Abstract

Ceria nanostructures have been utilized for various engineering applications due to their unique defect structure, enabling them to have regenerative redox properties. The ability to shuttle between Ce3+ and Ce4+ oxidation states extends to unique properties. Defect level in ceria is usually expressed in the Ce3+/Ce4+ ratio. It can be achieved by varying the size(0 to 3D), morphologies, dopants, hetero-nanostructures, and external stimuli. However, it is still a challenge to have precise control over defect structures in the ceria lattice. The current study aims to explore the thin film deposition techniques for cerium oxide using ALD (Atomic layer deposition) and SILAR (Successive ionic layer adsorption and reaction) methods with precise defect engineering in the ceria thin films. Beginning with ALD, we have presented an in-situ ellipsometry-aided rapid ALD process development and optimization technique. Compared to the traditional ex-situ approach, the in-situ ellipsometry-aided method has shown a dramatic decrease in iterations and time needed for optimization. We further utilized the in-situ ellipsometry data for training an efficient Machine learning algorithm (ML) to predict defect levels in real-time. We have observed that precise thin film thickness control yields a discrete range of defect levels. We have made composite thin films to expand further the ability to obtain higher defect concentration and highly reducible oxides. Our study has demonstrated a temperature-controlled defect engineering in Ceria nanostructures using thin filmVO2-CeOx bilayers. VO2 has a unique reversible low-temperature phase transformation from monoclinic to tetragonal, and it has been utilized indirectly to control defect levels in the reduced ceria film with a remarkable ~85% Ce3+ states. Furthermore, we have developed a unique, more frugal method of ceria thin film deposition using SILAR without any post-deposition annealing and explored it for antimicrobial applications. We have extended the defect engineering principle to other oxides (e.g., ZnO) using ALD for biomedical applications.

Notes

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Graduation Date

2022

Semester

Fall

Advisor

Seal, Sudipta

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Materials Science and Engineering

Degree Program

Materials Science and Engineering

Identifier

CFE0009831; DP0027772

URL

https://purls.library.ucf.edu/go/DP0027772

Language

English

Release Date

June 2024

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

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