Researchers from the University of Texas at Austin and other top universities have developed a new machine learning-based approach for creating complex thermal meta-emitters. These materials can emit heat at various levels in different manners, making them ideal for achieving energy efficiency through precise cooling and heating. In a recent test, one of these materials was used to coat a model house and compared with regular white and grey paints. After four hours of direct sunlight, the roof with the special coating was 5-20 degrees Celsius cooler than the roofs with regular paint. This level of cooling can save around 15,800 kilowatts per year in an apartment building in a hot climate.
The researchers have developed seven classes of meta-emitters, each with different strengths and applications. These thermal emitters can be used to mitigate temperature in metro cities by reflecting sunlight and emitting heat in specific wavelengths, reducing the urban heat island effect. They can also be used in daily use, such as in fabrics and textiles to improve cooling technology for clothing and outdoor equipment.
The slow and careful traditional way of designing these materials has stopped them from becoming widely used. However, with machine learning, researchers have created more than 1,500 different materials capable of emitting heat at various levels in different manners. The team will work on refining this technology and applying it to more aspects of nanophotonics, the interaction of light and matter at the tiniest scales.
Source: https://interestingengineering.com/innovation/us-thermal-coating-cools-buildings