Data driven analysis of fast oxide ion diffusion in solid oxide fuel cell cathodes

dc.contributor.advisorBenedek, Nicoleen
dc.contributor.committeeMemberYu, Guihuaen
dc.creatorMiller, Alexander Scoten
dc.creator.orcid0000-0002-3991-5262en
dc.date.accessioned2015-11-02T18:05:03Zen
dc.date.available2015-11-02T18:05:03Zen
dc.date.issued2015-08en
dc.date.submittedAugust 2015en
dc.date.updated2015-11-02T18:05:03Zen
dc.descriptiontexten
dc.description.abstractThe goal of this study was to determine whether atomic-scale features (related to composition and crystal structure) of perovskite and perovskite-related materials could be used to predict fast oxide ion diffusion for Solid Oxide Fuel Cell (SOFC) applications; materials that can be used as SOFC cathodes were a particular focus. One hundred and twenty six pairs of diffusion (D*) and surface exchange (k*) coefficients for a variety of materials were collected from literature sources published between 1991 and 2015. A website was created with these data for public viewing. Statistical tests revealed that diffusion measurements have significant differences at 400K, 700K, and 1000K when grouped according to material family and sample type. Models predicting diffusion rates were created from atomic-scale features at several temperatures between 400K and 1000K. Perovskite and double-perovskite models explained >85% of the variance in ln(D*k*) at 800K-1000K, meaning these models successfully predicted ln(D*k*) more than 85% of the time. These models explained 55%-75% of the variance at lower temperatures (400K-700K). Materials whose B-site cations had the highest electron affinities showed the fastest diffusion at all temperatures. Thus, these models suggest using B-site cations with high electron affinities (i.e. atoms that are easily reduced) may increase fuel cell performance, even at low and intermediate temperatures.en
dc.description.departmentMaterials Science and Engineeringen
dc.format.mimetypeapplication/pdfen
dc.identifierdoi:10.15781/T2NW32en
dc.identifier.urihttp://hdl.handle.net/2152/32132en
dc.language.isoenen
dc.subjectSolid oxide fuel cellsen
dc.subjectMaterials scienceen
dc.subjectStatisticsen
dc.subjectData miningen
dc.subjectMachine learningen
dc.titleData driven analysis of fast oxide ion diffusion in solid oxide fuel cell cathodesen
dc.typeThesisen
thesis.degree.departmentMaterials Science and Engineeringen
thesis.degree.disciplineMaterials science and engineeringen
thesis.degree.grantorThe University of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen

Access full-text files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
MILLER-THESIS-2015.pdf
Size:
1.88 MB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.85 KB
Format:
Plain Text
Description: