Neural Network Analysis of Electron Microscopy Video Data Reveals the Temperature-Driven Microphase Dynamics in the...

Real-time field-emission scanning electron microscopy (FE-SEM) measurements and neural network analysis were successfully merged to observe the temperature-induced behavior of soft liquid microdomains in mixtures of different ionic liquids with water. The combination of liquid FE-SEM and in situ heating techniques revealed temperature-driven solution restructuring for ions/water systems with different water states and their critical point behavior expressed in a rapid switch between thermal expansion and shrinkage of liquid microphases at temperatures of ≈100-130 °C, which was directly recorded on electron microscopy videos. Automation of FE-SEM video analysis by a neural network approach allowed quantification of the morphological changes in ions/water systems during heating on the basis of thousands of images processed with a speed almost equal to the frame rate of original electron microscopy videos. Tracking and evolution of the micro-heterogeneous domains, hypothesized in the Ioliomics concept, was mapped and quantified for the first time. The present study describes the concept for quick acquisition of big data in electron microscopy, develops rapid neural network analysis and shows how to link microscopic data to fundamental molecular properties.