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Computational discovery of fast interstitial oxygen conductors

Nature Materials (2024)Cite this article

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Abstract

New highly oxygen-active materials may enhance many energy-related technologies by enabling efficient oxygen-ion transport at lower temperatures, for example, below ~400 °C. Interstitial oxygen conductors have the potential to realize such performance but have received far less attention than vacancy-mediated conductors. Here we combine physically motivated structure and property descriptors, ab initio simulations and experiments to demonstrate an approach to discover new fast interstitial oxygen conductors. Multiple new families were found, which adopt completely different structures from known oxygen conductors. From these families, we synthesized and studied oxygen kinetics in La4Mn5Si4O22+δ, a representative member of the perrierite/chevkinite family. We found that La4Mn5Si4O22+δ has higher oxygen-ion conductivity than the widely used yttria-stabilized ZrO2, and among the highest surface oxygen exchange rates at the intermediate temperature of known materials. The fast oxygen kinetics is the result of simultaneously active interstitial and interstitialcy diffusion pathways. We propose that the essential features for forming an effective interstitial oxygen conductor are the availability of electrons and structural flexibility, enabling a sufficient accessible volume. This work provides a powerful approach for understanding and discovering new interstitial oxygen conductors.

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Fig. 1: Oxygen migration barriers in interstitial and vacancy conductors.
Fig. 2: Schematic of the screening approach.
Fig. 3: Bulk structure of fast interstitial oxygen conductors.
Fig. 4: Structure and interstitial oxygen mobility in La4Mn5Si4O22.

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Source data and data that support the plots within this paper are available via figshare at https://doi.org/10.6084/m9.figshare.23808606 (ref. 78). Please refer to the readme.txt file in the repository for guidance. Source data are provided with this paper.

References

  1. Wachsman, E. D. & Lee, K. T. Lowering the temperature of solid oxide fuel cells. Science 334, 935–939 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Vøllestad, E. et al. Mixed proton and electron conducting double perovskite anodes for stable and efficient tubular proton ceramic electrolysers. Nat. Mater. 18, 752–759 (2019).

    Article  PubMed  Google Scholar 

  3. Duan, C. et al. Highly efficient reversible protonic ceramic electrochemical cells for power generation and fuel production. Nat. Energy 4, 230–240 (2019).

    Article  CAS  Google Scholar 

  4. Hong, W. T., Risch, M., Stoerzinger, K. A. & Grimaud, A. Toward the rational design of non-precious transition metal oxides for oxygen electrocatalysis. Energy Environ. Sci. 8, 1404–1427 (2015).

    Article  CAS  Google Scholar 

  5. Zhang, C. & Huang, K. in Solid Oxide-Based Electrochemical Devices Ch. 7, 217–250 (Elsevier, 2020).

  6. Eranna, G., Joshi, B. C., Runthala, D. P. & Gupta, R. P. Oxide materials for development of integrated gas sensors—a comprehensive review. Crit. Rev. Solid State Mater. Sci. 29, 111–188 (2004).

    Article  CAS  Google Scholar 

  7. Hossain, M. M. & de Lasa, H. I. Chemical-looping combustion (CLC) for inherent CO2 separations—a review. Chem. Eng. Sci. 63, 4433–4451 (2008).

    Article  CAS  Google Scholar 

  8. Yang, J. J., Strukov, D. B. & Stewart, D. R. Memristive devices for computing. Nat. Nanotechnol. 8, 13–24 (2013).

    Article  CAS  PubMed  Google Scholar 

  9. Petric, A., Huang, P. & Tietz, F. Evaluation of La–Sr–Co–Fe–O perovskites for solid oxide fuel cells and gas separation membranes. Solid State Ion. 135, 719–725 (2000).

    Article  CAS  Google Scholar 

  10. van Gool, W. Fast ion conduction. Annu. Rev. Mater. Sci. 4, 311–335 (1974).

