New theoretical insights for oxygen electrocatalysis
Abstract/Contents
- Abstract
- Oxygen electrocatalysis, defined here as the study of electrocatalytic reactions consisting of O2, H2O, H2O2, has applications in a wide variety of industries from clean transportation to water purification. By utilizing electrochemical synthesis via either the 2-electron water oxidation reaction (2e-WOR) or the 2-electron oxygen reduction reaction (2e-ORR), we can produce hydrogen peroxide remotely for use as a water disinfectant in places with limited access to clean drinking water. By reducing oxygen to water in fuel cells, we can produce electricity to propel vehicles and power cities. In this work, we use a combination of density functional theory (DFT), microkinetic modelling, and ab initio molecular dynamics (AIMD), to develop new understanding of catalysts for these reactions.Specifically, we show that binding energies can help us understand and predict catalysts for the2e-WOR and 2e- and 4e-ORR, as well as predict more general electrochemical behavior of transition metal surfaces. First we use limiting potential analysis to predict ZnO as an active and selective catalyst for the 2e-WOR. We demonstrate that ZnO is not only the most active catalyst for this reaction known to date, but is also remarkably stable in reaction conditions. This result demonstrates the power of simple tools like limiting potential analysis in predicting catalysts for electrochemical reactions. Next, we demonstrate some of the limitations of limiting potential analysis in the ORR in two specific scenarios. First, we show that on transition metals, incorporating electric field ejects into microkinetic modeling allows us to accurately predict varying pH dependencies on several model catalysts. We use this newly developed model to create activity volcanoes which help us understand how and why electric field affects different catalysts in different ways in both the 2e- and 4e-ORR. We extend this understanding to transition metal oxides, where the intercept of the O-OH scaling relation is much higher than in metals. We show that by applying a similar microkinetic model to these new scaling relations, we can explain the relative inactivity of oxides in the 4e-ORR. Finally, we demonstrate that using similar principles as used in oxygen electrocatalysis, we can predict trends in work function reduction, and consequently, potential of zero charge (PZC), across transition metal surfaces. We connect work function reduction directly to water coverage using AIMD, and show that even simple vacuum binding energy calculations have predictive power for PZC. Throughout this thesis we attempt to show that by using simple descriptors to explain binding energies, reaction rates, and other electrochemical properties, we are not only able to predict new catalysts, but also better understand the physical phenomena which govern the way existing catalysts behave.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Kelly, Sara Rois |
|---|---|
| Degree supervisor | Bent, Stacey |
| Degree supervisor | Noerskov, Jens |
| Thesis advisor | Bent, Stacey |
| Thesis advisor | Noerskov, Jens |
| Thesis advisor | Jaramillo, Thomas Francisco |
| Degree committee member | Jaramillo, Thomas Francisco |
| Associated with | Stanford University, Department of Chemical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Sara R. Kelly. |
|---|---|
| Note | Submitted to the Department of Chemical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/th042sw3977 |
Access conditions
- Copyright
- © 2021 by Sara Rois Kelly
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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