Volumes and Issues
Home » Technical Physics » Year 2024, issue 4 » Article p. 586
<<<>>>
Applicability of XANES spectroscopy and machine learning methods for the determination of local atomic structure of Cu-MOR zeolites
Russian version
Gladchenko-Djevelekis Ya. N. 1, Sukharina G. B. 1, Ermakova A. M. 1, Kulaev K. D.1, Pryadchenko V. V. 1, Ponosova E. E. 1, Shemetova E. I. 1, Avakyan L. A. 1, Bugaev L. A. 1
1Southern Federal University, Rostov-on-Don, Russia
Email: ygl@sfedu.ru, gbsukharina@sfedu.ru, aleker@sfedu.ru, kulaev@sfedu.ru, ponosova@sfedu.ru, shemetova@sfedu.ru, laavakyan@sfedu.ru, bugaev@sfedu.ru
PDF
The research is devoted to the development of methods of the determination of the local structure of copper centers in Cu-MOR using a combination of machine learning and X-ray absorption spectroscopy techniques. Cu-zeolites are promising catalysts for processes of environmentally friendly production of methanol from natural methane gas, the catalytic activity of which is mostly determined by the local environment of copper atoms in the zeolite. The irregular distribution of copper centers in the zeolite framework increases the complexity of the problem, since it makes difficult to interpret the experimental Cu K-XANES spectra. Machine learning algorithms trained on the synthetic data obtained in the FDMNES software package allowed us to determine the location of copper centers in a particular zeolite ring with an accuracy of 0.97 according to the F1 metric. Keywords: zeolites, atomic structure, XANES, ML-classification, neural networks.
  1. S.E. Bozbag, E.M.C. Alayon, J. Pechacek, M. Nachtegaal, M. Ranocchiari, J.A. van Bokhoven. Catal. Sci. Technol., 6, 5011 (2016). DOI: 10.1039/C6CY00041J
  2. E.M.C. Alayon, M. Nachtegaal, A. Bodi, M. Ranocchiari, J.A. van Bokhoven. Phys. Chem. Chem. Phys., 17, 7681 (2015). DOI: 10.1039/C4CP03226H
  3. E.M.C. Alayon, M. Nachtegaal, E. Kleymenov, J.A. van Bokhoven. Microporous Mesoporous Mater., 166, 131 (2013). DOI: 10.1016/j.micromeso.2012.04.054
  4. B.F. Sels, L.M. Kustov (ed.). Zeolites and Zeolite-Like Materials (Elsevier, 2016), DOI: 10.1016/C2014-0-00257-2
  5. J.S. Woertink, P.J. Smeets, M.H. Groothaert, M.A. Vance, B.F. Sels, R.A. Schoonheydt, E.I. Solomon. Proc. Natl. Acad. Sci., 106, 18908 (2009). DOI: 10.1073/pnas.0910461106
  6. V.V. Pryadchenko, G.B. Sukharina, A.M. Ermakova, S.V. Bazovaya, T.I. Kurzina, V.A. Durymanov, V.A. Tolstopyatenko, V.V. Srabionyan, L.A. Avakyan, L.A. Bugaev. Tech. Phys., 66, 1018 (2021). DOI: 10.1134/S1063784221070124
  7. V.V. Srabionyan, G.B. Sukharina, S.Y. Kaptelinin, V.A. Durymanov, A.M. Ermakova, T.I. Kurzina, L.A. Avakyan, L.A. Bugaev. Phys. Solid State, 62, 1222 (2020). DOI: 10.1134/S1063783420070252
  8. V.V. Srabionyan, G.B. Sukharina, T.I. Kurzina, V.A. Durymanov, A.M. Ermakova, L.A. Avakyan, E.M.C. Alayon, M. Nachtegaal, J.A. van Bokhoven, L.A. Bugaev. J. Phys. Chem. C, 125, 25867 (2021). DOI: 10.1021/acs.jpcc.1c08240
  9. S. Grundner, M.A.C. Markovits, G. Li, M. Tromp, E.A. Pidko, E.J.M. Hensen, A. Jentys, M. Sanchez-Sanchez, J.A. Lercher. Nat. Commun., 6, 7546 (2015). DOI: 10.1038/ncomms8546
  10. Q. Zhang, J. Yu, A. Corma. Adv. Mater., 32, 2002927 (2020). DOI: 10.1002/adma.202002927
  11. A.A. Guda, S.A. Guda, A. Martini, A.L. Bugaev, M.A. Soldatov, A.V. Soldatov, C. Lamberti. Radiat. Phys. Chem., 175, 108430 (2020). DOI: 10.1016/j.radphyschem.2019.108430
