THE SCHOOL OF MATERIALS SCIENCE AND ENGINEERING

GEORGIA INSTITUTE OF TECHNOLOGY

Under the provisions of the regulations for the degree

DOCTOR OF PHILOSOPHY

on Tuesday, February 15, 2022

12:30 PM
in MRDC 3515

& via

BlueJeans Video Conferencing

https://bluejeans.com/1749377931/

will be held the

DISSERTATION DEFENSE

for

Matthew Drexler

"Exploration of the Effects of Graphene and Carbon Nanotubes on the Structural and Catalytic Properties of Metallic Catalysts"

Committee Members:

Prof. Faisal Alamgir, Advisor, MSE

Prof. Wenshan Cai, ECE/MSE

Prof. Seung Soon Jang, MSE

Prof. Matthew McDowell, ME/MSE

Prof. Phillip First, PHYS

Raymond Unocic, Ph.D., MSE, Oak Ridge National Lab

Abstract: 

Fuel cells struggle to compete with other, similar technologies, but graphene and carbon nanotubes may be able to address their relatively poor durability as a corrosion resistant catalyst support. Further improvements will come from advancements in catalyst materials, but to realize these improvements, it is desirable to understand the impact graphene and carbon nanotubes will have on catalytic activity.

This work contains several studies that examine the effects of graphene and carbon nanotubes on the structural and catalytic properties of transition metals. The strain of metallic catalysts deposited on graphene via surface limited redox replacement was measured through X-ray absorption spectroscopy and scanning transmission electron microscopy. These found that while Au on graphene shows a gradual transition from nanoscale to bulk properties, the structure of Pd and Pt are pinned in a compressive and tensile strain, respectively. Catalytic activity was measured using the current response from the oxygen and carbon dioxide reduction reactions. It was found that graphene can have a beneficial effect on catalytic activity for Pt, Au, CuZn, and Pd, although this persists over a wider range of deposited amount for Pt and Au than CuZn and Pd. This was correlated to a compressive strain in Pt and Au, but the influence on CuZ and Pd was less clear. 

Code was developed to assist with interpreting images and electrochemical measurements, some of which were adapted to solve clustering problems. Their performance was found to be comparable to standard clustering algorithms.