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Single-pane windows still account for a large percentage of US building energy consumption. In this paper, we introduced a new solution incorporating the photothermal effect of metallic nanoparticles(Fe3O4@Cu2−xS) into glazing structures to utilize solar infrared and then enhance the window’s thermal performance in winter. Such spectrally selective characteristics of the designed photothermal films were obtained from lab measurements and then integrated into a thermodynamic analytical model. Subsequently, we examined the thermal and optical behaviors of the photothermal single-pane window and compared its overall energy performance with the conventional low-e coated single-pane window, in which typical window properties, dimensions, winter boundary conditions, and solar irradiance were adopted. The numerical analysis results demonstrated that the photothermal window systems could yield 20.4% energy savings relative to the conventional low-e coated windows. This research paves an underlying thermodynamic mechanism for understanding such a nanoscale phenomenon at the architectural scale. From the implementation perspective, the designed photothermal film can be added into the existing single-pane windows for energy-efficient retrofitting purposes. 

Solar radiation is a key factor influencing sustainable building engineering, in terms of both optical and thermal properties of building envelopes. Solar irradiance data in a conventional weather data file are broadband, representing the total of ultraviolet (UV), visible light (VIS), and near-infrared radiation (NIR), three components of the solar spectrum; however, these three components play different roles in sustainable building design and engineering. For instance, solar VIS always provides benefits to indoor building energy savings (e.g., electrical lighting), while solar NIR is beneficial to building energy savings in winter but undesirable in summer. As a consequence, there is a need for reliable separate analyses focusing on individual solar radiation components. In this work, we explore and test classification-based modeling methods for decomposing hourly broadband global horizontal solar irradiance data in conventional weather files into hourly global horizontal solar NIR components. This model can then be conveniently implemented for sustainable building design and engineering purposes.