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<p>Hierarchical self-assembly in soft materials generates complex nanostructures with critical applications ranging from biomedicine to energy storage systems. Advanced transmission electron microscopy (TEM) methodologies—including cryogenic-TEM, electron tomography, and liquid-phase TEM—have significantly advanced our ability to visualize these assemblies with unprecedented spatiotemporal resolution. This perspective examines how machine learning integration overcomes inherent limitations in TEM analysis of beam-sensitive soft materials, enabling automated feature extraction, 3D reconstruction, and dynamic process analysis. We propose future directions for creating integrated analytical platforms that predict material properties from imaging data, accelerating the development of next-generation functional materials for extreme environments and emerging global challenges.</p>

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Dive into soft matter imaging: artificial intelligence-integrated electron microscopy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月23日

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  • Junyeon Yoon,
  • Jun Ho Hwang,
  • Eunji Lee

摘要

Hierarchical self-assembly in soft materials generates complex nanostructures with critical applications ranging from biomedicine to energy storage systems. Advanced transmission electron microscopy (TEM) methodologies—including cryogenic-TEM, electron tomography, and liquid-phase TEM—have significantly advanced our ability to visualize these assemblies with unprecedented spatiotemporal resolution. This perspective examines how machine learning integration overcomes inherent limitations in TEM analysis of beam-sensitive soft materials, enabling automated feature extraction, 3D reconstruction, and dynamic process analysis. We propose future directions for creating integrated analytical platforms that predict material properties from imaging data, accelerating the development of next-generation functional materials for extreme environments and emerging global challenges.