Dive into soft matter imaging: artificial intelligence-integrated electron microscopy
Source abstract
Abstract
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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Electron Microscopy Techniques and Applications · Machine Learning in Materials Science · Block Copolymer Self-Assembly
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
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