Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence

Abstract

X-ray computed tomography (CT) is a non-destructive imaging technique in which contrast originates from the materials’ absorption coefficient. The recent development of laboratory nanoscale CT (nano-CT) systems has pushed the spatial resolution for battery material imaging to voxel sizes of 50 nm, a limit previously achievable only with synchrotron facilities. Given the non-destructive nature of CT, in situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area and volume expansion, during battery operation or cycling. Combined with artificial intelligence and machine learning analysis techniques, nano-CT has enabled the development of predictive models to analyse the impact of the electrode microstructure on cell performances or the influence of material heterogeneities on electrochemical responses. In this Review, we discuss the role of X-ray CT and nano-CT experimentation in the battery field, discuss the incorporation of artificial intelligence and machine learning analyses and provide a perspective on how the combination of multiscale CT imaging techniques can expand the development of predictive multiscale battery behavioural models.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Purchase on Springer Link

Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: History and trends of CT.
Fig. 2: Experimental trends of CT in the battery field.
Fig. 3: CT segmentation and analysis of battery systems.
Fig. 4: Relationship between experimental tomography data, cell model and computation of electrochemical data in battery systems.
Fig. 5: Correlative workflow analysis and modelling.

Similar content being viewed by others

References

  1. Seeram, E. Computed tomography: a technical review. Radiol. Technol. 89, 279CT–302CT (2018).

    Google Scholar 

  2. Pietsch, P. & Wood, V. X-ray tomography for lithium ion battery research: a practical guide. Annu. Rev. Mater. Res. 47, 451–479 (2017).

    Article  CAS  Google Scholar 

  3. Extend the Limits of Your Exploration: ZEISS Xradia 610 and 620 Versa (ZEISS, accessed 10 December 2020); https://asset-downloads.zeiss.com/catalogs/download/mic/8fb6e06e-de4e-46dc-a9c9-883396ca1628/EN_product-information_610-620-Versa_rel1-3.pdf

  4. Röntgen, W. C. On a new kind of rays. Science 3, 227–231 (1896).

    Article  Google Scholar 

  5. Knutsson, F. Röntgen and the Nobel Prize: with notes from his correspondence with Svante Arrhenius. Acta Radiol. Diagn. 8, 449–460 (1969).

    Article  CAS  Google Scholar 

  6. Radon, J. Über die Bestimmung von Funktionen durch ihre Integralwerte längs gewisser Mannigfaltigkeiten. Akad. Wiss. 69, 262–277 (1917).

    Google Scholar 

  7. Rubin, G. D. Computed tomography: revolutionizing the practice of medicine for 40 years. Radiology 273, S45–S74 (2014).

    Article  Google Scholar 

  8. Christoph, R. & Neumann, H. J. X-ray Tomography in Industrial Metrology: Precise, Economical and Universal (Süddeutscher, 2011).

  9. Villarraga-Gómez, H., Herazo, E. L. & Smith, S. T. X-ray computed tomography: from medical imaging to dimensional metrology. Precis. Eng. 60, 544–569 (2019).

    Article  Google Scholar 

  10. Heenan, T. M. M., Tan, C., Hack, J., Brett, D. J. L. & Shearing, P. R. Developments in X-ray tomography characterization for electrochemical devices. Mater. Today 31, 69–85 (2019).

    Article  CAS  Google Scholar 

  11. Zenyuk, I. V. Bridging X-ray computed tomography and computational modeling for electrochemical energy-conversion and -storage. Curr. Opin. Electrochem. 13, 78–85 (2019).

    Article  CAS  Google Scholar 

  12. Gayon-Lombardo, A., Mosser, L., Brandon, N. P. & Cooper, S. J. Pores for thought: generative adversarial networks for stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries. npj Comput. Mater. 6, 82 (2020).

    Article  CAS  Google Scholar 

  13. Petrich, L. et al. Crack detection in lithium-ion cells using machine learning. Comput. Mater. Sci. 136, 297–305 (2017).

    Article  Google Scholar 

  14. Jiang, Z. et al. Machine-learning-revealed statistics of the particle–carbon/binder detachment in lithium-ion battery cathodes. Nat. Commun. 11, 2310 (2020).

    Article  CAS  Google Scholar 

  15. Chiro, G. D. & Brooks, R. A. The 1979 Nobel Prize in Physiology or Medicine. J. Comput. Assist. Tomogr. 4, 241–245 (1980).

    Article  Google Scholar 

  16. Beckmann, E. C. CT scanning the early days. Br. J. Radiol. 79, 5–8 (2006).

    Article  CAS  Google Scholar 

  17. Hounsfield, G. N. Computerized transverse axial scanning (tomography): part 1. Description of system. Br. J. Radiol. 46, 1016–1022 (1973).

    Article  CAS  Google Scholar 

  18. Boerckel, J. D., Mason, D. E., McDermott, A. M. & Alsberg, E. Microcomputed tomography: approaches and applications in bioengineering. Stem Cell Res. Ther. 5, 144 (2014).

    Article  Google Scholar 

  19. Krüger, P. et al. Synchrotron X-ray tomography for investigations of water distribution in polymer electrolyte membrane fuel cells. J. Power Sources 196, 5250–5255 (2011).

    Article  Google Scholar 

  20. Fazeli, M. et al. Pore network modeling to explore the effects of compression on multiphase transport in polymer electrolyte membrane fuel cell gas diffusion layers. J. Power Sources 335, 162–171 (2016).

