The demand for technologies to cool buildings is increasing as climate change intensifies global temperature. A team of researchers report using an active learning scheme assisted by quantum computing to devise a layer of transparent window coating.
The system is a combination of machine learning, quantum annealing, and active data production tested for its cooling effect, as per AZO Materials. The aim of making this transparent window coating is to lower the temperature inside buildings without expending energy.
Quantum-Assisted Design of Transparent Window Coating
Previous studies reveal that cooling accounts for 15% of global energy consumption. Unfortunately, conventional clear windows do not help address the heat inside the buildings. But energy consumption could be reduced if the coating on glass windows radiates heat at a wavelength that passes through the atmosphere into outer space, Phys.org reported.
Since making these materials is quite challenging, especially because it can interfere with the view, Professor Tengfei Luo from the University of Notre Dame and colleagues set out to design a "transparent radiative cooler" (TRC) that can cool the room while not using energy, and at the same time do not block the view.
The TRC allows visible light to come inside but keeps the heat-producing light outside. Researchers estimate that it could reduce cooling costs by up to 31% or about one-third in hot climates compared to conventional glass windows.
According to AZO Materials, the TRC was composed of a planar multilayer (PML) photonic structure developed on a glass substrate with polydimethylsiloxane as the top layer. The purpose of the optimization process is to develop TRC that somehow resembles the ideal TRC in terms of wavelength-dependent optical characteristics.
Enhancing PML complexity along with the number of layers facilitated the development of a TRC with greater performance, leading to higher resolutions in varied refractive indices. The team used the needle technique together with thickness refinement to represent the optimization process.
They also used the traditional thin-film deposition methods, which were predicted using quantum annealing-assisted optimization for its desired structure, to construct the TRC. At the end of the process, they developed two PML samples in which one has been annealed and the other is not.
QA-Assisted Active Machine Learning Technique Developed High-Quality TRC
Upon testing their TRC samples, they observed that the irradiance transmitted through the optimal designs was significantly closer to the observed ideal TRC. Researchers noted that the transmission efficiency of the ideal structure was 0.6 and 0.10 within the wavelength of over 1400 nm.
Moreover, the TRC that had undergone annealing was more transparent and visually homogenous compared to the unannealed TRC. More so, the constructed TRC has a reduced transmission efficiency of only between 300 to 500 nm wavelength range.
The results highlighted that annealing enhanced the crystallinity of the film and that the TRC displayed near-unity emission efficiency. Researchers also discovered that their TRC has a better performance than the triple-layer silver coating (TLSC).
The experiment demonstrates the potential of the TRC in reducing cooling energy costs in buildings across the world. As Phys.org reported, TRC can also be used for buildings and cars to help address climate change.
They published their study, titled "High-Performance Transparent Radiative Cooler Designed by Quantum Computing," in ACS Energy Letters, a scientific journal published by the American Chemical Society.
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