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Interpretable graph methods for determining nanoparticles ordering in electron microscopy image
M.Y. Kurbakov 1, V.V. Sulimova 1, O.S. Seredin 1, A.V. Kopylov 1

1 Tula State University,
300012, Russia, Tula, Lenina Av. 92

  PDF, 2458 kB

DOI: 10.18287/2412-6179-CO-1568

Страницы: 470-479.

Язык статьи: English.

Аннотация:
An important step in determining the properties of carbon materials is the analysis of images from a scanning electron microscope (SEM). These images show the material surface after the application of metal nanoparticles. The order of these nanoparticles is a key characteristic that affects the material properties. We have previously proposed an approach to formalize the order features based on the identification of lines by nanoparticles in the SEM image. This paper proposes a novel approach to line allocation that is based on the concept of constructing a minimum spanning forest. Additionally, it introduces a set of novel ordering functions that are derived from this approach. The experimental study demonstrates that the combination of these new and previously extracted features improves the recognition quality of SEM images with ordered and disordered nanoparticles arrangements. This approach allows us to gain a better understanding of the nanoparticles arrangement and their effect on the material properties.

Ключевые слова:
explainable machine learning, image analysis, nanoparticle detection, nanoparticles ordering features.

Благодарности
This work was supported by the Ministry of Science and Higher Education of the Russian Federation within the framework of the state task FEWG-2024-0001.
     Experiments were partially made using the equipment of the shared research facilities of HPC computing resources at the Lomonosov Moscow State University.
The authors thank the Scientific School of Academic V.P. Ananikov for the research topic, useful discussions and provided experimental data.

Citation:
Kurbakov MY, Sulimova VV, Seredin OS, Kopylov AV. Interpretable graph methods for determining nanoparticles ordering in electron microscopy images. Computer Optics 2025; 49(3): 470-479. DOI: 10.18287/2412-6179-CO-1568.