    Article  CAS  Google Scholar 

  21. Alrwashdeh, S. S. et al. In operando quantification of three-dimensional water distribution in nanoporous carbon-based layers in polymer electrolyte membrane fuel cells. ACS Nano 11, 5944–5949 (2017).

    Article  CAS  Google Scholar 

  22. Rawson, S. D., Maksimcuka, J., Withers, P. J. & Cartmell, S. H. X-ray computed tomography in life sciences. BMC Biol. 18, 21 (2020).

    Article  Google Scholar 

  23. Elliott, J. C. & Dover, S. D. X-ray microtomography. J. Microsc. 126, 211–213 (1982).

    Article  CAS  Google Scholar 

  24. Kress, J. W. & Feldkamp, L. A. "X-ray tomography applied to NDE of ceramics. In Proc. ASME 1983 International Gas Turbine Conference and Exhibit. Volume 5: Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education; Process Industries V005T11A003 (American Society of Mechanical Engineers, 1983); https://doi.org/10.1115/83-GT-206

  25. Stock, S. R. X-ray microtomography of materials. Int. Mater. Rev. 44, 141–164 (1999).

    Article  CAS  Google Scholar 

  26. Grodzins, L. Critical absorption tomography of small samples. Nucl. Instrum. Methods Phys. Res. 206, 547–552 (1983).

    Article  CAS  Google Scholar 

  27. Grodzins, L. Optimum energies for X-ray transmission tomography of small samples. Nucl. Instrum. Methods Phys. Res. 206, 541–545 (1983).

    Article  CAS  Google Scholar 

  28. Thompson, A. C. et al. Computed tomography using synchrotron radiation. Nucl. Instrum. Methods Phys. Res. 222, 319–323 (1984).

    Article  Google Scholar 

  29. Heiken, J. P., Brink, J. A. & Vannier, M. W. Spiral (helical) CT. Radiology 189, 647–656 (1993).

    Article  CAS  Google Scholar 

  30. Kalender, W. A., Seissler, W., Klotz, E. & Vock, P. in Classic Papers in Modern Diagnostic Radiology (eds Adrian, M. K. et al.) 1–68 (Springer, 2005).

  31. Polacin, A., Kalender, W. A. & Marchal, G. Evaluation of section sensitivity profiles and image noise in spiral CT. Radiology 185, 29–35 (1992).

    Article  CAS  Google Scholar 

  32. Rubin, G. D., Leung, A. N., Robertson, V. J. & Stark, P. Thoracic spiral CT: influence of subsecond gantry rotation on image quality. Radiology 208, 771–776 (1998).

    Article  CAS  Google Scholar 

  33. Hu, H., He, H. D., Foley, W. D. & Fox, S. H. Four multidetector-row helical CT: image quality and volume coverage speed. Radiology 215, 55–62 (2000).

    Article  CAS  Google Scholar 

  34. Ballabriga, R. et al. Photon counting detectors for X-ray imaging with emphasis on CT. IEEE Trans. Radiat. Plasma Med. Sci. 5, 422–440 (2021).

    Article  Google Scholar 

  35. Kruth, J. P. et al. Computed tomography for dimensional metrology. CIRP Ann. 60, 821–842 (2011).

    Article  Google Scholar 

  36. Steinbock, L. & Dustmann, C.-H. Investigation of the inner structures of ZEBRA cells with a microtomograph. J. Electrochem. Soc. 148, A132 (2001).

    Article  CAS  Google Scholar 

  37. Sinha, P. K., Halleck, P. & Wang, C.-Y. Quantification of liquid water saturation in a PEM fuel cell diffusion medium using X-ray microtomography. Electrochem. Solid State Lett. 9, A344 (2006).

    Article  CAS  Google Scholar 

  38. Shearing, P. R., Howard, L. E., Jørgensen, P. S., Brandon, N. P. & Harris, S. J. Characterization of the 3-dimensional microstructure of a graphite negative electrode from a Li-ion battery. Electrochem. Commun. 12, 374–377 (2010).

    Article  CAS  Google Scholar 

  39. Hutzenlaub, T., Thiele, S., Zengerle, R. & Ziegler, C. Three-dimensional reconstruction of a LiCoO2 Li-ion battery cathode. Electrochem. Solid State Lett. 15, A33 (2011).

    Article  Google Scholar 

  40. Yan, B., Lim, C., Yin, L. & Zhu, L. Three dimensional simulation of galvanostatic discharge of LiCoO2 cathode based on X-ray nano-CT images. J. Electrochem. Soc. 159, A1604 (2012).

    Article  CAS  Google Scholar 

  41. Ebner, M., Marone, F., Stampanoni, M. & Wood, V. Visualization and quantification of electrochemical and mechanical degradation in Li ion batteries. Science 342, 716–720 (2013).

    Article  CAS  Google Scholar 

  42. Loveridge, M. et al. Looking deeper into the Galaxy (Note 7). Batteries 4, 3 (2018).

    Article  Google Scholar 

  43. Carter, R., Huhman, B., Love, C. T. & Zenyuk, I. V. X-ray computed tomography comparison of individual and parallel assembled commercial lithium iron phosphate batteries at end of life after high rate cycling. J. Power Sources 381, 46–55 (2018).

    Article  CAS  Google Scholar 

  44. Maire, E. & Withers, P. J. Quantitative X-ray tomography. Int. Mater. Rev. 59, 1–43 (2014).

    Article  CAS  Google Scholar 

  45. Gelb, J., Finegan, D. P., Brett, D. J. L. & Shearing, P. R. Multi-scale 3D investigations of a commercial 18650 Li-ion battery with correlative electron- and X-ray microscopy. J. Power Sources 357, 77–86 (2017).

