“The information content in a piece of material can quickly exceed the total information content in the Library of Congress, which is about 20 terabytes,” said Eun-Ah Kim, professor of physics in the College of Arts and Sciences, who is at the forefront of both quantum materials research and harnessing the power of machine learning to analyze data from quantum material experiments. “The limited capacity of the traditional mode of analysis – largely manual – is quickly becoming the critical bottleneck,” Kim said.

Researchers have now designed a micro-sized artificial cilial system using platinum-based components that can control the movement of fluids at such a scale. The technology could someday enable low-cost, portable diagnostic devices for testing blood samples, manipulating cells or assisting in microfabrication processes.