‘Tree-on-a-Chip’ Serves As Natural Power Source For Small Robots [Video]


Engineers from Massachusetts Institute of Technology designed a chip that will provide power to small robots. Dubbed "Tree-on-a-Chip," it imitated the process of osmosis in plants.

Osmosis is a process that allows the distribution of nutrients in plants. Water travels from the roots of the plants to the leaves. It absorbs sugars produced by the leaves and becomes concentrated. Then, it goes down again to the roots. This process is cyclical.

Researchers had been known to replicate human tissues on chips to better study them. There were heart on a chip, lung on a chip, and even placenta on a chip. Today, engineers from MIT made a chip of nature, the "tree-on-a-chip". The cheap will serve as pump to make small robots move without the need for expensive parts, according to New Atlas.

The chip was composed of two malleable membranes representing the xylem and phloem. Another membrane was added where sugar cube was placed. The engineers fed water to the set up using a tube. The chip was able to pump water into a container without moving any parts for several days.

"Tree-on-a-chip" would serve as a hydraulic pumping mechanism for small robots. In the past, robots were mostly powered by batteries. Nowadays, small robots can run on water and sugar using the chip designed by MIT engineers. This would mean cheaper power source for small robots. In addition, as long as sugar is regularly added, the robot would continue moving, according to Science Daily.

Small robots are mostly used for medical purposes such as in surgical operations. Several aspects of health care had benefitted from the use of small robots. The "tree-on-a-chip" could lead to wider use of robots for medical and other purposes.

Results of the research study imply that a stable source of power would be available for small robots so that they could work non-stop when necessary.

© 2024 University Herald, All rights reserved. Do not reproduce without permission.
Join the Discussion
Real Time Analytics