Controlling radiative heat flows in interior spaces to improve heating and cooling efficiency

A Shish-Kebab Superstructure Film for Personal Radiative Cooling

Due to global warming and the energy crisis, incorporating passive radiative cooling into personal thermal management has attracted extensive attention. However, developing a wearable textile that reflects incoming sunlight and allows mid-infrared radiation transmission is still a tough challenge. Herein, a shish-kebab superstructure film was produced via a flow-induced crystallization strategy for personal radiative cooling. The resulting film endowed a high infrared transmittance (87%) and improved sunlight reflectivity (83%). A device was developed to simulate the human body skin, and the temperatures of the shish-kebab film were 2.5 and 2.6 °C lower than that of traditional textile in outdoor and indoor tests, respectively. In order to make the shish-kebab film more wearable, a series of modifications were then carried out. This study demonstrates the substantial potential to personal thermal management textiles.

Artificial neural network-based smart aerogel glazing in low-energy buildings: A state-of-the-art review

Aerogel materials with super-insulating, visual-penetrable, and sound-proof properties are promising in buildings, whereas the coupling effect of various parameters in complex porous aerogels proposes challenges for thermal/visual performance prediction. Traditional physics-based models face challenges such as modeling complexity, heavy computational load, and inadaptability for long-term validation (owing to boundary condition change, degradation of thermophysical properties, and so on). In this study, a holistic review is conducted on aerogel production, components prefabrication, modeling development, single-, and multi-objective optimizations. Methodologies to quantify parameter uncertainties are reviewed, including interface energy balance, Rosseland approximation and Monte Carlo method. Novel aerogel integrated glazing systems with synergistic functions are demonstrated. Originalities include an innovative modeling approach, enhanced computational efficiency, and user-friendly interface for non-professionals or multidisciplinary research. In addition, human knowledge-based machine learning can reduce abundant data requirement, increase performance prediction reliability, and improve model interpretability, so as to promote advanced aerogel materials in smart and energy-efficient buildings.