    Article  Google Scholar 

  11. Heider, U., Jörissen, L., Huggins, R. A. & Witschel, W. Oxygen ion conductivity in doped Gd2Ti2O7 with the pyrochlore structure. Ionics 2, 7–11 (1996).

    Article  CAS  Google Scholar 

  12. Huang, K., Tichy, R. S. & Goodenough, J. B. Superior perovskite oxide-ion conductor; strontium- and magnesium-doped LaGaO3: I, phase relationships and electrical properties. J. Am. Ceram. Soc. 81, 2565–2575 (1998).

    Article  CAS  Google Scholar 

  13. Skinner, S. J. & Kilner, J. A. Oxygen ion conductors. Mater. Today 6, 30–37 (2003).

    Article  CAS  Google Scholar 

  14. Strickler, D. W. & Carlson, W. G. Electrical conductivity in the ZrO2-rich region of several M2O3-ZrO2 systems. J. Am. Ceram. Soc. 48, 286–289 (1965).

    Article  CAS  Google Scholar 

  15. Steele, B. C. H. Appraisal of Ce1–yGdyO2–y/2 electrolytes for IT-SOFC operation at 500 °C. Solid State Ion. 129, 95–110 (2000).

    Article  CAS  Google Scholar 

  16. Antono, E., Meredig, B. & Mulholland, G. J. ARPA-E Ionics Database (accessed 18 August 2023); https://citrination.com/datasets/151085/show_files/

  17. Waldow, S. P. & De Souza, R. A. Is excess faster than deficient? A molecular-dynamics study of oxygen-interstitial and oxygen-vacancy diffusion in CeO2. J. Phys.: Energy 2, 024001 (2020).

    CAS  Google Scholar 

  18. Song, J., Ning, D., Boukamp, B., Bassat, J. M. & Bouwmeester, H. J. M. Structure, electrical conductivity and oxygen transport properties of Ruddlesden-Popper phases Lnn+1NinO3n+1 (Ln = La, Pr and Nd; n = 1, 2 and 3). J. Mater. Chem. A 8, 22206–22221 (2020).

    Article  CAS  Google Scholar 

  19. Arikawa, H., Nishiguchi, H., Ishihara, T. & Takita, Y. Oxide ion conductivity in Sr-doped La10Ge6O27 apatite oxide. Solid State Ion. 136–137, 31–37 (2000).

    Article  Google Scholar 

  20. Thomas, C. I. et al. Phase stability control of interstitial oxide ion conductivity in the La1+xSr1–xGa3O7+x/2 melilite family. Chem. Mater. 22, 2510–2516 (2010).

    Article  CAS  Google Scholar 

  21. Lacerda, M. et al. High oxide ion conductivity in Ca12Al14O33. Nature 332, 525–526 (1988).

    Article  CAS  Google Scholar 

  22. Li, J. et al. Modulated structure determination and ion transport mechanism of oxide-ion conductor CeNbO4+δ. Nat. Commun. 11, 4751 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Pramana, S. S. et al. Correlation of local structure and diffusion pathways in the modulated anisotropic oxide ion conductor CeNbO4.25. J. Am. Chem. Soc. 138, 1273–1279 (2016).

    Article  CAS  PubMed  Google Scholar 

  24. Skjærvø, S. H. et al. Interstitial oxygen as a source of p-type conductivity in hexagonal manganites. Nat. Commun. 7, 7491 (2016).

    Article  Google Scholar 

  25. Yashima, M. et al. High oxide-ion conductivity through the interstitial oxygen site in Ba7Nb4MoO20-based hexagonal perovskite related oxides. Nat. Commun. 12, 556 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fop, S. et al. High oxide ion and proton conductivity in a disordered hexagonal perovskite. Nat. Mater. 19, 752–757 (2020).

    Article  CAS  PubMed  Google Scholar 

  27. Mayeshiba, T. T. & Morgan, D. D. Factors controlling oxygen migration barriers in perovskites. Solid State Ion. 296, 71–77 (2016).

