MULTISCALE COMPUTATIONAL MODELING OF NANOSTRUCTURE AND TRANSPORT IN POLYMER ELECTROLYTE MEMBRANE FUEL CELLS

Polymer electrolyte membrane fuel cells (PEMFCs) are predicted to revolutionize energy conversion for transportation due to a multitude of advantages over conventional methods. However, due to their lack of resillience to adverse conditions, they are not as widespread as other portable energy technologies. In order to render PEMFCs suitable for extensive use, we must explore methods to enhance their performance such as improving conductivity in extreme conditions and lengthening their lifetime. This thesis aims to address the issue of PEMFC versatility by using multiscale computational simulations to provide fundamental understanding of PEM mechanisms and suggest superior chemistries for PEM components. Specifically, Aim 1 aids in the design of PEMs resistant to hot or dry conditions by offering novel insight into how PEM nanostructure influences proton transport in low-humidity conditions. Aim 2 involves the elucidation of the CeO2 radical scavenging mechanisms in PEMs, as well as the suggestion of an improved CeO2 surface chemistry. Finally, Aim 3 expands upon our first aim by offering an algorithm which accurately predicts pKa (and, consequently, approximates performance) of acids relevant to PEMs, streamlining the design of novel, durable chemistries.

Level thesis:degree_level

Doctoral

Department dc:contributor.department

Chemical and Biomolecular Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Lawler, Robin May

Advisors dc:contributor.advisor

• Jones, Christopher W.
• Jang, Seung Soon

Committee members dc:contributor.committeemember

• Kohl, Paul
• Fuller, Thomas
• Sholl, David

• polymer electrolyte membrane fuel cells
• DFT
• machine learning
• molecular dynamics

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1853/66187

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/66187