System Bits: Feb. 19

Eco-friendly material for wireless IoT sensors
Researchers at Canada’s Simon Fraser University and in Switzerland collaborated on developing a wood-derived cellulose material that could be used in a 3D printer, instead of the customary plastic and polymeric materials for electronics. With 3D printing, the material can offer flexibility to add or embed functions onto 3D shapes or fabrics, the team notes.

“Our eco-friendly 3D printed cellulose sensors can wirelessly transmit data during their life, and then can be disposed without concern of environmental contamination,” says Woo Soo Kim, a professor in SFU’s School of Mechatronic Systems Engineering. “This development will help to advance green electronics. For example, the waste from printed circuit boards is a hazardous source of contamination to the environment. If we are able to change the plastics in PCBs to cellulose composite materials, recycling of metal components on the board could be collected in a much easier way.”

Kim’s research program involves working with scientists at the Swiss Federal Laboratories for Materials Science. He is also collaborating with a team of South Korean researchers from the Daegu Gyeongbuk Institute of Science and Technology’s department of Robotics Engineering, and PROTEM Co. Inc., a technology-based company, for the development of printable conductive ink materials.

This latter project involves embossing process technology, enabling imprinting fine circuit patterns on a flexible polymer substrate, which could be used making electronic products.

MIT team crafts sub-terahertz-radiation receiver
Massachusetts Institute of Technology researchers report development of a sub-terahertz-radiation receiving system that could be used in autonomous vehicles, operating in conditions, such as dust clouds and fog, that would challenge infrared-based LiDAR sensors. Sub-terahertz wavelengths are found between IR and microwave. A sub-terahertz imaging system detects objects by sending a signal through a transmitter and a receiver measures the absorption and reflection of the rebounding sub-terahertz wavelengths. This process sends a signal to a processor that recreates an image of the object.

The team came up with a 2D, sub-terahertz receiving array on a chip that’s more sensitive than the discrete components used in traditional systems. They used heterodyne detectors to form an array of independent signal-mixing pixels, shrunk from their usual size so they would fit on a single chip. Their prototype has a 32-pixel array integrated on a device measuring 1.2 square millimeter.

“A big motivation for this work is having better ‘electric eyes’ for autonomous vehicles and drones,” says co-author Ruonan Han, an associate professor of electrical engineering and computer science, and director of the Terahertz Integrated Electronics Group in the MIT Microsystems Technology Laboratories. “Our low-cost, on-chip sub-terahertz sensors will play a complementary role to LiDAR for when the environment is rough.”

First author Zhi Hu says, “We designed a multifunctional component for a [decentralized] design on a chip and combine a few discrete structures to shrink the size of each pixel. “Even though each pixel performs complicated operations, it keeps its compactness, so we can still have a large-scale dense array.”

Researching organic semiconductor materials
Scientists at the Technical University of Munich are using data mining to research organic semiconductor materials that could replace silicon as the basis for photovoltaic solar cells, displays, and light-emitting diodes. The team settled upon organic compounds building on frameworks of carbon atoms.

“To date, a major problem has been tracking them down: It takes weeks to months to synthesize, test and optimize new materials in the laboratory,” says Karsten Reuter, professor of theoretical chemistry at TUM. “Using computational screening, we can accelerate this process immensely.”

“Knowing what you are looking for is crucial in data mining,” says Dr. Harald Oberhofer, who heads the project. “In our case, it is electrical conductivity. High conductivity ensures, for example, that a lot of current flows in photovoltaic cells when sunlight excites the molecules.”

Oberhofer searched for very specific parameters with his data-mining algorithms, namely the coupling parameter of electrons and the reorganization parameter. The team went through 64,000 organic compounds with those parameters and grouped them into clusters, the carbon-based molecular frameworks and what they called the functional groups.

“We can now use this to not only predict the properties of a molecule, but using artificial intelligence we can also design new compounds in which both the structural framework and the functional groups promise very good conductivity,” Reuter notes.