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

Kaiyang Tan, Mudit Dixit, James Dean, Giannis Mpourmpakis

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Single-atom catalysts (SACs), containing under-coordinated single metal atoms bound to the surface of supports, are promising heterogeneous catalysts due to their intrinsic catalytic properties and efficient utilization of noble metal atoms. However, SAC stability under catalytic operation is questioned due to the tendency of metals to sinter (aggregation). Herein, we perform density functional theory (DFT) calculations to investigate the metal-support interactions (MSIs) of a series of transition-metal atoms supported on three common oxide supports (Î-Al2O3, MgO, and MgAl2O4). Moreover, utilizing the DFT results and genetic programming, we develop a predictive model for the strength of MSIs using simple properties of both the SACs and supports. Finally, we introduce criteria for the synthetic accessibility of SACs based on thermodynamic arguments. Our computational work can guide experiments by identifying combinations of metals and oxides that can potentially lead to highly stable (and catalytically durable) SACs.

Original languageEnglish
JournalIndustrial and Engineering Chemistry Research
DOIs
StatePublished - 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
Copyright © 2019 American Chemical Society.

Funding

This material is based upon work supported by the National Science Foundation under Grant No. 1634880 (CMMI). Computational support was provided by the Center for Research Computing at the University of Pittsburgh, and the Extreme Science and Engineering Discovery Environment, which is supported by the NSF (ACI-1053575). This material is based upon work supported by the National Science Foundation under Grant No. 1634880 (CMMI). Computational support was provided by the Center for Research Computing at the University of Pittsburgh, and the Extreme Science and Engineering Discovery Environment which is supported by the NSF (ACI-1053575)

FundersFunder number
Center for Research Computing
Extreme Science and Engineering Discovery Environment
National Science Foundation1634880, CMMI, ACI-1053575
Division of Civil, Mechanical and Manufacturing Innovation
National Sleep Foundation
University of Pittsburgh

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