    Article  CAS  Google Scholar 

  46. Finegan, D. P. et al. Investigating lithium-ion battery materials during overcharge-induced thermal runaway: an operando and multi-scale X-ray CT study. Phys. Chem. Chem. Phys. 18, 30912–30919 (2016).

    Article  CAS  Google Scholar 

  47. Gelb, J. et al. Energy tunability in laboratory 3D nano-XRM. Microsc. Microanal. 25, 388–389 (2019).

    Article  Google Scholar 

  48. 2022 NSLS-II Strategic Plan (Brookhaven National Laboratory, 2021).

  49. Chenevier, D. & Joly, A. ESRF: inside the extremely brilliant source upgrade. Synchrotron Radiat. News 31, 32–35 (2018).

    Article  Google Scholar 

  50. Rack, A. Hard X-ray imaging at ESRF: exploiting contrast and coherence with the new EBS storage ring. Synchrotron Radiat. News 33, 20–28 (2020).

    Article  Google Scholar 

  51. Meirer, F. et al. Three-dimensional imaging of chemical phase transformations at the nanoscale with full-field transmission X-ray microscopy. J. Synchrotron Radiat. 18, 773–781 (2011).

    Article  CAS  Google Scholar 

  52. Müller, S. et al. Multimodal nanoscale tomographic imaging for battery electrodes. Adv. Energy Mater. 10, 1904119 (2020).

    Article  Google Scholar 

  53. Falch, K. V. et al. Zernike phase contrast in high-energy X-ray transmission microscopy based on refractive optics. Ultramicroscopy 184, 267–273 (2018).

    Article  CAS  Google Scholar 

  54. Withers, P. J. X-ray nanotomography. Mater. Today 10, 26–34 (2007).

    Article  CAS  Google Scholar 

  55. Yin, L. et al. High performance printed AgO–Zn rechargeable battery for flexible electronics. Joule 5, 228–248 (2021).

    Article  CAS  Google Scholar 

  56. Pietsch, P. et al. Quantifying microstructural dynamics and electrochemical activity of graphite and silicon–graphite lithium ion battery anodes. Nat. Commun. 7, 12909 (2016).

    Article  CAS  Google Scholar 

  57. Taiwo, O. O. et al. Microstructural degradation of silicon electrodes during lithiation observed via operando X-ray tomographic imaging. J. Power Sources 342, 904–912 (2017).

    Article  CAS  Google Scholar 

  58. Gonzalez, J. et al. Three dimensional studies of particle failure in silicon based composite electrodes for lithium ion batteries. J. Power Sources 269, 334–343 (2014).

    Article  CAS  Google Scholar 

  59. Vanpeene, V. et al. Dynamics of the morphological degradation of Si‐based anodes for Li‐ion batteries characterized by in situ synchrotron X‐ray tomography. Adv. Energy Mater. 9, 1803947 (2019).

    Article  Google Scholar 

  60. Gent, W. E. et al. Persistent state-of-charge heterogeneity in relaxed, partially charged Li1−xNi1/3Co1/3Mn1/3O2 secondary particles. Adv. Mater. 28, 6631–6638 (2016).

    Article  CAS  Google Scholar 

  61. Holzner, C. et al. Zernike phase contrast in scanning microscopy with X-rays. Nat. Phys. 6, 883–887 (2010).

    Article  CAS  Google Scholar 

  62. Komini Babu, S., Mohamed, A. I., Whitacre, J. F. & Litster, S. Multiple imaging mode X-ray computed tomography for distinguishing active and inactive phases in lithium-ion battery cathodes. J. Power Sources 283, 314–319 (2015).

    Article  CAS  Google Scholar 

  63. Chen-Wiegart, Y. K., Liu, Z., Faber, K. T., Barnett, S. A. & Wang, J. 3D analysis of a LiCoO2–Li(Ni1/3Mn1/3Co1/3)O2 Li-ion battery positive electrode using X-ray nano-tomography. Electrochem. Commun. 28, 127–130 (2013).

    Article  CAS  Google Scholar 

  64. Heenan, T. M. M. et al. Resolving Li‐ion battery electrode particles using rapid lab‐based X‐ray nano‐computed tomography for high‐throughput quantification. Adv. Sci. 7, 2000362 (2020).

    Article  CAS  Google Scholar 

  65. Frisco, S., Kumar, A., Whitacre, J. F. & Litster, S. Understanding Li-ion battery anode degradation and pore morphological changes through nano-resolution X-ray computed tomography. J. Electrochem. Soc. 163, A2636–A2640 (2016).

    Article  CAS  Google Scholar 

  66. Su, Z. et al. X-ray nanocomputed tomography in Zernike phase contrast for studying 3D morphology of Li–O2 battery electrode. ACS Appl. Energy Mater. 3, 4093–4102 (2020).

    Article  CAS  Google Scholar 

  67. Ngandjong, A. C. et al. Investigating electrode calendering and its impact on electrochemical performance by means of a new discrete element method model: towards a digital twin of Li-Ion battery manufacturing. J. Power Sources 485, 229320 (2021).

    Article  CAS  Google Scholar 

  68. Torayev, A. et al. Stochasticity of pores interconnectivity in Li–O2 batteries and its impact on the variations in electrochemical performance. J. Phys. Chem. Lett. 9, 791–797 (2018).

