Date of this Version
Fabio Parigi, "Fabrication and modeling of electrochemical double-layer capacitors using carbon nano-onion electrode structures," Ph.D. dissertation, University of Nebraska (July 12, 2013).
Electrochemical capacitors or ultracapacitors (UCs) that are commercially available today overcome battery limitations in terms of charging time (from tens of minutes to seconds) and limited lifetime (from a few thousand cycles up to more than one million) but still lack specific energy and energy density (2-5% of a lithium ion battery). The latest innovations in carbon nanomaterials, such as carbon nanotubes as an active electrode material for UCs, can provide up to five times as much energy and deliver up to seven times more power than today’s activated carbon electrodes. Further improvements in UC power density have been achieved by using state-of-the-art carbon nano-onions (CNOs) for ultracapacitor electrodes. CNO UCs could exhibit up to five times the power density of single-wall CNT UCs and could substantially contribute to reducing the size of an energy storage system as well as the volume and weight, thus improving device performance. This dissertation describes the fabrication of CNO electrodes as part of an UC device, the measurement and analysis of the new electrode’s performance as an energy storage component, and development of a new circuit model that accurately describes the CNO UC electrical behavior. The novel model is based on the impedance spectra of CNO UCs and cyclic voltammetry measurements. Further, the model was validated using experimental data and simulation. My original contributions are the fabrication process for reliable and repeatable electrode fabrication and the modeling of a carbon nano-onion ultracapacitor. The carbon nano-onion ultracapacitor model, composed of a resistor, an inductor, a capacitor (RLC), and a constant phase element (CPE), was developed along with a parameter extraction procedure for the benefit of other users. The new model developed, proved to be more accurate than previously reported UC models.
Advisor: Jerry Hudgins