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

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Machine learning prediction and experimental verification of Pt-modified nitride catalysts for ethanol reforming with reduced precious metal loading
 
Denny, Steven R.; Lin, Zhexi; Porter, William N.; Artrith, Nong; Chen, Jingguang G.
(2022) Applied Catalysis B: Environmental, volume 312
(Article)
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 ... read more
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Keywords: Density-functional theory, Ethanol reforming, Machine learning, Temperature-programmed desorption, Transition metal nitrides, Taverne, Catalysis, General Environmental Science, Process Chemistry and Technology
DOI: https://doi.org/10.1016/j.apcatb.2022.121380
ISSN: 0926-3373
Publisher: Elsevier
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.
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