    Article  CAS  Google Scholar 

  69. Torayev, A., Magusin, P. C. M. M., Grey, C. P., Merlet, C. & Franco, A. A. Importance of incorporating explicit 3D-resolved electrode mesostructures in Li–O2 battery models. ACS Appl. Energy Mater. 1, 6433–6441 (2018).

    Article  Google Scholar 

  70. Ding, N. et al. Influence of carbon pore size on the discharge capacity of Li–O 2 batteries. J. Mater. Chem. A 2, 12433–12441 (2014).

    Article  CAS  Google Scholar 

  71. Epstein, N. On tortuosity and the tortuosity factor in flow and diffusion through porous media. Chem. Eng. Sci. 44, 777–779 (1989).

    Article  CAS  Google Scholar 

  72. Tjaden, B., Brett, D. J. L. & Shearing, P. R. Tortuosity in electrochemical devices: a review of calculation approaches. Int. Mater. Rev. 63, 47–67 (2018).

    Article  CAS  Google Scholar 

  73. Lu, X. et al. 3D microstructure design of lithium-ion battery electrodes assisted by X-ray nano-computed tomography and modelling. Nat. Commun. 11, 2079 (2020).

    Article  CAS  Google Scholar 

  74. Usseglio-Viretta, F. L. E. et al. Quantitative relationships between pore tortuosity, pore topology, and solid particle morphology using a novel discrete particle size algorithm. J. Electrochem. Soc. 167, 100513 (2020).

    Article  CAS  Google Scholar 

  75. Ebner, M., Chung, D.-W., García, R. E. & Wood, V. Tortuosity anisotropy in lithium-ion battery electrodes. Adv. Energy Mater. 4, 1301278 (2014).

    Article  Google Scholar 

  76. Submicron X-ray Imaging: Maintain High Resolution Even at Large Working Distances: ZEISS Xradia Versa 510 (ZEISS, accessed 27 September 2020); https://asset-downloads.zeiss.com/catalogs/download/mic/59f564ec-a757-4f23-9607-b4ae6d91c05e/EN_product-info_Xradia-510-Versa_rel-1.2.pdf

  77. Varslot, T., Kingston, A., Myers, G. & Sheppard, A. High-resolution helical cone-beam micro-CT with theoretically-exact reconstruction from experimental data. Med. Phys. 38, 5459–5476 (2011).

    Article  CAS  Google Scholar 

  78. Li, T. et al. Three-dimensional reconstruction and analysis of all-solid Li-ion battery electrode using synchrotron transmission X-ray microscopy tomography. ACS Appl. Mater. Interfaces 10, 16927–16931 (2018).

    Article  CAS  Google Scholar 

  79. Ghorbani Kashkooli, A. et al. Synchrotron X-ray nano computed tomography based simulation of stress evolution in LiMn2O4 electrodes. Electrochim. Acta 247, 1103–1116 (2017).

    Article  CAS  Google Scholar 

  80. ZEISS Xradia 810 Ultra—Nanoscale X-ray Imaging: Explore at the Speed of Science (ZEISS, accessed 10 December 2020); https://asset-downloads.zeiss.com/catalogs/download/mic/c5e5bd17-4f66-42df-a4b3-18ecd933024e/EN_product-info_Xradia-810-Ultra_rel3.0.pdf

  81. Frisco, S. et al. Internal morphologies of cycled Li–metal electrodes investigated by nano-scale resolution X-ray computed tomography. ACS Appl. Mater. Interfaces 9, 18748–18757 (2017).

    Article  CAS  Google Scholar 

  82. Yermukhambetova, A. et al. Exploring 3D microstructural evolution in Li–sulfur battery electrodes using in-situ X-ray tomography. Sci. Rep. 6, 35291 (2016).

    Article  CAS  Google Scholar 

  83. Yufit, V. et al. Investigation of lithium-ion polymer battery cell failure using X-ray computed tomography. Electrochem. Commun. 13, 608–610 (2011).

    Article  CAS  Google Scholar 

  84. Taiwo, O. O. et al. Investigating the evolving microstructure of lithium metal electrodes in 3D using X-ray computed tomography. Phys. Chem. Chem. Phys. 19, 22111–22120 (2017).

    Article  CAS  Google Scholar 

  85. Ito, Y., Wei, X., Desai, D., Steingart, D. & Banerjee, S. An indicator of zinc morphology transition in flowing alkaline electrolyte. J. Power Sources 211, 119–128 (2012).

    Article  CAS  Google Scholar 

  86. Ko, J. S. et al. Robust 3D Zn sponges enable high-power, energy-dense alkaline batteries. ACS Appl. Energy Mater. 2, 212–216 (2019).

    Article  CAS  Google Scholar 

  87. Mitsch, T. et al. Preparation and characterization of Li-ion graphite anodes using synchrotron tomography. Materials 7, 4455–4472 (2014).

    Article  Google Scholar 

  88. Arlt, T., Schröder, D., Krewer, U. & Manke, I. In operando monitoring of the state of charge and species distribution in zinc air batteries using X-ray tomography and model-based simulations. Phys. Chem. Chem. Phys. 16, 22273–22280 (2014).

    Article  CAS  Google Scholar 

  89. Yu, Y.-S. et al. Three-dimensional localization of nanoscale battery reactions using soft X-ray tomography. Nat. Commun. 9, 921 (2018).

    Article  Google Scholar 

  90. Tonin, G. et al. Operando investigation of the lithium/sulfur battery system by coupled X-ray absorption tomography and X-ray diffraction computed tomography. J. Power Sources 468, 228287 (2020).

