[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120126-en":3,"doc-seo-120126-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120126,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","MACHINE-LEARNING DESIGNED SMART COATING - TEMPERATURE-DEPENDENT SELF-ADAPTATION BETWEEN A SOLAR ABSORBER AND A RADIATIVE COOLER","We designed a multilayer self-adaptive absorber/emitter metamaterial that switches between a solar absorber and a radiative cooler in response to temperature change. The switching relies on a phase change material, while the layer structure and optical performance are optimized using machine learning, specifically Bayesian optimization. The system targets a comfortable operating range with mode-dependent spectral behavior, reaching solar absorption above 0.85 and atmospheric-window emissivity above 0.8.","arXiv :2407 .02050v1 [physics .optics] 2 Jul 2024  \nMACHINE-LEARNING DESIGNED SMART COATING:  \nTEMPERATURE-DEPENDENT SELF-ADAPTATION BETWEEN A  \nSOLAR ABSORBER AND A RADIATIVE COOLER  \nZhaocheng Zhanga, Jiahao Xub, Pengran Houa, Yang Dengc  \na School of Mechanical Engineering, Dalian University of Technology  \nb School of Mathematics, Dalian University of Technology  \ncDUT-BSU Joint Institute, Dalian University of Technology  \nNo. 2, Linggong Road, Ganjingzi District, Dalian City, Liaoning Province, China [Dream020501zzc@mail.dlut.edu.cn](Dream020501zzc@mail.dlut.edu.cn)  \nABSTRACT  \nWe designed a multilayered self-adaptive absorber/emitter metamaterial, which can smartly switch between a solar absorber and a radiative cooler based on temperature change. The switching capability is facilitated by the phase change material and the structure is optimized by machine learning. Our design not only advances the machine-learning-based development of metamaterials but also has the potential to significantly reduce carbon emissions and contribute to the goal of achieving carbon neutrality.  \nKeywords : machine learning, radiative cooling, solar heating, self-adaptive, atmospheric window  \n1 Introduction  \nPassive temperature adjustment has gained significant attention in recent years due to its potential to reduce energy consumption [1–5] . Owing to the high energy in the range of solar spectrum and transparency of the atmospheric window in the wavelength range of 8 – 14 µm [6], there has been a significant effort in developing solar absorbers and radiative coolers using different strategies such as photonic structures [7–11], metamaterials [12–14], and energy-saving paints [15–17] . However, most heating/cooling devices designed recently are single-function and non-adjustable, rendering them unsuitable for mid-latitude regions where temperatures fluctuate drastically throughout the year. Therefore, a smart system of passive temperature adjustment is urgently needed.  \nHere, we propose a temperature-dependence passive solar-absorber and radiative-cooler (TDPSR) system, developed through Bayesian optimization. Using the phase change material n-octadecane [18], such a system can switch between solar absorbing and radiative cooling modes within a comfortable temperature range, without any additional energy input for switching. Our structure can achieve an average solar absorption of over 0.85 in solar absorbing mode and average emissivity in the atmospheric window of over 0.8 in the radiative mode. The presented results explore new functionalities of the combination and applications of solar absorbing and radiative cooling, enabling automated regulation and optimization between the two modes, and could potentially lead to significant reduction in energy consumption and enhancement of thermal comfort, applicable to a wide range of uses including vehicles, buildings, textiles, and smart thermal regulations.  \n2 Results and Discussion  \n2.1 Design of TDPSR  \nThe working principle of the TDPSR is presented in Fig. 1B, whereas the expected spectral behaviors of its absorptance and emissivity are shown in Fig. 1A and Fig. 1C. We aim for the TDPSR to facilitate cooling by dissipating heat during hot summers, and to absorb maximum thermal energy from the sun during cold winters to increase indoor temperatures. By using the phase change material n-octadecane with a phase transition temperature at Tp (22.7-31.8°C), slightly above room temperature [19], TDPSR can easily maintain a comfortable surrounding temperature by switching sun absorbing mode and radiative cooling mode.  \nTDPSR with a multilayer structure (Fig. 1D) is designed to meet the spectral requirements for smart dynamic switching between a solar absorber and a radiative cooler. HfO2 and n-octadecane are alternately deposited to manufacture the Epsilon-Near-Zero Metamaterials (ENZM), which are then alternately layered with SiO2 on top of a 200 nm of silver (Ag) layer. Although we explore","cbCaipeTwO7pXqNC","https://ap.wps.com/l/cbCaipeTwO7pXqNC","pdf",3933442,1,14,"English","en",105,"# Introduction\n# Results and Discussion\n## Design of TDPSR","[{\"question\":\"What is the core function of the proposed smart coating system?\",\"answer\":\"It switches between a solar-absorbing mode and a radiative-cooling mode based on temperature change, without additional energy input for switching.\"},{\"question\":\"How does Bayesian optimization contribute to the design?\",\"answer\":\"Bayesian optimization searches for the best combination of structural variables to achieve the target optical properties for dynamic mode switching.\"},{\"question\":\"What phase change material and temperature range enable self-adaptation?\",\"answer\":\"The system uses n-octadecane, whose phase transition temperature Tp is 22.7–31.8°C, slightly above room temperature, allowing comfortable switching between modes.\"}]","MACHINE-LEARNING DESIGNED SMART COATING - TEMPERATURE-DEPENDENT SELF-ADAPTATION BETWEEN A SOLAR ABSORBER AND A RADIATIVE COOLER | 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is the core function of the proposed smart coating system?","Question",{"text":76,"@type":77},"It switches between a solar-absorbing mode and a radiative-cooling mode based on temperature change, without additional energy input for switching.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does Bayesian optimization contribute to the design?",{"text":81,"@type":77},"Bayesian optimization searches for the best combination of structural variables to achieve the target optical properties for dynamic mode switching.",{"name":83,"@type":74,"acceptedAnswer":84},"What phase change material and temperature range enable self-adaptation?",{"text":85,"@type":77},"The system uses n-octadecane, whose phase transition temperature Tp is 22.7–31.8°C, slightly above room temperature, allowing comfortable switching between 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