    Article  CAS  Google Scholar 

  28. Solodovnikov, S. F., Klevtsova, R. F., Kim, V. G. & Klevtsov, P. V. Double molybdates of composition CsR22+(MoO4)3 (R=Ni, Co, Mg, Mn, Cd) and the crystal structure of Cs2Co2(MoO4)3. J. Struct. Chem. 27, 928–933 (1986).

    Article  Google Scholar 

  29. Ito, J. & Arem, J. E. Chevkinite and perrierite: synthesis, crystal growth and polymorphism. Am. Mineral. 56, 307–319 (1971).

    Google Scholar 

  30. Taviot-Guého, C., Léone, P., Palvadeau, P. & Rouxel, J. Synthesis and structural characterization of two new rare-earth manganese germanates: CeMn2Ge4O12 and GdMnGe2O7. J. Solid State Chem. 143, 145–150 (1999).

    Article  Google Scholar 

  31. Gueho, C., Giaquinta, D., Mansot, J. L., Ebel, T. & Palvadeau, P. Structure and magnetism of La4Mn5Si4O22 and La4V5Si4O22: two new rare-earth transition metal sorosilicates. Chem. Mater. 7, 486–492 (1995).

    Article  CAS  Google Scholar 

  32. Nakayama, S., Kageyama, T., Aono, H. & Sadaokac, Y. Ionic conductivity of lanthanoid silicates, Ln10(SiO4)6O3 (Ln=La, Nd, Sm, Gd, Dy, Y, Ho, Er and Yb). J. Mater. Chem. 5, 1801–1805 (1995).

    Article  CAS  Google Scholar 

  33. Kuang, X. et al. Interstitial oxide ion conductivity in the layered tetrahedral network melilite structure. Nat. Mater. 7, 498–504 (2008).

    Article  CAS  PubMed  Google Scholar 

  34. Murch, G. E., Bradhurst, D. H. & De Bruin, J. H. Oxygen self-diffusion in non-stoichiometric uranium dioxide. Philos. Mag. 32, 1141–1150 (1975).

    Article  CAS  Google Scholar 

  35. Medasani, B., Sushko, M. L., Rosso, K. M., Schreiber, D. K. & Bruemmer, S. M. First-principles investigation of native interstitial diffusion in Cr2O3. J. Phys. Chem. C 122, 12984–12993 (2018).

    Article  CAS  Google Scholar 

  36. Roma, G., Limoge, Y. & Baroni, S. Oxygen self-diffusion in α-quartz. Phys. Rev. Lett. 86, 4564–4567 (2001).

    Article  CAS  PubMed  Google Scholar 

  37. Yasui, Y., Niwa, E., Matsui, M., Fujii, K. & Yashima, M. Discovery of a rare-earth-free oxide-ion conductor Ca3Ga4O9 by screening through bond valence-based energy calculations, synthesis, and characterization of structural and transport properties. Inorg. Chem. 58, 9460–9468 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Martín-Sedeño, M. C. et al. Enhancement of oxide ion conductivity in cuspidine-type materials. Chem. Mater. 16, 4960–4968 (2004).

    Article  Google Scholar 

  39. Diaz-Lopez, M. et al. Interstitial oxide ion conductivity in the langasite structure: carrier trapping by formation of (Ga,Ge)2O8 units in La3Ga5–xGe1+xO14+x/2 (0 < x ≤ 1.5). Chem. Mater. 31, 5742–5758 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jun, K. J. et al. Lithium superionic conductors with corner-sharing frameworks. Nat. Mater. 21, 924–931 (2022).

    Article  CAS  PubMed  Google Scholar 

  41. Muy, S., Schlem, R., Shao-Horn, Y. & Zeier, W. G. Phonon–ion interactions: designing ion mobility based on lattice dynamics. Adv. Energy Mater. 11, 2002787 (2021).

