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Reducing CO2 and Corrosion: Insights from Thermodynamic Descriptors Calculated with Density Functional Theory

Groenenboom, Mitchell (2018) Reducing CO2 and Corrosion: Insights from Thermodynamic Descriptors Calculated with Density Functional Theory. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Catalytic reaction mechanisms can be extremely complex, and it is difficult to determine all the factors that control reaction rates. Fortunately, complex chemical phenomena can frequently be described by thermodynamic properties (such as molecular pKas and reaction overpotentials) that correlate with catalytic reaction rates. While these properties can be difficult or time intensive to measure experimentally, they can be easily computed using Kohn-Sham density functional theory (KS-DFT).

We have developed a thermodynamic descriptor-based model that uses molecular pKas and redox potentials calculated with KS-DFT to predict the electrochemical conditions at which aromatic N-heterocycle (ANH) molecules could facilitate multi-proton and multi-electron reduction reactions. By automating this procedure using the ADF modeling suite, we can rapidly screen through potential catalysts with minimal user input. To establish a baseline procedure for studying the chemical reduction of CO2 via hydride transfers from ANH molecules, we characterized the chemical reduction of CO2 by hydride transfers from sodium borohydride. We located hydride transfer pathways with nudged elastic band calculations and obtained free energy barriers from potentials of mean force derived from constrained molecular dynamics simulations along the reaction pathways. These simulations provided reaction energetics at realistic operating conditions and highlighted the potential pitfalls of only studying reaction pathways at 0 K.

Cathodic reduction reactions can limit galvanic corrosion rates in atmospheric environments. To help guide the design of titanium alloys that resist galvanic corrosion, we used density functional theory to predict dopants that inhibit cathodic reduction reaction kinetics on oxide surfaces. We calculated overpotentials for the oxygen reduction reaction (ORR) occurring on metal dopants in an amorphous TiO2 surface. These overpotential trends successfully predicted six dopants that have been experimentally verified to inhibit ORR activity by up to 77% (Sn, Cr, Co, Al, Mn, and V). Next, we used this approach to study the native oxides of Ti-6Al-4V, a Ti alloy with improved corrosion resistance. We used Behler-Parrinello neural networks to create defective and amorphous surface models for TiAl2O5 (the oxide that forms on Ti-6Al-4V surfaces in addition to TiO2) and predicted how ORR activity was altered by different complex oxide surface morphologies.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Groenenboom, Mitchellmcg64@pitt.edumcg64
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairKeith, Johnjakeith@pitt.edujakeith
Committee MemberMpourmpakis, Giannisgmpourmp@pitt.edugmpourmp
Committee MemberWang, Guofengguw8@pitt.eduguw8
Committee MemberMckone, Jamesjmckone@pitt.edujmckone
Date: 20 June 2018
Date Type: Publication
Defense Date: 15 March 2018
Approval Date: 20 June 2018
Submission Date: 7 March 2018
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 236
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Chemical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Computational Chemistry, Density Functional Theory, Oxygen Reduction Reaction, Oxide, Carbon Dioxide Reduction, Aromatic N-Heterocycles, Neural Networks
Date Deposited: 20 Jun 2018 17:59
Last Modified: 20 Jun 2020 05:15


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