Machine learning prediction and experimental verification of Pt-modified nitride catalysts for ethanol reforming with reduced precious metal loading

Steven R. Denny, Zhexi Lin, William N. Porter, Nong Artrith*, Jingguang G. Chen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Ethanol is the smallest molecule containing C–O, C–C, C–H, and O–H bonds present in biomass-derived oxygenates. The development of inexpensive and selective catalysts for ethanol reforming is important towards the renewable generation of hydrogen from biomass. Transition metal nitrides (TMN) are interesting catalyst support materials that can effectively reduce precious metal loading for the catalysis of ethanol and other oxygenates. Herein theoretical and experimental methods were used to probe platinum-modified molybdenum nitride (Pt/Mo2N) surfaces for ethanol reforming. Computations using density-functional theory and machine learning predicted monolayer Pt/Mo2N to be highly active and selective for ethanol reforming. Temperature-programmed desorption (TPD) experiments verified that ethanol primarily underwent decomposition on Mo2N, and the reaction pathway shifted to reforming on Pt/Mo2N surfaces. High-resolution electron energy loss spectroscopy (HREELS) results further indicated that while Mo2N decomposed the ethoxy intermediate by cleaving C–C, C–O, and C–H bonds, Pt-modification preserved the C–O bond, resulting in ethanol reforming.

Original languageEnglish
Article number121380
Number of pages8
JournalApplied Catalysis B: Environmental
Volume312
DOIs
Publication statusPublished - 5 Sept 2022

Bibliographical note

Funding Information:
This research was supported by the US Department of Energy, Office of Basic Energy Sciences, Catalysis Science Program (Grant No. DE-FG02–13ER16381 ). The authors would also like to thank Dr. Brian M. Tackett for the synthesis of the Mo 2 N thin films. The DFT calculations were carried out in part at the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility, and used resource at the Scientific Data and Computing Center of the Computational Science Initiative, at Brookhaven National Laboratory under Contract No. DE-SC0012704. Some DFT calculations and the machine-learning model construction also made use of the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575 (allocation no. DMR14005). We also acknowledge Dr. Zhenhua Xie for valuable discussion. The VESTA program used for model visualization is also gratefully acknowledged.

Publisher Copyright:
© 2022 Elsevier B.V.

Funding

This research was supported by the US Department of Energy, Office of Basic Energy Sciences, Catalysis Science Program (Grant No. DE-FG02–13ER16381 ). The authors would also like to thank Dr. Brian M. Tackett for the synthesis of the Mo 2 N thin films. The DFT calculations were carried out in part at the Center for Functional Nanomaterials, which is a U.S. DOE Office of Science Facility, and used resource at the Scientific Data and Computing Center of the Computational Science Initiative, at Brookhaven National Laboratory under Contract No. DE-SC0012704. Some DFT calculations and the machine-learning model construction also made use of the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575 (allocation no. DMR14005). We also acknowledge Dr. Zhenhua Xie for valuable discussion. The VESTA program used for model visualization is also gratefully acknowledged.

Keywords

  • Density-functional theory
  • Ethanol reforming
  • Machine learning
  • Temperature-programmed desorption
  • Transition metal nitrides

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