Fig. 3: Evaluation of the thermal management of the D-type intelligent window combined with the actual scene.
From: Isotope-driven hydrogel smart windows for self-adaptive thermoregulation
a Schematic of buildings in an actual scene. b Infrared thermal images of the architecture at different angles from 18:00–18:15 on July 9th, 2024, in Shanghai, China. c) LWIR spectral characteristics of architectural glass before and after modification. d Schematic and photograph of outdoor temperature-measuring devices. e Tracking the outdoor cooling temperature of the air and samples (control group: HPN smart window, test group: DPN/PVA smart window and PVDF film) on September 8th, 2023. f Tracking the outdoor heating temperature of the air and samples (control group: HPN smart window and test group: DPN/PVA smart window) on November 27th, 2023. The thickness of the hydrogel layer in both e) and f) is 3.4 mm. g, h represent calculated net radiative cooling (0 C: 0 W/m2/K + 9.72%Psun; 3 C: 3 W/m2/K + 9.72%Psun; 6 C: 6 W/m2/K + 9.72%Psun; 9 C: 9 W/m2/K + 9.72%Psun; 12 C: 12 W/m2/K + 9.72%Psun) and heating (0H: 0 W/m2/K-7.51%Psun; 3H: 3 W/m2/K-7.51%Psun; 6H: 6 W/m2/K-7.51%Psun; 9H: 9 W/m2/K-7.51%Psun; 12H: 12 W/m2/K-7.51%Psun) power of DPN/PVA smart windows. i Maps of 10 urban areas of different latitudes and longitudes on a world map and the annual energy savings compared with those of ordinary architectural glass. j Energy consumption and cooling and heating energy savings of the DPN/PVA smart window per year corresponding to each city (AEC: annual energy consumption; ACES: annual cooling energy savings; AHES: annual heating energy savings. All units are kWh·104). k Carbon dioxide (CO2) emission reduction (%) in 10 urban areas compared with that of ordinary architectural glass.
