PhD Proposal by Julia Allen

Event Details
  • Date/Time:
    • Monday April 26, 2021
      11:00 am - 2:00 pm
  • Location: Atlanta, GA; REMOTE
  • Phone:
  • URL: Bluejeans
  • Email:
  • Fee(s):
  • Extras:
No contact information submitted.

Summary Sentence: Understanding Electrode-Electrolyte Interactions for Increased Energy Density in Supercapacitors for Aerospace Applications

Full Summary: No summary paragraph submitted.





Under the provisions of the regulations for the degree


on Monday, April 26, 2021

11:00 AM




BlueJeans Video Conferencing


will be held the 





Julia Allen


  “Understanding Electrode-Electrolyte Interactions for Increased Energy Density in Supercapacitors for Aerospace Applications”   


Committee Members: 


Prof. W. Jud Ready, Advisor, GTRI/MSE

Prof. Matthew McDowell, ME/MSE

Prof. Rampi Ramprasad, MSE

Prof. Paul Kohl, ChBE

Eric Fox, Ph.D., NASA-MSFC

Mr. Curtis Hill, NASA-MSFC




Current and future energy storage applications require devices with high energy density, high power density, and long cycle lives. Supercapacitors are a form of energy storage that currently have moderate energy density and power density and long cycle lives. The two main types of supercapacitors are electrochemical double layer capacitors, which use non-faradiac charge storage mechanisms, and pseudocapacitors, which use faradiac charge storage mechanisms. Hybrid supercapacitors combine these two charge storage mechanisms. For this project, samples of various pseudocapacitive coatings deposited on carbon nanotubes (CNT) using atomic layer deposition will be investigated. Coated CNTs make a promising electrode material because they benefit from the high surface area and stability of the carbon nanotubes and the pseudocapacitance of the coating material. The overall objective is to understand the interactions between ionic liquid electrolytes and the coatings resulting in higher energy densities. First, samples will be cycled ex situ and then observed with the transmission electron microscope (TEM). This will be compared to samples cycled with the same ionic liquids in situ with the same scan rate and voltage range. The variables considered will be coating type, ionic liquid type, coating thickness, and pore size.


This project focuses on the interactions between two pseudocapacitive materials and two ionic liquids.   Both TiO2 and MnO2 have been investigated for use in supercapacitors and have demonstrated pseudocapacitance.  In addition, thin coatings of Al2O3 has been shown to reduce electrode degradation and increase interactions between the electrode material and electrolyte.  To examine this, the proposed research will include samples both with and without Al2O3 layers.  One of the ionic liquids used in this experiment is 10 wt% lithium bis(trifluoromethylsulfonyl)imide (TFSI) in 1-butyl-1-methylpyrrolidiniumbis(trifluoromethylsulfonyl)imide (P14TFSI), which is known to work for in situ TEM experiments.  The second ionic liquid that will be included in the experiment will be determined using materials informatics. In this part of the project, a machine learning algorithm is used to predict the conductivity of ionic liquids. Using these predictions, a high conductivity ionic liquid will be selected.


Preliminary data shows that supercapacitors with bare CNT electrodes can store 3.39 ± 0.88 mF. Based on literature values, the TiO2-coated samples should have 3 to 4 times the capacitance, and the MnO2 samples should have a higher capacitance than that. The samples with additional Al2O3 coatings are expected to have about the same capacitance as the respective electrodes without the Al2O3 coatings but have a much lower ESR and higher power density.


This work will be a significant contribution to the understanding of pseudocapacitive energy storage mechanisms in supercapacitors.   Improving this understanding is a crucial step in the development of supercapacitors as high energy density, high power density energy storage devices. In addition, this work investigates the use of TiO2 and MnO2, which are promising alternatives to RuO2.  Demonstrating high performance with these materials will expand their use in future research. The development of a machine learning model for predicting ionic liquid conductivity is also a significant contribution to the field. In the future, it may even be possible to expand the model to predict additional properties. However, this project focused on predicting conductivity since it is challenging to find consistent conductivity information for ionic liquids.  This work should make it less costly to experiment with a wider range of ionic liquids in supercapacitors since low conductivity ionic liquids can be eliminated using the model. Finally, this research will demonstrate that in situ TEM can be used to observe supercapacitor electrode materials throughout charging and discharging.

Additional Information

In Campus Calendar

Graduate Studies

Invited Audience
Faculty/Staff, Public, Undergraduate students
Phd proposal
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Apr 12, 2021 - 11:56am
  • Last Updated: Apr 12, 2021 - 11:56am