    Article  CAS  Google Scholar 

  42. Lei, C., Simpson, M. F. & Virkar, A. V. Investigation of ion and electron conduction in the mixed ionic-electronic conductor-La-Sr-Co-Fe-oxide (LSCF) using alternating current (a.c.) and direct current (d.c.) techniques. J. Electrochem. Soc. 169, 014506 (2022).

    Article  CAS  Google Scholar 

  43. Du, Y. et al. in Computational Design of Engineering Materials: Fundamentals and Case Studies Ch. 6 (Cambridge Univ. Press, 2023).

  44. Endler-Schuck, C., Joos, J., Niedrig, C., Weber, A. & Ivers-Tiffée, E. The chemical oxygen surface exchange and bulk diffusion coefficient determined by impedance spectroscopy of porous La0.58Sr0.4Co0.2Fe0.8O3−δ (LSCF) cathodes. Solid State Ion. 269, 67–79 (2015).

    Article  CAS  Google Scholar 

  45. Zohourian, R., Merkle, R. & Maier, J. Proton uptake into the protonic cathode material BaCo0.4Fe0.4Zr0.2O3–δ and comparison to protonic electrolyte materials. Solid State Ion. 299, 64–69 (2017).

    Article  CAS  Google Scholar 

  46. Bucher, E., Egger, A., Ried, P., Sitte, W. & Holtappels, P. Oxygen nonstoichiometry and exchange kinetics of Ba0.5Sr0.5Co0.8Fe0.2O3−δ. Solid State Ion. 179, 1032–1035 (2008).

    Article  CAS  Google Scholar 

  47. Jacobs, R. et al. Unconventional highly active and stable oxygen reduction catalysts informed by computational design strategies. Adv. Energy Mater. 12, 2201203 (2022).

    Article  CAS  Google Scholar 

  48. Li, M. et al. A family of oxide ion conductors based on the ferroelectric perovskite Na0.5Bi0.5TiO3. Nat. Mater. 13, 31–35 (2014).

    Article  CAS  PubMed  Google Scholar 

  49. Jain, A. et al. Commentary: the Materials Project: a materials genome approach to accelerating materials innovation. APL Mater. 1, 11002 (2013).

    Article  Google Scholar 

  50. Okabe, A., Boots, B., Sugihara K. & Chiu, S. N. Spatial Tessellations: Concepts and Applications of Voronoi Diagrams (John Wiley & Sons, 2000).

  51. O’Keefe, M. & Brese, N. E. Atom sizes and bond lengths in molecules and crystals. J. Am. Chem. Soc. 113, 3226–3229 (1991).

    Article  Google Scholar 

  52. Belsky, A., Hellenbrandt, M., Karen, V. L. & Luksch, P. New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design. Acta Cryst. B58, 364–369 (2002).

    Article  CAS  Google Scholar 

  53. Pan, H. et al. Benchmarking coordination number prediction algorithms on inorganic crystal structures. Inorg. Chem. 60, 1590–1603 (2021).

    Article  CAS  PubMed  Google Scholar 

  54. Linstrom, P. J. & Mallard W. G. (eds) NIST Chemistry WebBook, NIST Standard Reference Database Number 69 (National Institute of Standards and Technology, 2023).

  55. Jacobs, R. M., Booske, J. H. & Morgan, D. Intrinsic defects and conduction characteristics of Sc2O3 in thermionic cathode systems. Phys. Rev. B 86, 054106 (2012).

    Article  Google Scholar 

  56. Hine, N. D. M., Frensch, K., Foulkes, W. M. C. & Finnis, M. W. Supercell size scaling of density functional theory formation energies of charged defects. Phys. Rev. B 79, 024112 (2009).

    Article  Google Scholar 

  57. Wang, L., Maxisch, T. & Ceder, G. Oxidation energies of transition metal oxides within the GGA+U framework. Phys. Rev. B 73, 195107 (2006).

    Article  Google Scholar 

  58. Lee, Y.-L., Kleis, J., Rossmeisl, J. & Morgan, D. Ab initio energetics of LaBO3(001) (B=Mn, Fe, Co, and Ni) for solid oxide fuel cell cathodes. Phys. Rev. B 80, 224101 (2009).

