hyperbolicity, such as voltage‐controlled phase shifting. The apparent capacitance is shown to diverge to infinity having a sign dependent on the frequency sweep direction. Hence devices may be geometrically tuned to operate with desired capacitance (either positive or negative) at the desired frequency. Among various possibilities of theoretical models that can explain the negative capacitance found, the standard space charge accumulation theory is considered and compared to other quantum relativity effects. The discussion of experimental results based on those theories suggests that a new paradigm is required for a comprehensive explanation of the observed phenomena, which include the observation of high‐frequency (MHz) negative capacitance, voltage‐controlled phase shifting, and diverging negative/positive capacitance in correspondence of the characteristic resonance frequency (kHz). The various possibilities of exploiting these peculiar properties in advanced electronic devices are also discussed. Abstract : A soluble polyaniline/graphene nanocomposite is developed to fabricate innovative devices simply by inkjet printing technology. The quantum relativistic properties of graphene are shown to induce electronic resonances between the polymeric matrix and the filler (as sketched in the artist's impression), with extremely long transfer rates. These events induce peculiar physical phenomena, such as voltage‐controlled phase shifting, shown in the graph.