Researchers explore the transformation dynamics of cubic liquid crystals using direct simulation and machine learning, offering new possibilities for advanced materials development.
By unlocking a transformation between two types of structural defects on the surface of liquid droplets, the research opens new possibilities for controlling molecular patterns with unprecedented precision.
The proof-of-concept work was used to create diodes and transistors, and paves the way for self-assembling more complex electronic devices without relying on existing computer chip manufacturing techniques.
The technology offers a promising alternative to the cumbersome process currently used for monitoring brainwaves and diagnosing neurological conditions. It also has the potential to enhance non-invasive brain-computer interface applications.
This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.
Scientists built the tiniest walking robot ever, designed to navigate at visible light wavelengths. It can move independently to specific spots in tissue to image and measure microscopic structures.
Liquid-processed transistors achieve high performance at low temps, enabling flexible electronics and wearables through stable operation on bendable plastic substrates.
Imagine placing an object under a microscope and pressing a button to rearrange the surface atoms with atomic-scale precision. This once sci-fi scenario is now a reality.