    Article  Google Scholar 

  59. Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 54, 11169–11186 (1996).

    Article  CAS  Google Scholar 

  60. Perdew, J., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).

    Article  CAS  PubMed  Google Scholar 

  61. Kresse, G. & Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 59, 1758–1775 (1999).

    Article  CAS  Google Scholar 

  62. Shuichi, N. Constant temperature molecular dynamics methods. Prog. Theor. Phys. Suppl. 103, 1–46 (1991).

    Article  Google Scholar 

  63. Hoover, W. G. Canonical dynamics: equilibrium phase-space distributions. Phys. Rev. A 31, 1695–1697 (1985).

    Article  CAS  Google Scholar 

  64. He, X., Zhu, Y., Epstein, A. & Mo, Y. Statistical variances of diffusional properties from ab initio molecular dynamics simulations. npj Comput. Mater. 4, 18 (2018).

    Article  Google Scholar 

  65. Box, G. E. P. & Jenkins, G. M. Time Series Analysis: Forecasting and Control (Holden-Day, 1976).

  66. Shapeev, A. V. Moment tensor potentials: a class of systematically improvable interatomic potentials. Multiscale Model. Simul. 14, 1153–1173 (2016).

    Article  Google Scholar 

  67. Gubaev, K., Podryabinkin, E. V., Hart, G. L. W. & Shapeev, A. V. Accelerating high-throughput searches for new alloys with active learning of interatomic potentials. Comput. Mater. Sci. 156, 148–156 (2019).

    Article  CAS  Google Scholar 

  68. Zuo, Y. et al. Performance and cost assessment of machine learning interatomic potentials. J. Phys. Chem. A 124, 731–745 (2020).

    Article  CAS  PubMed  Google Scholar 

  69. Li, X.-G., Chen, C., Zheng, H., Zuo, Y. & Ong, S. P. Complex strengthening mechanisms in the NbMoTaW multi-principal element alloy. npj Comput. Mater. 6, 70 (2020).

    Article  CAS  Google Scholar 

  70. Novikov, I. S., Gubaev, K., Podryabinkin, E. V. & Shapeev, A. V. The MLIP package: moment tensor potentials with MPI and active learning. Mach. Learn. Sci. Technol. 2, 025002 (2021).

    Article  Google Scholar 

  71. Rodríguez-Carvajal, J. Recent advances in magnetic structure determination by neutron powder diffraction. Phys. B Condens. Matter 192, 55–69 (1993).

    Article  Google Scholar 

  72. Sangiorgi, N., Aversa, L., Tatti, R., Verucchi, R. & Sanson, A. Spectrophotometric method for optical band gap and electronic transitions determination of semiconductor materials. Opt. Mater. 64, 18–25 (2017).

    Article  CAS  Google Scholar 

  73. Tauc, J., Grigorovici, R. & Vancu, A. Optical properties and electronic structure of amorphous germanium. Phys. status solidi (b) 15, 627–637 (1966).

    Article  CAS  Google Scholar 

  74. Kumar, U., Yadav, D. & Upadhyay, S. Investigation of structural, optical, and magnetic properties of Nd-doped Sr2SnO4 Ruddlesden Popper oxide. J. Am. Ceram. Soc. 103, 5743–5757 (2020).

    Article  CAS  Google Scholar 

  75. Kumar, U. & Upadhyay, S. Investigation of structural, optical and electrical properties of Sr2SnO4, Sr1.99Eu0.01SnO4 and Sr2Sn0.99Eu0.01O4 Ruddlesden Popper oxide. Mater. Res. Express 6, 55805 (2019).

    Article  CAS  Google Scholar 

  76. Islam, M. N., Ghosh, T. B., Chopra, K. L. & Acharya, H. N. XPS and X-ray diffraction studies of aluminum-doped zinc oxide transparent conducting films. Thin Solid Films 280, 20–25 (1996).

