Paper or Plastic?
Paper-based RadioGami sensors use a novel ultra-low power circuit to enable battery-free, long-range wireless communication for IoT devices.
Adding intelligence to homes, cities, and industry is going to require a lot of data collection in the years ahead. The AI algorithms that will drive innovation in these areas will rely heavily on high-quality, diverse, and relevant data to learn, improve, and make accurate predictions. Perhaps the best way to collect this data is to simply stick sensors on everything. And why not? The components are now inexpensive and small enough to make this possible.
But while it may be possible, it is not yet entirely practical. The biggest problems remaining are supplying these sensors with power and transmitting the results to external systems for analysis. Keeping many millions of tiny batteries charged up (and replaced as needed) would be a maintenance nightmare, and traditional wireless radios used for communication consume a lot of energy, ensuring that the batteries would not last long. Moreover, the environmental impacts of all of the discarded hardware could prove to be devastating over time.
A simpler and more environmentally-friendly path forward has recently been proposed by researchers at the University of Tennessee that could satisfy our thirst for data without the present worries. They have developed batteryless, long-range wireless paper sensors called RadioGami. Through the use of a novel ultra-low power circuit and energy harvesting techniques, the team has created practical sensors that can be sprinkled around everywhere they are needed.
Built from little more than paper, copper tape, and off-the-shelf electronic components, these paper-based sensors can wirelessly communicate over distances of up to 45.7 meters — a range far exceeding other batteryless devices of this kind. This was made possible with the help of a special electronic component known as a Tunnel Diode Oscillator (TDO), which allows the sensors to transmit data wirelessly while consuming as little as 35 microwatts of power.
Similar low-power systems often use backscatter communications, which rely on strong external radio sources to reflect signals back to a receiver. But RadioGami’s tunnel diode circuits generate their own radio frequency signals locally. This eliminates the need for powerful nearby transmitters and enables reliable operation over long distances.
The tunnel diode, operating within its unique negative differential resistance region, forms the heart of the circuit. By carefully tuning its biasing network, using resistors and inductors on a flexible paper substrate, the researchers achieved stable oscillations suitable for low-power radio transmission.
Power for the system comes entirely from ambient light. Tiny photodiodes convert available light into electricity, which is then stored in two supercapacitors. One capacitor maintains the precise voltage needed to keep the TDO oscillating, while the other provides short bursts of energy to an intermittent switching circuit that regulates power flow. The switching circuit, designed with subthreshold MOSFETs, is a clever addition that conserves energy by controlling when the oscillator is active, allowing the device to function even under dim lighting or during short interruptions in illumination.
The choice of paper as a substrate was made for more than just environmental reasons. Paper’s flexibility and foldability make it ideal for interactive and deformable interfaces. By combining origami-inspired designs such as Miura-Ori and Kresling folds, the team demonstrated that bending, tearing, or folding the paper changes the oscillator’s frequency, allowing for sensing of deformation and motion. In demonstrations, these paper sensors were used to detect sliding and rotation, monitor object status, and even sense environmental changes.
By combining ultra-low-power RF circuitry, energy harvesting, and paper-based fabrication, RadioGami brings the vision of large-scale, sustainable, and disposable sensing systems much closer to reality. This technology could ultimately serve an important role in supplying AI algorithms with the data they need to improve.