  12. A.A. Guda, S.A. Guda, A. Martini, A.N. Kravtsova, A. Algasov, A. Bugaev, S.P. Kubrin, L.V. Guda, P. Sot, J.A. van Bokhoven, C. Coperet, A.V. Soldatov. Npj Comput. Mater., 7, 203 (2021). DOI: 10.1038/s41524-021-00664-9
  13. L. Avakyan, D. Tolchina, V. Barkovski, S. Belenov, A. Alekseenko, A. Shaginyan, V. Srabionyan, V. Guterman, L. Bugaev. Comput. Mater. Sci., 208, 111326 (2022). DOI: 10.1016/j.commatsci.2022.111326
  14. M. Moliner, Y. Roman-Leshkov, A. Corma. Acc. Chem. Res., 52, 2971 (2019). DOI: 10.1021/acs.accounts.9b00399
  15. A. Gandhi, M.M.F. Hasan. Curr. Opin. Chem. Eng., 35, 100739 (2022). DOI: 10.1016/j.coche.2021.100739
  16. J.D. Evans, F.-X. Coudert. Chem. Mater., 29, 7833 (2017). DOI: 10.1021/acs.chemmater.7b02532
  17. J.H. Friedman. Ann. Stat., 29, 1189 (2001). DOI: 10.1214/aos/1013203451
  18. T. Hastie, J. Friedman, R. Tibshirani. The Elements of Statistical Learning (Springer, NY., 2001), DOI: 10.1007/978-0-387-21606-5
  19. Q. Zhu, Y. Gu, X. Liang, X. Wang, J. Ma. ACS Catal., 12, 12336 (2022). DOI: 10.1021/acscatal.2c03250
  20. A.V. Soldatov, A.N. Kravtsova, L.N. Mazalov, S.V. Trubina, N.A. Kryuchkova, G.B. Sukharina. J. Struct. Chem., 48, 1061 (2007). DOI: 10.1007/s10947-007-0171-0
  21. J.J. Rehr, A.L. Ankudinov. Coord. Chem. Rev., 249, 131 (2005). DOI: 10.1016/j.ccr.2004.02.014
  22. A. Hjorth Larsen, J. J rgen Mortensen, J. Blomqvist, I.E. Castelli, R. Christensen, M. Du ak, J. Friis, M.N. Groves, B. Hammer, C. Hargus, E.D. Hermes, P.C. Jennings, P. Bjerre Jensen, J. Kermode, J.R. Kitchin, E. Leonhard Kolsbjerg, J. Kubal, K. Kaasbjerg, S. Lysgaard, J. Bergmann Maronsson, T. Maxson, T. Olsen, L. Pastewka, A. Peterson, C. Rostgaard, J. Schi tz, O. Schutt, M. Strange, K.S. Thygesen, T. Vegge, L. Vilhelmsen, M. Walter, Z. Zeng, K.W. Jacobsen. J. Phys. Condens. Matter, 29, 273002 (2017). DOI: 10.1088/1361-648X/aa680e
  23. G.M. Psofogiannakis, J.F. McCleerey, E. Jaramillo, A.C.T. van Duin. J. Phys. Chem. C, 119, 6678 (2015). DOI: 10.1021/acs.jpcc.5b00699
  24. Y. Joly. Phys. Rev. B, 63, 125120 (2001). DOI: 10.1103/PhysRevB.63.125120
  25. M.S. Asyaky, R. Mandala. Improving the Performance of HDBSCAN on Short Text Clustering by Using Word Embedding and UMAP, in: 2021 8th Int. Conf. Adv. Informatics Concepts, Theory Appl., IEEE, (2021), p. 6. DOI: 10.1109/ICAICTA53211.2021.9640285
  26. B.E.R. Snyder, P. Vanelderen, R.A. Schoonheydt, B.F. Sels, E.I. Solomon. J. Am. Chem. Soc., 140, 9236 (2018). DOI: 10.1021/jacs.8b05320
  27. M.H. Mahyuddin, T. Tanaka, A. Staykov, Y. Shiota, K. Yoshizawa. Inorg. Chem., 57, 10146 (2018). DOI: 10.1021/acs.inorgchem.8b01329
  28. P. Han, Z. Zhang, Z. Chen, J. Lin, S. Wan, Y. Wang, S. Wang, Catalysts, 11, 751 (2021). DOI: 10.3390/catal11060751
  29. Electronic source. Available at: https://pytorch.org/

Подсчитывается количество просмотров абстрактов ("html" на диаграммах) и полных версий статей ("pdf"). Просмотры с одинаковых IP-адресов засчитываются, если происходят с интервалом не менее 2-х часов.

Дата начала обработки статистических данных - 27 января 2016 г.

Publisher:

Ioffe Institute

Institute Officers:

Director: Sergei V. Ivanov

Contact us:

26 Polytekhnicheskaya, Saint Petersburg 194021, Russian Federation
Fax: +7 (812) 297 1017
Phone: +7 (812) 297 2245
E-mail: post@mail.ioffe.ru