    Article  CAS  Google Scholar 

  91. Lewis, J. A. et al. Linking void and interphase evolution to electrochemistry in solid-state batteries using operando X-ray tomography. Nat. Mater. 20, 503–510 (2021).

    Article  CAS  Google Scholar 

  92. Wang, J., Chen-Wiegart, Y. K. & Wang, J. In situ three-dimensional synchrotron X-ray nanotomography of the (de)lithiation processes in tin anodes. Angew. Chem. Int. Ed. 53, 4460–4464 (2014).

    Article  CAS  Google Scholar 

  93. Vanpeene, V. et al. Monitoring the morphological changes of Si-based electrodes by X-ray computed tomography: a 4D-multiscale approach. Nano Energy 74, 104848 (2020).

    Article  CAS  Google Scholar 

  94. Christensen, M. K., Mathiesen, J. K., Simonsen, S. B. & Norby, P. Transformation and migration in secondary zinc–air batteries studied by in situ synchrotron X-ray diffraction and X-ray tomography. J. Mater. Chem. A 7, 6459–6466 (2019).

    Article  CAS  Google Scholar 

  95. Choi, P., Parimalam, B. S., Su, L., Reeja-Jayan, B. & Litster, S. Operando particle-scale characterization of silicon anode degradation during cycling by ultrahigh-resolution X-ray microscopy and computed tomography. ACS Appl. Energy Mater. 4, 1657–1665 (2021).

    Article  CAS  Google Scholar 

  96. Jervis, R. et al. In situ compression and X-ray computed tomography of flow battery electrodes. J. Energy Chem. 27, 1353–1361 (2018).

    Article  Google Scholar 

  97. Doux, J. et al. Stack pressure considerations for room‐temperature all‐solid‐state lithium metal batteries. Adv. Energy Mater. 10, 1903253 (2020).

    Article  CAS  Google Scholar 

  98. Franke-lang, R., Arlt, T., Manke, I. & Kowal, J. X-ray tomography as a powerful method for zinc–air battery research. J. Power Sources 370, 45–51 (2017).

    Article  CAS  Google Scholar 

  99. Tippens, J. et al. Visualizing chemomechanical degradation of a solid-state battery electrolyte. ACS Energy Lett. 4, 1475–1483 (2019).

    Article  CAS  Google Scholar 

  100. Scharf, J. et al. Investigating degradation modes in Zn–AgO aqueous batteries with in situ X‐ray micro computed tomography. Adv. Energy Mater. https://doi.org/10.1002/aenm.202101327 (2021).

  101. Lu, B. et al. Quantitatively designing porous copper current collectors for lithium metal anodes. ACS Appl. Energy Mater. 4, 6454–6465 (2021).

    Article  CAS  Google Scholar 

  102. Flannery, B. P., Deckman, H. W., Roberge, W. G. & D’amico, K. L. Three-dimensional X-ray microtomography. Science 237, 1439–1444 (1987).

    Article  CAS  Google Scholar 

  103. Daemi, S. R. et al. 4D visualisation of in situ nano-compression of Li-ion cathode materials to mimic early stage calendering. Mater. Horiz. 6, 612–617 (2019).

    Article  CAS  Google Scholar 

  104. Hubbell, J. H. & Seltzer, S. M. Tables of X-Ray Mass Attenuation Coefficients and Mass Energy-Absorption Coefficients 1 keV to 20 meV for Elements Z = 1 to 92 and 48 Additional Substances of Dosimetric Interest (NIST, accessed 18 February 2021); https://www.osti.gov/biblio/76335

  105. Usseglio-Viretta, F. L. E. et al. Resolving the discrepancy in tortuosity factor estimation for Li-ion battery electrodes through micro–macro modeling and experiment. J. Electrochem. Soc. 165, A3403–A3426 (2018).

    Article  CAS  Google Scholar 

  106. Bailey, J. J. et al. Laser-preparation of geometrically optimised samples for X-ray nano-CT. J. Microsc. 267, 384–396 (2017).

    Article  CAS  Google Scholar 

  107. Tan, C. et al. Evolution of electrochemical cell designs for in-situ and operando 3D characterization. Materials 11, 2157 (2018).

    Article  Google Scholar 

  108. Ho, A. S. et al. 3D detection of lithiation and lithium plating in graphite anodes during fast charging. ACS Nano 15, 10480–10487 (2021).

    Article  Google Scholar 

  109. Nelson, J. et al. Identifying and managing radiation damage during in situ transmission X-ray microscopy of Li-ion batteries. Proc. SPIE 8851, 88510B (2013).

    Article  Google Scholar 

  110. Borkiewicz, O. J., Wiaderek, K. M., Chupas, P. J. & Chapman, K. W. Best practices for operando battery experiments: influences of X-ray experiment design on observed electrochemical reactivity. J. Phys. Chem. Lett. 6, 2081–2085 (2015).

    Article  CAS  Google Scholar 

  111. Kulkarni, D., Normile, S. J., Connolly, L. G. & Zenyuk, I. V. Development of low temperature fuel cell holders for operando X-ray micro and nano computed tomography to visualize water distribution. J. Phys. Energy 2, 044005 (2020).

    Article  CAS  Google Scholar 

  112. Finegan, D. P. et al. Spatial dynamics of lithiation and lithium plating during high-rate operation of graphite electrodes. Energy Environ. Sci. 13, 2570–2584 (2020).

