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Predicting Metal-Support Interactions in Oxide-Supported Single-Atom Catalysts

Tan, Kaiyang (2019) Predicting Metal-Support Interactions in Oxide-Supported Single-Atom Catalysts. Master's Thesis, University of Pittsburgh. (Unpublished)

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Abstract

Single-Atom Catalysts (SACs), containing under-coordinated single metal atoms bound on the surface of supports, have recently emerged as promising heterogeneous catalysts due to their intrinsic catalytic properties and efficient utilization (high dispersion) of noble metal atoms. Strong Metal-Support Interactions (MSIs) present in these catalysts can dictate the physicochemical properties, activity, and stability of SACs, which are significantly different from the conventional supported nanoscale metal catalysts. Although SACs exhibit unique catalytic behavior, their stability under catalytic operation is questioned due to the tendency of metals to sinter (aggregation). An optimal MSI can avoid metal aggregation and tune the stability and catalytic activity of SACs. Herein, we investigate MSIs of a series of transition metal atoms (Au, Cu, Ag, Pt, Pd, Ni, Rh, and Ir) supported on low-index surface facets of three oxides (γ-Al2O3, MgO, and MgAl2O4) that are commonly used as supports in catalysis. By investigating the adsorption of the metals at different binding sites across the oxide surfaces, we identify the best descriptors of MSI to be the gas-phase metal-oxygen binding energy and the oxide support’s band gap. Moreover, utilizing the results of Density-Functional Theory (DFT) calculations and genetic programming, we develop a predictive model for the strength of MSI (which we quantify as adsorption energy) using simple properties of the SAC and the support. Finally, we introduce some guidelines for the synthetic accessibility of a series of SACs based on thermodynamic arguments. Our computational
work can guide experimentation by identifying combinations of metals and oxides that can potentially lead to highly stable (and catalytically durable) SACs.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Tan, KaiyangKAT129@pitt.eduKAT1290000-0003-3173-806X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorMpourmpakis, GiannisGMPOURMP@pitt.eduGMPOURMP
Committee MemberVeser, Goetzgveser@pitt.edugveser
Committee MemberJohnson, J Karlkarlj@pitt.edukarlj
Date: 9 September 2019
Date Type: Publication
Defense Date: 10 July 2019
Approval Date: 9 September 2019
Submission Date: 5 July 2019
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 94
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Chemical Engineering
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Single-Atom Catalyst, Metal-Support Interactions, Density Functional Theory, Machine Learning
Date Deposited: 09 Sep 2019 19:12
Last Modified: 09 Sep 2019 19:12
URI: http://d-scholarship.pitt.edu/id/eprint/37031

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