Computational insight into the selectivity of γ-valerolactone hydrodeoxygenation over Rh(111) and Ru(0001)
Abstract
The observed difference in the selectivity towards alkane, ketone, and alcohol hydrodeoxygenation products over Ru and Rh catalysts is explored using a combination of density functional theory and microkinetics. Using
γ-valerolactone as a model compound, we investigate the reaction mechanism in order to identify selectivity determining species. The effect of the coadsorbed water molecule as well as the higher adsorbate surface coverage on reaction barriers and energies is explored as well. The performed calculations suggest that the desired alkane product is formed from a ketone intermediate on Ru, and through both ketone and alcohol on Rh, although the selectivity towars alkane on Rh is much lower than on Ru.
Main Authors
Format
Articles
Research article
Published
2025
Series
Subjects
Publication in research information system
Publisher
Elsevier
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202411087070Use this for linking
Review status
Peer reviewed
ISSN
0039-6028
DOI
https://doi.org/10.1016/j.susc.2024.122624
Language
English
Published in
Surface Science
Citation
- Kauppinen, M. M., Szlapa, E. N., González, E. J. L., Puurunen, R. L., & Honkala, K. (2025). Computational insight into the selectivity of γ-valerolactone hydrodeoxygenation over Rh(111) and Ru(0001). Surface Science, 751, Article 122624. https://doi.org/10.1016/j.susc.2024.122624
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Funder(s)
Research Council of Finland
Funding program(s)
Academy Programme, AoF
Akatemiaohjelma, SA
Additional information about funding
The computational work was funded by Research Council of Finland (307623) and University of Jyväskylä. The electronic structure calculations were made possible by the computational resources provided by the CSC — IT Center for Science, Espoo, Finland (https://www.csc.fi/en/) and FGCI. The experiments on which this work is based (Supporting Information) were funded by Neste Corporation. J. L. G. E. acknowledges funding from Fortum Foundation (number 201800142) and from the Finnish Foundation for Technology Promotion (number 6712).
Copyright© 2024 The Authors. Published by Elsevier B.V.