Scientists have developed micromagnetic robots to manage microflow-controlled samples more efficiently

Scientists working on microfluidtechnology often rely on large, clumsy machines to manage samples, but in the future this management can be left to micro-robots and can significantly improve processing efficiency.

Scientists have developed micromagnetic robots to manage microflow-controlled samples more efficiently

Engineers at the University of California, Los Angeles (UCLA) are working toward the future by developing a small set of disc-shaped machines that function like “warehouse robots” that can move and deposit tiny droplets with great precision.

Sam Emaminejad, senior author of the study, said: “We were inspired by the transformative impact of network mobile robotics systems on industries such as manufacturing, storage and logistics, such as Amazon storage systems for sorting and transporting packages. Therefore, we set out to achieve the same level of automation and mobility in a microfluidic environment. However, our “warehouse” is much smaller, about the size of your palm, and our cargo (droplets) is only a few tenths of a millimeter. “

The study was tested in an area of about the size of an index card (12.5cm x 9.5cm card) and was equipped with an internal structure and a test tray that could accommodate a small amount of liquid. The robot is about 2 mm (0.8 inches) in diameter and is operated by electromagnetic bricks integrated into the platform that drag them at a predetermined path of 10 cm (4 inches) per second.

“We set the time and position to open and close the tiles to guide the iron robots through their designated routes,” said Wenzhuo Yu, lead author of the paper. This allows us to have multiple robots working in the same space at a relatively fast pace to complete tasks efficiently. “

The team’s findings, published in the journal Science Robotics, show how the micro-robots are working in the motion picture below.

Scientists have developed micromagnetic robots to manage microflow-controlled samples more efficiently