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Speciation of Ru Molecular Complexes in a Homogeneous Catalytic System: Fingerprint XANES Analysis Guided by Machine Learning

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Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Phys. Chem. C 2021, 125, 50, 27844-27852
    C: Physical Properties of Materials and Interfaces

    Speciation of Ru Molecular Complexes in a Homogeneous Catalytic System: Fingerprint XANES Analysis Guided by Machine Learning
    Click to copy article linkArticle link copied!

    • E. G. Kozyr*
      E. G. Kozyr
      The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
      *Email: [email protected]
      More by E. G. Kozyr
      Orcidhttps://orcid.org/0000-0001-9308-5338
    • A. L. Bugaev
      A. L. Bugaev
      The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
      More by A. L. Bugaev
      Orcidhttps://orcid.org/0000-0001-8273-2560
    • S. A. Guda
      S. A. Guda
      The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
      Institute of Mathematics, Mechanics and Computer Science, Southern Federal University, Milchakova 8a, 344090 Rostov-on-Don, Russia
      More by S. A. Guda
      Orcidhttps://orcid.org/0000-0002-2398-1847
    • A. A. Guda
      A. A. Guda
      The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
      More by A. A. Guda
      Orcidhttps://orcid.org/0000-0002-6941-4987
    • K. A. Lomachenko
      K. A. Lomachenko
      European Synchrotron Radiation Facility, 71 Avenue des Martyrs, CS 40220, 38043 Grenoble Cedex 9, France
      More by K. A. Lomachenko
      Orcidhttps://orcid.org/0000-0003-0238-1719
    • K. Janssens
      K. Janssens
      Centre for Membrane Separations, Adsorption, Catalysis and Spectroscopy for Sustainable Solutions (cMACS), Department of Microbial and Molecular Systems (M2S), KU Leuven, Celestijnenlaan 200F, Post Box 2454, 3001 Leuven, Belgium
      More by K. Janssens
      Orcidhttps://orcid.org/0000-0002-7194-2623
    • S. Smolders
      S. Smolders
      Centre for Membrane Separations, Adsorption, Catalysis and Spectroscopy for Sustainable Solutions (cMACS), Department of Microbial and Molecular Systems (M2S), KU Leuven, Celestijnenlaan 200F, Post Box 2454, 3001 Leuven, Belgium
      More by S. Smolders
    • Dirk De Vos
      Dirk De Vos
      Centre for Membrane Separations, Adsorption, Catalysis and Spectroscopy for Sustainable Solutions (cMACS), Department of Microbial and Molecular Systems (M2S), KU Leuven, Celestijnenlaan 200F, Post Box 2454, 3001 Leuven, Belgium
      More by Dirk De Vos
      Orcidhttps://orcid.org/0000-0003-0490-9652
    • A. V. Soldatov
      A. V. Soldatov
      The Smart Materials Research Institute, Southern Federal University, Sladkova 178/24, 344090 Rostov-on-Don, Russia
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    Other Access OptionsSupporting Information (1)

    The Journal of Physical Chemistry C

    Cite this: J. Phys. Chem. C 2021, 125, 50, 27844–27852
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpcc.1c09082
    Published December 12, 2021
    Copyright © 2021 American Chemical Society
    Request reuse permissions

    Abstract

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    X-ray absorption spectroscopy is a powerful tool for the characterization of local atomic structure. Commonly, bond lengths and coordination numbers are extracted from the extended energy region of the spectrum (extended X-ray absorption fine structure, EXAFS). However, for many diluted systems, such as homogeneous catalysts, with a low concentration of the active component and under in situ or operando conditions, one cannot collect sufficient EXAFS data for a quantitative analysis. Considering the case of a homogeneous ruthenium-based catalyst, where the ligand surrounding the ruthenium atoms can change from Br to CO depending on the reaction conditions, we establish here an effective machine learning approach based on the descriptor analysis of spectral features. After the training procedure, the algorithm predicts both the ligand surrounding ruthenium and the distances to Br and CO ligands. The prediction quality of the approach was verified by means of a cross-validation procedure applied to the mixture of compounds and was validated for experimental spectra of reference RuBr3 and [RuBr2(CO)3]2 complexes. This work describes a practical route to improve classical fingerprint analysis and linear combination fit by more sophisticated data science algorithms.

    Copyright © 2021 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jpcc.1c09082.

    • Convolution parameters, visualizations of XANES spectra from the training set, the full list of descriptors, and 3D structures obtained after XANES fitting (PDF)

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    Cited By

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    This article is cited by 6 publications.

    1. Tatyana V. Krasnyakova, Denis V. Nikitenko, Andrey S. Gogil’chin, Irina O. Krasniakova, Alexander A. Guda, Aram L. Bugaev, Sergey A. Mitchenko. Reductive Cross-Electrophile C(sp2)–C(sp3) Coupling Catalyzed by PtI2: In Situ Structural Determination of the Intermediates by X-ray Absorption Spectroscopy and Multinuclear NMR. Organometallics 2024, 43 (1) , 55-67. https://doi.org/10.1021/acs.organomet.3c00400
    2. Alina A. Skorynina, Bogdan O. Protsenko, Oleg A. Usoltsev, Sergey A. Guda, Aram L. Bugaev. Quantitative Structural Description of Zeolites by Machine Learning Analysis of Infrared Spectra. Inorganic Chemistry 2023, 62 (17) , 6608-6616. https://doi.org/10.1021/acs.inorgchem.2c04395
    3. M. A. Golubeva, M. Mukhtarova, A. L. Bugaev, E. R. Naranov. In Situ Generated Dispersed Catalysts Based on Molybdenum and Tungsten Phosphides in Hydroprocessing of Guaiacol. Petroleum Chemistry 2022, 62 (11) , 1300-1307. https://doi.org/10.1134/S0965544122110019
    4. Petr V. Shvets, Pavel A. Prokopovich, Artur I. Dolgoborodov, Oleg A. Usoltsev, Alina A. Skorynina, Elizaveta G. Kozyr, Viktor V. Shapovalov, Alexander A. Guda, Aram L. Bugaev, Evgeny R. Naranov, Dmitry N. Gorbunov, Kwinten Janssens, Dirk E. De Vos, Alexander L. Trigub, Emiliano Fonda, Mark B. Leshchinsky, Vladimir R. Zagackij, Alexander V. Soldatov, Alexander Yu. Goikhman. In Situ X-ray Absorption Spectroscopy Cells for High Pressure Homogeneous Catalysis. Catalysts 2022, 12 (10) , 1264. https://doi.org/10.3390/catal12101264
    5. K. Janssens, A. L. Bugaev, E. G. Kozyr, V. Lemmens, A. A. Guda, O. A. Usoltsev, S. Smolders, A. V. Soldatov, D. E. De Vos. Evolution of the active species of homogeneous Ru hydrodeoxygenation catalysts in ionic liquids. Chemical Science 2022, 13 (35) , 10251-10259. https://doi.org/10.1039/D2SC02150A
    6. C. D. Rankine, T. J. Penfold. Accurate, affordable, and generalizable machine learning simulations of transition metal x-ray absorption spectra using the XANESNET deep neural network. The Journal of Chemical Physics 2022, 156 (16) , 164102. https://doi.org/10.1063/5.0087255
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    The Journal of Physical Chemistry C

    Cite this: J. Phys. Chem. C 2021, 125, 50, 27844–27852
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.jpcc.1c09082
    Published December 12, 2021
    Copyright © 2021 American Chemical Society
    Request reuse permissions

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