    Article  CAS  Google Scholar 

  113. Pietsch, P., Hess, M., Ludwig, W., Eller, J. & Wood, V. Combining operando synchrotron X-ray tomographic microscopy and scanning X-ray diffraction to study lithium ion batteries. Sci. Rep. 6, 27994 (2016).

    Article  CAS  Google Scholar 

  114. Finegan, D. P. et al. Spatially resolving lithiation in silicon–graphite composite electrodes via in situ high-energy X-ray diffraction computed tomography. Nano Lett. 19, 3811–3820 (2019).

    Article  CAS  Google Scholar 

  115. Doyle, M., Fuller, T. F. & Newman, J. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. J. Electrochem. Soc. 140, 1526–1533 (1993).

    Article  CAS  Google Scholar 

  116. Newman, J. S. & Tobias, C. W. Theoretical analysis of current distribution in porous electrodes. J. Electrochem. Soc. 109, 1183 (1962).

    Article  CAS  Google Scholar 

  117. Landesfeind, J., Ebner, M., Eldiven, A., Wood, V. & Gasteiger, H. A. Tortuosity of battery electrodes: validation of impedance-derived values and critical comparison with 3D tomography. J. Electrochem. Soc. 165, A469–A476 (2018).

    Article  CAS  Google Scholar 

  118. Trembacki, B. L. et al. Editors’ choice—mesoscale analysis of conductive binder domain morphology in lithium-ion battery electrodes. J. Electrochem. Soc. 165, E725–E736 (2018).

    Article  CAS  Google Scholar 

  119. Duquesnoy, M., Lombardo, T., Chouchane, M., Primo, E. N. & Franco, A. A. Data-driven assessment of electrode calendering process by combining experimental results, in silico mesostructures generation and machine learning. J. Power Sources 480, 229103 (2020).

    Article  CAS  Google Scholar 

  120. Chouchane, M., Rucci, A. & Franco, A. A. A versatile and efficient voxelization-based meshing algorithm of multiple phases. ACS Omega 4, 11141–11144 (2019).

    Article  CAS  Google Scholar 

  121. Cooper, S. J., Bertei, A., Shearing, P. R., Kilner, J. A. & Brandon, N. P. TauFactor: an open-source application for calculating tortuosity factors from tomographic data. SoftwareX 5, 203–210 (2016).

    Article  Google Scholar 

  122. Westhoff, D. et al. Parametric stochastic 3D model for the microstructure of anodes in lithium-ion power cells. Comput. Mater. Sci. 126, 453–467 (2017).

    Article  CAS  Google Scholar 

  123. Kashkooli, A. G. et al. Multiscale modeling of lithium-ion battery electrodes based on nano-scale X-ray computed tomography. J. Power Sources 307, 496–509 (2016).

    Article  CAS  Google Scholar 

  124. Yan, B., Lim, C., Yin, L. & Zhu, L. Simulation of heat generation in a reconstructed LiCoO2 cathode during galvanostatic discharge. Electrochim. Acta 100, 171–179 (2013).

    Article  CAS  Google Scholar 

  125. Ngandjong, A. C. et al. Multiscale simulation platform linking lithium ion battery electrode fabrication process with performance at the cell level. J. Phys. Chem. Lett. 8, 5966–5972 (2017).

    Article  CAS  Google Scholar 

  126. Roberts, S. A., Brunini, V. E., Long, K. N. & Grillet, A. M. A framework for three-dimensional mesoscale modeling of anisotropic swelling and mechanical deformation in lithium-ion electrodes. J. Electrochem. Soc. 161, F3052–F3059 (2014).

    Article  CAS  Google Scholar 

  127. Ferraro, M. E., Trembacki, B. L., Brunini, V. E., Noble, D. R. & Roberts, S. A. Electrode mesoscale as a collection of particles: coupled electrochemical and mechanical analysis of NMC cathodes. J. Electrochem. Soc. 167, 013543 (2020).

    Article  Google Scholar 

  128. Chouchane, M., Primo, E. N. & Franco, A. A. Mesoscale effects in the extraction of the solid-state lithium diffusion coefficient values of battery active materials: physical insights from 3D modeling. J. Phys. Chem. Lett. 11, 2775–2780 (2020).

    Article  CAS  Google Scholar 

  129. Danner, T. et al. Thick electrodes for Li-ion batteries: a model based analysis. J. Power Sources 334, 191–201 (2016).

    Article  CAS  Google Scholar 

  130. Chouchane, M. & Franco, A. A. Deconvoluting the impacts of the active material skeleton and the inactive phase morphology on the performance of lithium ion battery electrodes. Energy Storage Mater. 47, 649–655 (2022).

    Article  Google Scholar 

  131. Lim, C., Yan, B., Yin, L. & Zhu, L. Simulation of diffusion-induced stress using reconstructed electrodes particle structures generated by micro/nano-CT. Electrochim. Acta 75, 279–287 (2012).

    Article  CAS  Google Scholar 

  132. Qiu, G. et al. 3-D pore-scale resolved model for coupled species/charge/fluid transport in a vanadium redox flow battery. Electrochim. Acta 64, 46–64 (2012).

    Article  CAS  Google Scholar 

  133. Zhang, D. et al. The effect of wetting area in carbon paper electrode on the performance of vanadium redox flow batteries: a three-dimensional lattice Boltzmann study. Electrochim. Acta 283, 1806–1819 (2018).

    Article  CAS  Google Scholar 

  134. Wang, M. et al. Numerical evaluation of the effect of mesopore microstructure for carbon electrode in flow battery. J. Power Sources 424, 27–34 (2019).

