Revealing microscopic dynamics: <i>in situ</i> liquid-phase TEM for live observations of soft materials and quantitative analysis <i>via</i> deep learning
Yangyang Sun, Xingyu Zhang, Rui Huang, Dahai Yang, Juyeong Kim, Junhao Chen, Edison Huixiang Ang, Mufan Li, Lin Li, Xiaohui Song
Abstract
liquid-phase TEM technology while integrating deep learning methodologies to comprehensively analyze the quantitative aspects of soft matter dynamics. This study centers on diverse phenomena, encompassing surfactant molecule nucleation, block copolymer behavior, confinement-driven self-assembly, and drying processes. Furthermore, deep learning techniques are employed to precisely analyze Ostwald ripening and digestive ripening dynamics. The outcomes of this study not only deepen the understanding of soft matter at its fundamental level but also serve as a pivotal foundation for developing innovative functional materials and cutting-edge devices.