Author ORCID Identifier
Semester
Summer
Date of Graduation
2024
Document Type
Dissertation
Degree Type
PhD
College
Statler College of Engineering and Mineral Resources
Department
Mechanical and Aerospace Engineering
Committee Chair
David Mebane
Committee Co-Chair
Debangsu Bhattacharyya
Committee Member
Debangsu Bhattacharyya
Committee Member
Fernando Lima
Committee Member
Kostas Sierros
Committee Member
Dong Ding
Abstract
In studying novel energy conversion and storage systems, such as high-temperature electrolysis, numerous underlying fundamental physical processes remain unclear or inadequately understood. Among these, the modeling and comprehension of surface reaction mechanisms, coupled with the intricate effects of space‑charge interfaces, remains an unclear and challenging area of research.
The work of this dissertation involves the development of a 2D finite element analysis model, leveraging the robust MOOSE framework from INL. This model, featuring inhomogeneous defect thermodynamics for near-surface chemistry, formulated through Poisson‑Cahn variational theory, has been exploited for studying the electrocatalytic reduction of CO2 on gadolinia doped ceria. The integration of Bayesian model calibration tools, such as Sequential Monte Carlo, enabled the development of a C++ open-source repository toolkit, known as ElePhaNT (Electrocatalytic Phase-Field modeling Numerical Tool), tailored for simulating operating electrolysis cells. Moreover, the ElePhaNT code facilitates the calibration of model parameters to experimental datasets, unraveling reaction pathways by proposing posterior distributions for model parameters, including defect interaction energies, gradient energy coefficients, and surface reaction rate parameters. This model-based Bayesian analysis sheds light on how the space‑charge affects the catalytic reaction. The integration of these methods accomplished a comparison of the numerical simulations with surface potential measurements collected via surface scanning Kelvin probe.
This work produced a compelling demonstration of the framework’s efficacy in the model parameter estimation for an operating cell at 500°C, working under various applied overpotentials. This showcases the capabilities of this developed framework in probing and better understanding the complex effects of defect interactions on space charge structure and the corresponding effects on the catalytic activity of doped ceria for CO2 electrolysis.
Recommended Citation
Mejia, Alejandro, "Investigation of Space Charge Effects on CO2 Electrocatalytic Reduction on Gd-Doped Ceria Via Scanning Kelvin Probe and Model-Based Bayesian Analysis" (2024). Graduate Theses, Dissertations, and Problem Reports. 12624.
https://researchrepository.wvu.edu/etd/12624
Included in
Artificial Intelligence and Robotics Commons, Catalysis and Reaction Engineering Commons, Data Science Commons, Energy Systems Commons, Materials Chemistry Commons, Other Materials Science and Engineering Commons, Partial Differential Equations Commons, Statistical Models Commons