References:
  1. Titirici M-M, White RJ, Brun N, Budarin VL, Su DS, del Monte F, Clark JH, MacLachlan MJ. Sustainable carbon materials. Chem Soc Rev 2015; 44(1): 250-290. DOI: 10.1039/C4CS00232F.
  2. Takakura A, Beppu K, Nishihara T, Fukui A, Kozeki T, Namazu T, Miyauchi Y, Itami K. Strength of carbon nanotubes depends on their chemical structures. Nat Commun 2019; 10: 3040. DOI: 10.1038/s41467-019-10959-7.
  3. Morishita K, Takarada T. Scanning electron microscope observation of the purification behaviour of carbon nanotubes. J Mater Sci 1999; 34: 1169-1174. DOI: 10.1023/A:1004544503055.
  4. Achaw O-W. A study of the porosity of activated carbons using the scanning electron microscope. In Book: Kazmiruk V, ed. Scanning electron microscopy. Ch 24. InTech; 2012. Source: <https://www.intechopen.com/chapters/30949>. DOI: 10.5772/36337.
  5. Pentsak EO, Kashin AS, Polynski MV, Kvashnina KO, Glatzel P, Ananikov VP. Spatial imaging of carbon reactivity centers in Pd/C catalytic systems. Chem Sci 2015; 6: 3302-3313. DOI: 10.1039/C5SC00802F.
  6. Pokrajac L, Abbas A, Chrzanowski W, Dias GM, Eggleton BJ, Maguire S, Maine E, Malloy T, Nathwani J, Nazar L, Sips A, Sone J, van den Berg A, Weiss PS, Mitra S. Nanotechnology for a sustainable future: Addressing global challenges with the international network4sustainable nanotechnology. ACS Nano 2021; 15(12): 18608-18623. DOI: 10.1021/acsnano.1c10919.
  7. Zhang P, Guo Z, Ullah S, Melagraki G, Afantitis A, Lynch I. Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nat Plants 2021; 7(7): 864-876. DOI: 10.1038/s41477-021-00946-6.
  8. Jenewein KJ, Torresi L, Haghmoradi N, Kormanyos A, Friederich P, Cherevko S. Navigating the unknown with AI: multiobjective Bayesian optimization of non-noble acidic OER catalysts. J Mater Chem A 2024; 12: 3072-3083. DOI: 10.1039/D3TA06651G.
  9. Boiko DA, Pentsak EO, Cherepanova VA, Gordeev EG, Ananikov VP. Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials. Chem Sci 2021; 12(21): 7428-7441. DOI: 10.1039/D0SC05696K.
  10. Kurbakov MY, Sulimova VV, Kopylov AV, Seredin OS, Boiko DA, Pentsak EO, Cherepanova VA, Ananikov VP. Determining the orderliness of carbon materials with nanoparticle imaging and explainable machine learning. Nanoscale 2024; 16(16): 13663-13676. DOI: 10.1039/d4nr00952e.
  11. Boiko DA, Sulimova VV, Kurbakov MY, Kopylov AV, Seredin OS, Cherepanova VA, Pentsak EO, Ananikov VP. Automated recognition of nanoparticles in electron microscopy images of nanoscale palladium catalysts. Nanomaterials 2022; 12(21): 3914. DOI: 10.3390/nano12213914.
  12. Prim RC. Shortest connection networks and some generalizations. Bell Syst Tech J 1957; 36(6): 1389-1401. DOI: 10.1002/j.1538-7305.1957.tb01515.x.
  13. Gorban A, Kégl B, Wunch D, Zinovyev A, eds. Principal manifolds for data visualization and dimension reduction. Berlin, Heidelberg: Springer-Verlag; 2008. ISBN: 978-3-540-73749-0.
  14. Hu M-K. Visual pattern recognition by moment invariants. IRE Trans Inf Theory 1962; 8(2): 179-187. DOI: 10.1109/TIT.1962.1057692.
  15. Surkov EE, Seredin OS, Kopylov AV. Locally optimal solutions in the shortest unclosed path search problem. Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) 2023: 221-224. DOI: 10.1109/USBEREIT58508.2023.10158834.
  16. Nesetril J, Milkova E, Nesetrilova H. Otakar Boruvka on minimum spanning tree problem translation of both the 1926 papers, comments, history. Discrete Math 2001; 233(1-3): 3-36. DOI: 10.1016/S0012-365X(00)00224-7.
  17. Kruskal JB. On the shortest spanning subtree of a graph and the traveling salesman problem. Proc American Mathematical Society 1956; 7: 48-50. DOI: 10.1090/S0002-9939-1956-0078686-7.
  18. Boiko DA, Pentsak EO, Cherepanova VA, Ananikov VP. Electron microscopy dataset for the recognition of nanoscale ordering effects and location of nanoparticles. Sci Data 2020; 7(1): 101. DOI: 10.1038/s41597-020-0439-1.
  19. Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948; 27(4): 623-656. DOI: 10.1002/j.1538-7305.1948.tb00917.x.
  20. Cover TM, Thomas JA. Elements of Information Theory. Hoboken, New Jersey: Wiley; 1991. ISBN: 978-0-471-24195-9.
  21. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. 14th Int Joint Conf on Artificial Intelligence 1995; 2: 1137-1143.
  22. Esfahani MSh, Dougherty ER. Effect of separate sampling on classification accuracy. Bioinformatics 2014; 30(2): 242-250. DOI: 10.1093/bioinformatics/btt662.
  23. Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.       Int J Mach Learn Technol 2011; 2(1): 37-63.
  24. Seredin OS, Kopylov AV, Harmonic Averaging in Classifier Quality Assessment. Pattern Recognition and Image Analysis 2024; 34(4): 1160–1171.
  25. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: Machine learning in Python. J Mach Learn Res 2011; 12: 2825-2830.
  26. Kurbakov MY, Sulimova VV. High-performance two-level parallel computing scheme for nanoparticles detection in SEM images. Int Arch Photogramm Remote Sens Spatial Inf Sci XLVIII-2/W3-2023: 145-150. DOI: 10.5194/isprs-archives-XLVIII-2-W3-2023-145-2023.
  27. Kurbakov MYu, Sulimova VV, Seredin OS, Kopylov AV. A program for detecting nanoparticles in images from an electron microscope based on the exponential approximation method [In Russian]. Certificate of state registration of the computer program No. 2023688122 of December 20, 2023.

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