    Article  CAS  Google Scholar 

  135. Zhang, D. et al. Understanding the role of the porous electrode microstructure in redox flow battery performance using an experimentally validated 3D pore-scale lattice Boltzmann model. J. Power Sources 447, 227249 (2020).

    Article  CAS  Google Scholar 

  136. Harris, W. M. & Chiu, W. K. S. Determining the representative volume element size for three-dimensional microstructural material characterization. Part 1: predictive models. J. Power Sources 282, 552–561 (2015).

    Article  CAS  Google Scholar 

  137. Harris, W. M. & Chiu, W. K. S. Determining the representative volume element size for three-dimensional microstructural material characterization. Part 2: application to experimental data. J. Power Sources 282, 622–629 (2015).

    Article  CAS  Google Scholar 

  138. Roberts, S. A., Mendoza, H., Brunini, V. E. & Noble, D. R. A verified conformal decomposition finite element method for implicit, many-material geometries. J. Comput. Phys. 375, 352–367 (2018).

    Article  Google Scholar 

  139. Landstorfer, M., Prifling, B. & Schmidt, V. Mesh generation for periodic 3D microstructure models and computation of effective properties. J. Comput. Phys. 431, 110071 (2021).

    Article  Google Scholar 

  140. Chouchane, M. & Franco, A. A. An invitation to engage with computational modeling: user‐friendly tool for in silico battery component generation and meshing. Batter. Supercaps 4, 1451–1456 (2021).

    Article  Google Scholar 

  141. Chouchane, M., Rucci, A., Lombardo, T., Ngandjong, A. C. & Franco, A. A. Lithium ion battery electrodes predicted from manufacturing simulations: Assessing the impact of the carbon-binder spatial location on the electrochemical performance. J. Power Sources 444, 227285 (2019).

    Article  CAS  Google Scholar 

  142. Lu, X. et al. Microstructural evolution of battery electrodes during calendering. Joule 4, 2746–2768 (2020).

    Article  CAS  Google Scholar 

  143. Lombardo, T. et al. Accelerated optimization methods for force-field parametrization in battery electrode manufacturing modeling. Batteries Supercap. 3, 721–730 (2020).

    Article  CAS  Google Scholar 

  144. Lombardo, T., Ngandjong, A. C., Belhcen, A. & Franco, A. A. Carbon-binder migration: a three-dimensional drying model for lithium-ion battery electrodes. Energy Storage Mater. 43, 337–347 (2021).

    Article  Google Scholar 

  145. Lombardo, T. et al. The ARTISTIC online calculator: exploring the impact of lithium‐ion battery electrode manufacturing parameters interactively through your browser. Batter. Supercaps 5, e202100324 (2022).

    Google Scholar 

  146. Burnett, T. L. et al. Correlative tomography. Sci. Rep. 4, 4711 (2015).

    Article  Google Scholar 

  147. Slater, T. J. et al. Multiscale correlative tomography: an investigation of creep cavitation in 316 stainless steel. Sci. Adv. 7, 7332 (2017).

    CAS  Google Scholar 

  148. Apeleo Zubiri, B. et al. Correlative laboratory nano-CT and 360° electron tomography of macropore structures in hierarchical zeolites. Adv. Mater. Interfaces 8, 2001154 (2021).

    Article  CAS  Google Scholar 

  149. Daemi, S. R. et al. Visualizing the carbon binder phase of battery electrodes in three dimensions. ACS Appl. Energy Mater. 1, 3702–3710 (2018).

    Article  CAS  Google Scholar 

  150. Xu, H., Usseglio-Viretta, F., Kench, S., Cooper, S. J. & Finegan, D. P. Microstructure reconstruction of battery polymer separators by fusing 2D and 3D image data for transport property analysis. J. Power Sources 480, 229101 (2020).

    Article  CAS  Google Scholar 

  151. Kench, S. & Cooper, S. J. Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion. Nat. Mach. Intell. 3, 299–305 (2021).

    Article  Google Scholar 

  152. Franco, A. A. Escape from flatland. Nat. Mach. Intell. 3, 277–278 (2021).

    Article  Google Scholar 

  153. De Carlo, F. et al. TomoBank: a tomographic data repository for computational X-ray science. Meas. Sci. Technol. 29, 034004 (2018).

    Article  Google Scholar 

  154. Quinn, A. et al. Electron backscatter diffraction for investigating lithium-ion electrode particle architectures. Cell Rep. Phys. Sci. 1, 100137 (2020).

    Article  CAS  Google Scholar 

  155. Urban, A., Seo, D.-H. & Ceder, G. Computational understanding of Li-ion batteries. npj Comput. Mater. 2, 16002 (2016).

    Article  CAS  Google Scholar 

  156. Xu, H. et al. Guiding the design of heterogeneous electrode microstructures for Li-ion batteries: microscopic imaging, predictive modeling, and machine learning. Adv. Energy Mater. 11, 2003908 (2021).

    Article  CAS  Google Scholar 

  157. Franco, A. A. et al. Entering the augmented era: immersive and interactive virtual reality for battery education and research. Batter. Supercaps 3, 1147–1164 (2020).

    Article  Google Scholar 

  158. Andrade, V. D. et al. Fast X-ray nanotomography with sub-10 nm resolution as a powerful imaging tool for nanotechnology and energy storage applications. Adv. Mater. 33, 2008653 (2021).

