Sun Tianxiao, Peng Robert, Li Wenlong, Liu Yijin
Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
McNeil High School, Austin, TX 78729, USA.
J Synchrotron Radiat. 2025 Mar 1;32(Pt 2):417-423. doi: 10.1107/S1600577524012293. Epub 2025 Feb 4.
Operando imaging techniques have become increasingly valuable in both battery research and manufacturing. However, the reliability of these methods can be compromised by instabilities in the imaging setup and operando cells, particularly when utilizing high-resolution imaging systems. The acquired imaging data often include features arising from both undesirable system vibrations and drift, as well as the scientifically relevant deformations occurring in the battery sample during cell operation. For meaningful analysis, it is crucial to distinguish and separately evaluate these two factors. To address these challenges, we employ a suite of advanced image-processing techniques. These include fast Fourier transform analysis in the frequency domain, power spectrum-based assessments for image quality, as well as rigid and non-rigid image-registration methods. These techniques allow us to identify and exclude blurred images, correct for displacements caused by motor vibrations and sample holder drift and, thus, prevent unwanted image artifacts from affecting subsequent analyses and interpretations. Additionally, we apply optical flow analysis to track the dynamic deformation of battery electrode materials during electrochemical cycling. This enables us to observe and quantify the evolving mechanical responses of the electrodes, offering deeper insights into battery degradation. Together, these methods ensure more accurate image analysis and enhance our understanding of the chemomechanical interplay in battery performance and longevity.
在电池研究和制造中，原位成像技术变得越来越有价值。然而，这些方法的可靠性可能会受到成像装置和原位电池不稳定性的影响，特别是在使用高分辨率成像系统时。采集到的成像数据通常包含由不良系统振动和漂移产生的特征，以及电池样品在电池运行过程中发生的与科学相关的变形。为了进行有意义的分析，区分并分别评估这两个因素至关重要。为了应对这些挑战，我们采用了一套先进的图像处理技术。这些技术包括频域中的快速傅里叶变换分析、基于功率谱的图像质量评估，以及刚性和非刚性图像配准方法。这些技术使我们能够识别并排除模糊图像，校正由电机振动和样品架漂移引起的位移，从而防止不需要的图像伪影影响后续的分析和解释。此外，我们应用光流分析来跟踪电化学循环过程中电池电极材料的动态变形。这使我们能够观察和量化电极不断变化的机械响应，从而更深入地了解电池的降解情况。这些方法共同确保了更准确的图像分析，并加深了我们对电池性能和寿命中化学机械相互作用的理解。
