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Atomic control of active-site ensembles in ordered alloys to enhance hydrogenation selectivity

Nature Chemistry volume 14, pages 523–529 (2022)Cite this article

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Abstract

Intermetallic compounds offer unique opportunities for atom-by-atom manipulation of catalytic ensembles through precise stoichiometric control. The (Pd, M, Zn) γ-brass phase enables the controlled synthesis of Pd–M–Pd catalytic sites (M = Zn, Pd, Cu, Ag and Au) isolated in an inert Zn matrix. These multi-atom heteronuclear active sites are catalytically distinct from Pd single atoms and fully coordinated Pd. Here we quantify the unexpectedly large effect that active-site composition (that is, identity of the M atom in Pd–M–Pd sites) has on ethylene selectivity during acetylene semihydrogenation. Subtle stoichiometric control demonstrates that Pd–Pd–Pd sites are active for ethylene hydrogenation, whereas Pd–Zn–Pd sites show no measurable ethylene-to-ethane conversion. Agreement between experimental and density-functional-theory-predicted activities and selectivities demonstrates precise control of Pd–M–Pd active-site composition. This work demonstrates that the diversity and well-defined structure of intermetallics can be used to design active sites assembled with atomic-level precision.

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Fig. 1: Hydrogenation of ethylene over γ-brass (Pd, Zn) catalysts.
Fig. 2: X-ray diffraction and DFT results show the location of Pd atoms in the bulk and surface of (Pd, Zn) γ-brass structures.
Fig. 3: Experimental study of acetylene hydrogenation on Pd–Zn intermetallics and Pd foil and DFT reaction energy diagrams for acetylene hydrogenation on Pd–M–Pd sites (M = Zn, Pd, Au, Ag, Cu).
Fig. 4: Competitive hydrogenation of 13C2-ethylene and 12C2-acetylene mixed feed over γ-brass [Pd, (Au), Zn] catalysts.
Fig. 5: Experimental and computational comparison of acetylene semihydrogenation on γ-brass (Pd, M, Zn) (M = Zn, Pd, Cu, Ag, Au).

Data availability

All data supporting the findings of this study are available within the paper and its Supplementary Information files. Source data are provided with this paper.

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All computational codes/algorithms utilized in this study were performed with either commercially available or open-source software.

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Acknowledgements

This work is supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Catalysis Division under award no. DE-SC0020147. A.D. acknowledges financial support from the US National Science Foundation (grant no. CBET–1748365). H.H. acknowledges training provided by the Computational Materials Education and Training (CoMET) National Science Foundation Research Traineeship (grant no. DGE-1449785). This work used the Extreme Science and Engineering Discovery Environment, which is supported by the National Science Foundation under grant no. ACI-1548562.

Author information

Author notes

  1. These authors contributed equally: Anish Dasgupta, Haoran He.

Authors and Affiliations

  1. Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA

    Anish Dasgupta, Haoran He, Eric K. Zimmerer, Michael J. Janik & Robert M. Rioux

  2. Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, USA

    Rushi Gong, Shun-Li Shang & Zi-Kui Liu

  3. ExxonMobil Research and Engineering, Annandale, NJ, USA

    Randall J. Meyer

  4. Department of Chemistry, The Pennsylvania State University, University Park, PA, USA

    Robert M. Rioux

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Contributions

A.D., E.K.Z. and R.M.R. planned, executed and analysed the experimental work. H.H. and M.J.J. performed and analysed the DFT computational work. R.G., S.-L.S. and Z.-K.L. performed and analysed the CEM work. R.J.M., M.J.J. and R.M.R. conceived the work and supervised the research. A.D. and H.H. wrote the paper with contributions from all authors.

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Correspondence to Michael J. Janik or Robert M. Rioux.

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Nature Chemistry thanks Siris Laursen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Projected density of states on surface Pd atoms.

Projected density of state of (a) surface Pd atoms of 3 × 3 5-layer Pd(111) slab with a d-band center of −1.36 eV, (b) surface Pd atoms of Pd8Zn44, with d-band center of −2.02 eV, (c) edge Pd of a Pd trimer site on the surface of Pd9Zn43, with d-band center of −1.96 eV (d) middle Pd of a Pd trimer site on the surface of Pd9Zn43, with d-band center of −1.99 eV (energies relative to Fermi levels).

Source data

Extended Data Fig. 2 SEM image of γ-Pd9Zn43 after re-annealing post ball milling at 500°C for 7 days.

The SEM image clearly shows particles have a platelet like morphology and a particle size ranging from 1 to ~10 micron.

Supplementary information

Supplementary Information

Supplementary Figs. 1–16, Tables 1–9 and Sections 1–9 providing additional information on methods and discussion of results.

Supplementary Data 1

A compressed zip file containing all optimized DFT structures (in Vienna Ab initio Simulation Package CONTCAR format).

Supplementary Data 2

A compressed zip file containing all structures, parameters and data for the CEM analysis.

Source data

Source Data Fig. 1

Excel file containing all data that appear in graphs within Fig. 1.

Source Data Fig. 2

Excel file containing all data that appear in graphs within Fig. 2.

Source Data Fig. 3

Excel file containing all data that appear in graphs within Fig. 3.

Source Data Fig. 4

Excel file containing all data that appear in graphs within Fig. 4.

Source Data Fig. 5

Excel file containing all data that appear in graphs within Fig. 5.

Source Data Extended Data Fig. 1

Excel file containing all data that appear in graphs within Extended Data Fig. 1.

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Dasgupta, A., He, H., Gong, R. et al. Atomic control of active-site ensembles in ordered alloys to enhance hydrogenation selectivity. Nat. Chem. 14, 523–529 (2022). https://doi.org/10.1038/s41557-021-00855-3

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