    Article  Google Scholar 

  159. Oikonomou, C. M., Chang, Y.-W. & Jensen, G. J. A new view into prokaryotic cell biology from electron cryotomography. Nat. Rev. Microbiol. 14, 205–220 (2016).

    Article  CAS  Google Scholar 

  160. Lee, J. Z. et al. Cryogenic focused ion beam characterization of lithium metal anodes. ACS Energy Lett. 4, 489–493 (2019).

    Article  CAS  Google Scholar 

  161. Seidman, D. N. Three-dimensional atom-probe tomography: advances and applications. Annu. Rev. Mater. Res. 37, 127–158 (2007).

    Article  CAS  Google Scholar 

  162. Borgia, G. C., Camaiti, M., Cerri, F., Fantazzini, P. & Piacenti, F. Study of water penetration in rock materials by nuclear magnetic resonance tomography: hydrophobic treatment effects. J. Cultural Herit. 1, 127–132 (2000).

    Article  Google Scholar 

  163. Walther, F. et al. Visualization of the interfacial decomposition of composite cathodes in argyrodite-based all-solid-state batteries using time-of-flight secondary-ion mass spectrometry. Chem. Mater. 31, 3745–3755 (2019).

    Article  CAS  Google Scholar 

  164. Schlüter, S., Sheppard, A., Brown, K. & Wildenschild, D. Image processing of multiphase images obtained via X-ray microtomography: a review. Water Resour. Res. 50, 3615–3639 (2014).

    Article  Google Scholar 

  165. Carvalho, L. E., Sobieranski, A. C. & von Wangenheim, A. 3D segmentation algorithms for computerized tomographic imaging: a systematic literature review. J. Digit. Imaging 31, 799–850 (2018).

    Article  CAS  Google Scholar 

  166. Arganda-Carreras, I. et al. Trainable WEKA segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33, 2424–2426 (2017).

    Article  CAS  Google Scholar 

  167. Satjaritanun, P. et al. Observation of preferential pathways for oxygen removal through porous transport layers of polymer electrolyte water electrolyzers. iScience 23, 101783 (2020).

    Article  CAS  Google Scholar 

  168. Serra, J. Image Analysis and Mathematical Morphology Vol. I (Academic Press, 1982).

  169. Ghani, M. U. et al. Noise power characteristics of a micro-computed tomography system. J. Comput. Assist. Tomogr. 41, 82–89 (2017).

    Article  Google Scholar 

  170. Orhan, K. Micro-Computed Tomography (Micro-CT) in Medicine and Engineering (Springer International, 2020).

  171. Barrett, J. F. & Keat, N. Artifacts in CT: recognition and avoidance. RadioGraphics 24, 1679–1691 (2004).

    Article  Google Scholar 

  172. Iassonov, P. & Tuller, M. Application of segmentation for correction of intensity bias in X-ray computed tomography images. Vadose Zone J. 9, 187 (2010).

    Article  Google Scholar 

  173. Tuller, M., Kulkarni, R. & Fink, W. in Soil–Water–Root Processes: Advances in Tomography and Imaging (eds Anderson, S. H. & Hopmans, J. W.) 157–182 (Soil Science Society of America, 2015).

  174. Perona, P. & Malik, J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990).

    Article  Google Scholar 

  175. Russ, J. C., Matey, J. R., Mallinckrodt, A. J. & McKay, S. The image processing handbook. Microsc. Microanal. 17, 843 (2011).

    Google Scholar 

  176. Gonzalez, R. C., Woods, R. E. & Eddins, S. Image segmentation. Digit. Image Process. 2, 331–390 (2002).

    Google Scholar 

Download references

Acknowledgements

We acknowledge funding support from the US Department of Energy, Office of Basic Energy Sciences, under award no. DE-SC0002357 (programme manager J. Zhu). A.A.F. and M.C. acknowledge the European Union’s Horizon 2020 research and innovation programme for funding support through the European Research Council (grant agreement 772873, ‘ARTISTIC’ project). A.A.F. acknowledges the Institut Universitaire de France for the support. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under contract no. DEAC36-08GO28308. Funding was provided by the US DOE Office of Vehicle Technology Extreme Fast Charge Program, programme manager S. Gillard. The views expressed in the article do not necessarily represent the views of the DOE or the US Government. We also acknowledge the support of M. Scharf in the design of the illustrations. For the collection of the zinc battery CT data, we acknowledge the National Center for Microscopy and Imaging Research (NCMIR) technologies and instrumentation supported by grant R24GM137200 from the National Institute of General Medical Sciences. The AgO and Zn used in this work were provided by Riot Energy Inc., and LiNi0.5Mn1.5O4 was supplied by Haldor Topsoe. We also acknowledge support for the LiNi0.5Mn1.5O4 electrode fabrication by the Ningbo Institute of Materials Technology and Engineering (NIMTE) in China. This work was performed in part at the San Diego Nanotechnology Infrastructure (SDNI) of UCSD, NANO3, a member of the National Nanotechnology Coordinated Infrastructure, which is supported by the National Science Foundation (grant ECCS-1542148).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Jonathan Scharf, Jean-Marie Doux or Ying Shirley Meng.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Nanotechnology thanks Iryna Zenyuk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Scharf, J., Chouchane, M., Finegan, D.P. et al. Bridging nano- and microscale X-ray tomography for battery research by leveraging artificial intelligence. Nat. Nanotechnol. 17, 446–459 (2022). https://doi.org/10.1038/s41565-022-01081-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41565-022-01081-9

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing