This Metamaterial Captures and Boosts Tiny Vibrations, Delivering Electricity for the IoT and More
Amplifying tiny everyday vibrations, this energy harvesting system could help lessen the power demand of the growing Internet of Things.
Researchers from the Korea Research Institute of Standards and Science (KRISS), Daejeon University of Science and Technology (UST), and Sungkyunkwan University have come developed a metamaterial that can capture vibrations and extract their energy — and hope it could be used to help power sensors for the Internet of Things (IoT) and medical wearables.
"This research," claims KRISS senior researcher Lee Hyung Kin, of the Institute's Acoustics, Ultrasound, and Vibration Metrology Group, "is the first in the world to successfully accumulate and amplify vibrations using a surface metamaterial that temporarily traps vibrations."
The core concept behind the team's work, harvesting energy that would otherwise be wasted, isn't new. Many remote nodes in the Internet of Things are powered by harvested energy, typically — but not exclusively — sunlight. The researchers have taken a different approach, though: their creation harnesses tiny everyday vibrations as its energy source.
The problem with tiny everyday vibrations, of course, is that they're tiny. The team's solution to this: a metamaterial that captures vibrations and amplifies them up to 45 times their original magnitude — then feeds that amplified vibration into a small number of piezoelectric elements to convert their energy into electricity.
The team claims the result is a device, which can deliver a fourfold improvement in energy harvested over its rivals, offering enough usable energy to be deployable in a range of scenarios including IoT building monitoring and compact wearable sensors for healthcare — or even directly as a sensor itself, turning its ability to amplify vibrations into a high-sensitivity detector of weak waves, the researchers suggest.
The team's work has been published under open-access terms in the journal Mechanical Systems and Signal Processing.