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Publication Detail
A greyscale erosion algorithm for tomography (GREAT) to rapidly detect battery particle defects
  • Publication Type:
    Journal article
  • Authors:
    Wade A, Heenan TMM, Kok M, Tranter T, Leach A, Tan C, Jervis R, Brett DJL, Shearing PR
  • Publisher:
  • Publication date:
  • Journal:
    npj Materials Degradation
  • Volume:
  • Issue:
  • Article number:
  • Status:
  • Language:
  • Keywords:
    Science & Technology, Technology, Materials Science, Multidisciplinary, Materials Science, LAYERED CATHODE MATERIALS, NI-RICH, LITHIUM, NMC, CELLS, DEGRADATION, MECHANISMS, FRACTURE, CHARGE, PHASE
  • Notes:
    This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Particle micro-cracking is a major source of performance loss within lithium-ion batteries, however early detection before full particle fracture is highly challenging, requiring time consuming high-resolution imaging with poor statistics. Here, various electrochemical cycling (e.g., voltage cut-off, cycle number, C-rate) has been conducted to study the degradation of Ni-rich NMC811 (LiNi0.8Mn0.1Co0.1O2) cathodes characterized using laboratory X-ray micro-computed tomography. An algorithm has been developed that calculates inter- and intra-particle density variations to produce integrity measurements for each secondary particle, individually. Hundreds of data points have been produced per electrochemical history from a relatively short period of characterization (ca. 1400 particles per day), an order of magnitude throughput improvement compared to conventional nano-scale analysis (ca. 130 particles per day). The particle integrity approximations correlated well with electrochemical capacity losses suggesting that the proposed algorithm permits the rapid detection of sub-particle defects with superior materials statistics not possible with conventional analysis.
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