Researchers from Tokyo Metropolitan University have enhanced super-resolution machine learning techniques to study phase transitions. They identified key features of how large arrays of interacting particles behave at different temperatures by simulating tiny arrays before using a convolutional neural network to generate a good estimate of what a larger array would look like using correlation configurations. The massive saving in computational cost may realize unique ways of understanding how materials behave.
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Scientists from Tokyo Metropolitan University have unraveled how liquid foams collapse by visualizing individual collapse “events” using high-speed video microscopy.
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IMAGE: An initial crack in a film creates a RVPB (a). A second crack event in the film (b) causes a collapse front to be formed which sweeps up the RVPB. view more
Credit: Tokyo Metropolitan University
Tokyo, Japan - Researchers from Tokyo Metropolitan University have revealed how liquid foams collapse by observing individual collapse events with high-speed video microscopy. They found that cracks in films led to a receding liquid front which sweeps up the original film border, inverts its shape, and releases a droplet which hits and breaks other films. Their observations and physical model provide key insights into how to make foams more or less resistant to collapse.