    Article  CAS  Google Scholar 

  77. TA Instruments Q500 (UW–Madison, accessed 4 June 2024); https://wcnt.wisc.edu/thermogravimetric-analysis/

  78. Meng, J. et al. Computational discovery of fast interstitial oxygen conductors. figshare https://doi.org/10.6084/m9.figshare.23808606 (2024).

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Acknowledgements

This work was funded by the US Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under award no. DE-SC0020419 (J.M., M.S.S., R.J. and D.M.). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation grant no. ACI-1548562 (to J.M., R.J. and D.M.). This work used the computational resources provided by the Center for High Throughput Computing at the University of Wisconsin–Madison. This work used facilities and instrumentation supported by the National Science Foundation (NSF) through the University of Wisconsin Materials Research Science and Engineering Center (DMR-1720415).

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  1. These authors contributed equally: Jun Meng, Md Sariful Sheikh.

Authors and Affiliations

  1. Department of Materials Science and Engineering, University of Wisconsin–Madison, Madison, WI, USA

    Jun Meng, Md Sariful Sheikh, Ryan Jacobs, Xiangguo Li & Dane Morgan

  2. National Energy Technology Laboratory, Morgantown, WV, USA

    Jian Liu

  3. Department of Geoscience, University of Wisconsin–Madison, Madison, WI, USA

    William O. Nachlas

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Contributions

R.J. and D.M. conceived and managed the project. J.M. performed the screening, ab initio calculations and theoretical analyses, with assistance from D.M. and R.J. M.S.S. performed the synthesis, characterization and conductivity and kinetic measurements. J.L. helped with the ECR analysis and contributed to scientific discussions. W.O.N. performed the EPMA analysis. X.L. trained the ML-IP. J.M. wrote the first version of the manuscript with input from M.S.S. R.J. and D.M. reviewed the manuscript. All authors have reviewed and commented on the manuscript.

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Correspondence to Dane Morgan.

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Supplementary Information

Supplementary Tables 1–6, Figs. 1–16 and Discussions 1–11.

Supplementary Video 1

AIMD simulation showcasing the real-time simultaneous occurrence of interstitial and interstitialcy diffusion mechanisms of oxygen-ion diffusion in La4Mn5Si4O22. The La, Mn and Si sites are shown as light blue, purple and dark blue spheres, respectively. In this video, the interstitial oxygen (red ball) initially hops through the interstitial mechanism to a new interstitial site, and subsequently, it kicks a lattice oxygen (yellow ball) to another interstitial site, which then, in turn, kicks another lattice oxygen (orange ball) to another interstitial site. This latter step represents an interstitialcy mechanism. The simulation was conducted at 2,000 K using the SCAN functional.

Supplementary Video 2

AIMD simulation showcasing the interstitial-oxygen-ion diffusion in K2Mn2(MoO4)3. The K, Mn, Mo and O sites are shown as big purple, small purple, grey and red spheres, respectively. In this video, the interstitial oxygen (yellow ball) kicks out the lattice oxygen (orange ball) to another interstitial site along with the facile polyhedra rotation. The simulation was conducted at 1,600 K using the GGA-PBE functional.

Supplementary Video 3

AIMD simulation showcasing the interstitial-oxygen-ion diffusion in CeMn2Ge4O12. The Ce, Mn, Ge and O sites are shown as green, purple, blue and red spheres, respectively. In this video, the interstitial oxygen (yellow ball) in between two corner-sharing Ge tetrahedra kicks a lattice oxygen (black ball) to the Ce tunnel, which then kicks another lattice oxygen (blue ball) to the interstitial site in between two Ge tetrahedra. The interstitial oxygen diffuses through an interstitialcy mechanism in CeMn2Ge4O12. The simulation was conducted at 2,000 K using the GGA-PBE functional.

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Meng, J., Sheikh, M.S., Jacobs, R. et al. Computational discovery of fast interstitial oxygen conductors. Nat. Mater. (2024). https://doi.org/10.1038/s41563-024-01919-8

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