Excessive heat from electronic or mechanical devices can indicate inefficiency. Engineers can use embedded sensors to monitor heat flow, enabling them to improve device performance. Researchers have developed a thin, real-time heat flow sensor using a novel thermoelectric phenomenon. This sensor is cost-effective and easy to manufacture, making it suitable for deep integration in devices where other sensors may not work. Managing thermal behavior is crucial for optimizing device efficiency and safety, especially for batteries and health-related applications.
The team, led by Project Associate Professor Tomoya Higo and Professor Satoru Nakatsuji from the University of Tokyo’s Department of Physics, aimed to address this challenge and improve device efficiency, safety, and health by measuring heat flux effectively. Traditional thermal diode devices only provide temperature values in specific areas, not a comprehensive image of heat flux across the entire surface.
The research team focused on exploring a heat flux sensor made of special magnetic materials and electrodes to understand complex heat flow patterns. The magnetic material, based on iron and gallium, exhibits the anomalous Nernst effect (ANE), converting heat energy into an electrical signal in an unusual manner.
Although the Seebeck effect can generate more electrical power, it requires large, brittle materials that are challenging to work with. In contrast, ANE allowed the team to create a flexible and thin sensor on a plastic sheet, using a special repeating pattern of magnetic and electrode materials.
The process involved sputtering magnetic and electrode layers onto a clear, strong, and lightweight PET plastic sheet and etching desired patterns, similar to making electronic circuits. The unique circuit design boosted ANE while suppressing the Seebeck effect, making the sensor highly efficient in real-time heat flux data output.
The potential applications of this sensor include power generation and data centers, where managing heat is essential for efficiency. In automated manufacturing environments, the sensor could predict machine failures and improve safety. Future developments might lead to internal medical applications, aiding doctors in producing heat maps of specific body areas or organs for imaging and diagnosis.
Source: University of Tokyo