Deep learning-based tracking of polymeric particle assembly
발표자
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초록
내용
The study of solution-state particles constitutes a critical component in understanding their reciprocal interactions. These are often concealed by fast Brownian motions and unpredictable solvent effects, posing significant challenges. Overcoming these challenges, real-time transmission electron microscopy (TEM) has surfaced as a powerful tool for visualizing the dynamics of these particles, offering the possibility to dissect their motion in a time-resolved manner. In an innovative approach, this research integrates deep learning methods into this imaging framework for image binarization which facilitates the analysis of particle trajectories. A notable direction is to generate a spatiotemporal representation of particles by distinguishing contrast variations in a 3D context. Furthermore, this framework not only presents significant potential for analyzing small molecule interactions, especially within the liquid phase, but also provides new insights into complex nanoscale dynamic systems.